WorldWideScience

Sample records for predicting regional control

  1. Networked Predictive Control for Nonlinear Systems With Arbitrary Region Quantizers.

    Science.gov (United States)

    Yang, Hongjiu; Xu, Yang; Xia, Yuanqing; Zhang, Jinhui

    2017-04-06

    In this paper, networked predictive control is investigated for planar nonlinear systems with quantization by an extended state observer (ESO). The ESO is used not only to deal with nonlinear terms but also to generate predictive states for dealing with network-induced delays. Two arbitrary region quantizers are applied to take effective values of signals in forward channel and feedback channel, respectively. Based on a "zoom" strategy, sufficient conditions are given to guarantee stabilization of the closed-loop networked control system with quantization. A simulation example is proposed to exhibit advantages and availability of the results.

  2. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

    Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

    2008-01-01

    In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optimal...... steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...

  3. Predicting human age using regional morphometry and inter-regional morphological similarity

    Science.gov (United States)

    Wang, Xun-Heng; Li, Lihua

    2016-03-01

    The goal of this study is predicting human age using neuro-metrics derived from structural MRI, as well as investigating the relationships between age and predictive neuro-metrics. To this end, a cohort of healthy subjects were recruited from 1000 Functional Connectomes Project. The ages of the participations were ranging from 7 to 83 (36.17+/-20.46). The structural MRI for each subject was preprocessed using FreeSurfer, resulting in regional cortical thickness, mean curvature, regional volume and regional surface area for 148 anatomical parcellations. The individual age was predicted from the combination of regional and inter-regional neuro-metrics. The prediction accuracy is r = 0.835, p Pearson correlation coefficient between predicted ages and actual ages. Moreover, the LASSO linear regression also found certain predictive features, most of which were inter-regional features. The turning-point of the developmental trajectories in human brain was around 40 years old based on regional cortical thickness. In conclusion, structural MRI could be potential biomarkers for the aging in human brain. The human age could be successfully predicted from the combination of regional morphometry and inter-regional morphological similarity. The inter-regional measures could be beneficial to investigating human brain connectome.

  4. Generalized Predictive Control and Neural Generalized Predictive Control

    Directory of Open Access Journals (Sweden)

    Sadhana CHIDRAWAR

    2008-12-01

    Full Text Available As Model Predictive Control (MPC relies on the predictive Control using a multilayer feed forward network as the plants linear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. This paper presents a detailed derivation of the Generalized Predictive Control and Neural Generalized Predictive Control with Newton-Raphson as minimization algorithm. Taking three separate systems, performances of the system has been tested. Simulation results show the effect of neural network on Generalized Predictive Control. The performance comparison of this three system configurations has been given in terms of ISE and IAE.

  5. Development of a regional ensemble prediction method for probabilistic weather prediction

    International Nuclear Information System (INIS)

    Nohara, Daisuke; Tamura, Hidetoshi; Hirakuchi, Hiromaru

    2015-01-01

    A regional ensemble prediction method has been developed to provide probabilistic weather prediction using a numerical weather prediction model. To obtain consistent perturbations with the synoptic weather pattern, both of initial and lateral boundary perturbations were given by differences between control and ensemble member of the Japan Meteorological Agency (JMA)'s operational one-week ensemble forecast. The method provides a multiple ensemble member with a horizontal resolution of 15 km for 48-hour based on a downscaling of the JMA's operational global forecast accompanied with the perturbations. The ensemble prediction was examined in the case of heavy snow fall event in Kanto area on January 14, 2013. The results showed that the predictions represent different features of high-resolution spatiotemporal distribution of precipitation affected by intensity and location of extra-tropical cyclone in each ensemble member. Although the ensemble prediction has model bias of mean values and variances in some variables such as wind speed and solar radiation, the ensemble prediction has a potential to append a probabilistic information to a deterministic prediction. (author)

  6. Model Predictive Vibration Control Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures

    CERN Document Server

    Takács, Gergely

    2012-01-01

    Real-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry. If the control of lightly damped mechanical structures is assumed, the region of attraction containing the set of allowable initial conditions requires a large prediction horizon, making the already computationally demanding on-line process even more complex. Model Predictive Vibration Control provides insight into the predictive control of lightly damped vibrating structures by exploring computationally efficient algorithms which are capable of low frequency vibration control with guaranteed stability and constraint feasibility. In addition to a theoretical primer on active vibration damping and model predictive control, Model Predictive Vibration Control provides a guide through the necessary steps in understanding the founding ideas of predictive control applied in AVC such as: ·         the implementation of ...

  7. Ensemble-based Regional Climate Prediction: Political Impacts

    Science.gov (United States)

    Miguel, E.; Dykema, J.; Satyanath, S.; Anderson, J. G.

    2008-12-01

    Accurate forecasts of regional climate, including temperature and precipitation, have significant implications for human activities, not just economically but socially. Sub Saharan Africa is a region that has displayed an exceptional propensity for devastating civil wars. Recent research in political economy has revealed a strong statistical relationship between year to year fluctuations in precipitation and civil conflict in this region in the 1980s and 1990s. To investigate how climate change may modify the regional risk of civil conflict in the future requires a probabilistic regional forecast that explicitly accounts for the community's uncertainty in the evolution of rainfall under anthropogenic forcing. We approach the regional climate prediction aspect of this question through the application of a recently demonstrated method called generalized scalar prediction (Leroy et al. 2009), which predicts arbitrary scalar quantities of the climate system. This prediction method can predict change in any variable or linear combination of variables of the climate system averaged over a wide range spatial scales, from regional to hemispheric to global. Generalized scalar prediction utilizes an ensemble of model predictions to represent the community's uncertainty range in climate modeling in combination with a timeseries of any type of observational data that exhibits sensitivity to the scalar of interest. It is not necessary to prioritize models in deriving with the final prediction. We present the results of the application of generalized scalar prediction for regional forecasts of temperature and precipitation and Sub Saharan Africa. We utilize the climate predictions along with the established statistical relationship between year-to-year rainfall variability in Sub Saharan Africa to investigate the potential impact of climate change on civil conflict within that region.

  8. Applied predictive control

    CERN Document Server

    Sunan, Huang; Heng, Lee Tong

    2002-01-01

    The presence of considerable time delays in the dynamics of many industrial processes, leading to difficult problems in the associated closed-loop control systems, is a well-recognized phenomenon. The performance achievable in conventional feedback control systems can be significantly degraded if an industrial process has a relatively large time delay compared with the dominant time constant. Under these circumstances, advanced predictive control is necessary to improve the performance of the control system significantly. The book is a focused treatment of the subject matter, including the fundamentals and some state-of-the-art developments in the field of predictive control. Three main schemes for advanced predictive control are addressed in this book: • Smith Predictive Control; • Generalised Predictive Control; • a form of predictive control based on Finite Spectrum Assignment. A substantial part of the book addresses application issues in predictive control, providing several interesting case studie...

  9. Ellipsoidal prediction regions for multivariate uncertainty characterization

    DEFF Research Database (Denmark)

    Golestaneh, Faranak; Pinson, Pierre; Azizipanah-Abarghooee, Rasoul

    2018-01-01

    , for classes of decision-making problems based on robust, interval chance-constrained optimization, necessary inputs take the form of multivariate prediction regions rather than scenarios. The current literature is at very primitive stage of characterizing multivariate prediction regions to be employed...... in these classes of optimization problems. To address this issue, we introduce a new class of multivariate forecasts which form as multivariate ellipsoids for non-Gaussian variables. We propose a data-driven systematic framework to readily generate and evaluate ellipsoidal prediction regions, with predefined...... probability guarantees and minimum conservativeness. A skill score is proposed for quantitative assessment of the quality of prediction ellipsoids. A set of experiments is used to illustrate the discrimination ability of the proposed scoring rule for potential misspecification of ellipsoidal prediction regions...

  10. Body region dissatisfaction predicts attention to body regions on other women.

    Science.gov (United States)

    Lykins, Amy D; Ferris, Tamara; Graham, Cynthia A

    2014-09-01

    The proliferation of "idealized" (i.e., very thin and attractive) women in the media has contributed to increasing rates of body dissatisfaction among women. However, it remains relatively unknown how women attend to these images: does dissatisfaction predict greater or lesser attention to these body regions on others? Fifty healthy women (mean age=21.8 years) viewed images of idealized and plus-size models; an eye-tracker recorded visual attention. Participants also completed measures of satisfaction for specific body regions, which were then used as predictors of visual attention to these regions on models. Consistent with an avoidance-type process, lower levels of satisfaction with the two regions of greatest reported concern (mid, lower torso) predicted less attention to these regions; greater satisfaction predicted more attention to these regions. While this visual attention bias may aid in preserving self-esteem when viewing idealized others, it may preclude the opportunity for comparisons that could improve self-esteem. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. The UKC2 regional coupled environmental prediction system

    Science.gov (United States)

    Lewis, Huw W.; Castillo Sanchez, Juan Manuel; Graham, Jennifer; Saulter, Andrew; Bornemann, Jorge; Arnold, Alex; Fallmann, Joachim; Harris, Chris; Pearson, David; Ramsdale, Steven; Martínez-de la Torre, Alberto; Bricheno, Lucy; Blyth, Eleanor; Bell, Victoria A.; Davies, Helen; Marthews, Toby R.; O'Neill, Clare; Rumbold, Heather; O'Dea, Enda; Brereton, Ashley; Guihou, Karen; Hines, Adrian; Butenschon, Momme; Dadson, Simon J.; Palmer, Tamzin; Holt, Jason; Reynard, Nick; Best, Martin; Edwards, John; Siddorn, John

    2018-01-01

    It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, require a more integrated Earth System approach to forecasting. This hypothesis can be explored using regional coupled prediction systems, in which the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land can be simulated. Such systems are becoming increasingly common research tools. This paper describes the development of the UKC2 regional coupled research system, which has been delivered under the UK Environmental Prediction Prototype project. This provides the first implementation of an atmosphere-land-ocean-wave modelling system focussed on the United Kingdom and surrounding seas at km-scale resolution. The UKC2 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean waves (WAVEWATCH III). These components are coupled, via OASIS3-MCT libraries, at unprecedentedly high resolution across the UK within a north-western European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a new research tool for UK environmental science. This paper documents the technical design and implementation of UKC2, along with the associated evaluation framework. An analysis of new results comparing the output of the coupled UKC2 system with relevant forced control simulations for six contrasting case studies of 5-day duration is presented. Results demonstrate that performance can be achieved with the UKC2 system that is at least comparable to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and mean wave period highlight the potential

  12. The UKC2 regional coupled environmental prediction system

    Directory of Open Access Journals (Sweden)

    H. W. Lewis

    2018-01-01

    Full Text Available It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, require a more integrated Earth System approach to forecasting. This hypothesis can be explored using regional coupled prediction systems, in which the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land can be simulated. Such systems are becoming increasingly common research tools. This paper describes the development of the UKC2 regional coupled research system, which has been delivered under the UK Environmental Prediction Prototype project. This provides the first implementation of an atmosphere–land–ocean–wave modelling system focussed on the United Kingdom and surrounding seas at km-scale resolution. The UKC2 coupled system incorporates models of the atmosphere (Met Office Unified Model, land surface with river routing (JULES, shelf-sea ocean (NEMO and ocean waves (WAVEWATCH III. These components are coupled, via OASIS3-MCT libraries, at unprecedentedly high resolution across the UK within a north-western European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a new research tool for UK environmental science. This paper documents the technical design and implementation of UKC2, along with the associated evaluation framework. An analysis of new results comparing the output of the coupled UKC2 system with relevant forced control simulations for six contrasting case studies of 5-day duration is presented. Results demonstrate that performance can be achieved with the UKC2 system that is at least comparable to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and mean wave period

  13. Application of model predictive control for optimal operation of wind turbines

    Science.gov (United States)

    Yuan, Yuan; Cao, Pei; Tang, J.

    2017-04-01

    For large-scale wind turbines, reducing maintenance cost is a major challenge. Model predictive control (MPC) is a promising approach to deal with multiple conflicting objectives using the weighed sum approach. In this research, model predictive control method is applied to wind turbine to find an optimal balance between multiple objectives, such as the energy capture, loads on turbine components, and the pitch actuator usage. The actuator constraints are integrated into the objective function at the control design stage. The analysis is carried out in both the partial load region and full load region, and the performances are compared with those of a baseline gain scheduling PID controller. The application of this strategy achieves enhanced balance of component loads, the average power and actuator usages in partial load region.

  14. Data-Based Predictive Control with Multirate Prediction Step

    Science.gov (United States)

    Barlow, Jonathan S.

    2010-01-01

    Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.

  15. Model Predictive Control of Mineral Column Flotation Process

    Directory of Open Access Journals (Sweden)

    Yahui Tian

    2018-06-01

    Full Text Available Column flotation is an efficient method commonly used in the mineral industry to separate useful minerals from ores of low grade and complex mineral composition. Its main purpose is to achieve maximum recovery while ensuring desired product grade. This work addresses a model predictive control design for a mineral column flotation process modeled by a set of nonlinear coupled heterodirectional hyperbolic partial differential equations (PDEs and ordinary differential equations (ODEs, which accounts for the interconnection of well-stirred regions represented by continuous stirred tank reactors (CSTRs and transport systems given by heterodirectional hyperbolic PDEs, with these two regions combined through the PDEs’ boundaries. The model predictive control considers both optimality of the process operations and naturally present input and state/output constraints. For the discrete controller design, spatially varying steady-state profiles are obtained by linearizing the coupled ODE–PDE model, and then the discrete system is obtained by using the Cayley–Tustin time discretization transformation without any spatial discretization and/or without model reduction. The model predictive controller is designed by solving an optimization problem with input and state/output constraints as well as input disturbance to minimize the objective function, which leads to an online-solvable finite constrained quadratic regulator problem. Finally, the controller performance to keep the output at the steady state within the constraint range is demonstrated by simulation studies, and it is concluded that the optimal control scheme presented in this work makes this flotation process more efficient.

  16. Using Machine Learning to Predict Swine Movements within a Regional Program to Improve Control of Infectious Diseases in the US.

    Science.gov (United States)

    Valdes-Donoso, Pablo; VanderWaal, Kimberly; Jarvis, Lovell S; Wayne, Spencer R; Perez, Andres M

    2017-01-01

    Between-farm animal movement is one of the most important factors influencing the spread of infectious diseases in food animals, including in the US swine industry. Understanding the structural network of contacts in a food animal industry is prerequisite to planning for efficient production strategies and for effective disease control measures. Unfortunately, data regarding between-farm animal movements in the US are not systematically collected and thus, such information is often unavailable. In this paper, we develop a procedure to replicate the structure of a network, making use of partial data available, and subsequently use the model developed to predict animal movements among sites in 34 Minnesota counties. First, we summarized two networks of swine producing facilities in Minnesota, then we used a machine learning technique referred to as random forest, an ensemble of independent classification trees, to estimate the probability of pig movements between farms and/or markets sites located in two counties in Minnesota. The model was calibrated and tested by comparing predicted data and observed data in those two counties for which data were available. Finally, the model was used to predict animal movements in sites located across 34 Minnesota counties. Variables that were important in predicting pig movements included between-site distance, ownership, and production type of the sending and receiving farms and/or markets. Using a weighted-kernel approach to describe spatial variation in the centrality measures of the predicted network, we showed that the south-central region of the study area exhibited high aggregation of predicted pig movements. Our results show an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome, which is believed to be transmitted, at least in part, though animal movements. While the correspondence of movements and disease is not a causal test, it suggests that the predicted network may approximate

  17. The regional costs and benefits of acid rain control

    International Nuclear Information System (INIS)

    Berkman, M.P.

    1991-01-01

    Congress recently enacted acid rain control legislation as part of the 1990 Clean Air Act Amendments following a decade-long debate among disparate regional interests. Although Congress succeeded in drafting a law acceptable to all regions, the regional costs and benefits of the legislation remain uncertain. The research presented here attempts to estimate the regional costs and benefits and the economic impacts of acid rain controls. These estimates are made using a modeling system composed of econometric, linear programming and input-output models. The econometric and linear programming components describe markets for electricity and coal. The outputs of these components including capital investment, electricity demand, and coal production are taken as exogenous inputs by a multiregional input-output model. The input-output model produces estimates of changes in final demand, gross output, and employment. The utility linear programming model also predicts sulfur dioxide emissions (an acid-rain precursor). According to model simulations, the costs of acid rain control exceed the benefits for many regions including several regions customarily thought to be the major beneficiaries of acid rain control such as New England

  18. Lake nutrient stoichiometry is less predictable than nutrient concentrations at regional and sub-continental scales.

    Science.gov (United States)

    Collins, Sarah M; Oliver, Samantha K; Lapierre, Jean-Francois; Stanley, Emily H; Jones, John R; Wagner, Tyler; Soranno, Patricia A

    2017-07-01

    Production in many ecosystems is co-limited by multiple elements. While a known suite of drivers associated with nutrient sources, nutrient transport, and internal processing controls concentrations of phosphorus (P) and nitrogen (N) in lakes, much less is known about whether the drivers of single nutrient concentrations can also explain spatial or temporal variation in lake N:P stoichiometry. Predicting stoichiometry might be more complex than predicting concentrations of individual elements because some drivers have similar relationships with N and P, leading to a weak relationship with their ratio. Further, the dominant controls on elemental concentrations likely vary across regions, resulting in context dependent relationships between drivers, lake nutrients and their ratios. Here, we examine whether known drivers of N and P concentrations can explain variation in N:P stoichiometry, and whether explaining variation in stoichiometry differs across regions. We examined drivers of N:P in ~2,700 lakes at a sub-continental scale and two large regions nested within the sub-continental study area that have contrasting ecological context, including differences in the dominant type of land cover (agriculture vs. forest). At the sub-continental scale, lake nutrient concentrations were correlated with nutrient loading and lake internal processing, but stoichiometry was only weakly correlated to drivers of lake nutrients. At the regional scale, drivers that explained variation in nutrients and stoichiometry differed between regions. In the Midwestern U.S. region, dominated by agricultural land use, lake depth and the percentage of row crop agriculture were strong predictors of stoichiometry because only phosphorus was related to lake depth and only nitrogen was related to the percentage of row crop agriculture. In contrast, all drivers were related to N and P in similar ways in the Northeastern U.S. region, leading to weak relationships between drivers and stoichiometry

  19. Stabilizing model predictive control : on the enlargement of the terminal set

    NARCIS (Netherlands)

    Brunner, F.D.; Lazar, M.; Allgöwer, F.

    2015-01-01

    It is well known that a large terminal set leads to a large region where the model predictive control problem is feasible without the need for a long prediction horizon. This paper proposes a new method for the enlargement of the terminal set. Different from existing approaches, the method uses the

  20. Robust Model Predictive Control of a Wind Turbine

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2012-01-01

    In this work the problem of robust model predictive control (robust MPC) of a wind turbine in the full load region is considered. A minimax robust MPC approach is used to tackle the problem. Nonlinear dynamics of the wind turbine are derived by combining blade element momentum (BEM) theory...... of the uncertain system is employed and a norm-bounded uncertainty model is used to formulate a minimax model predictive control. The resulting optimization problem is simplified by semidefinite relaxation and the controller obtained is applied on a full complexity, high fidelity wind turbine model. Finally...... and first principle modeling of the turbine flexible structure. Thereafter the nonlinear model is linearized using Taylor series expansion around system operating points. Operating points are determined by effective wind speed and an extended Kalman filter (EKF) is employed to estimate this. In addition...

  1. Delta-Domain Predictive Control and Identification for Control

    DEFF Research Database (Denmark)

    Lauritsen, Morten Bach

    1997-01-01

    The present thesis is concerned with different aspects of modelling, control and identification of linear systems. Traditionally, discrete-time sampled-data systems are represented using shift-operator parametrizations. Such parametrizations are not suitable at fast sampling rates. An alternative...... minimum-variance predictor as a special case and to have a well-defined continuous-time limit. By means of this new prediction method a unified framework for discrete-time and continuous-time predictive control algorithms is developed. This contains a continuous-time like discrete-time predictive...... controller which is insensitive to the choice of sampling period and has a well-defined limit in the continuous-time case. Also more conventional discrete-time predictive control methods may be described within the unified approach. The predictive control algorithms are extended to frequency weighted...

  2. Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

    Science.gov (United States)

    Li, Yifeng; Shi, Wenqiang; Wasserman, Wyeth W

    2018-05-31

    In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.

  3. Predicting Surface Runoff from Catchment to Large Region

    Directory of Open Access Journals (Sweden)

    Hongxia Li

    2015-01-01

    Full Text Available Predicting surface runoff from catchment to large region is a fundamental and challenging task in hydrology. This paper presents a comprehensive review for various studies conducted for improving runoff predictions from catchment to large region in the last several decades. This review summarizes the well-established methods and discusses some promising approaches from the following four research fields: (1 modeling catchment, regional and global runoff using lumped conceptual rainfall-runoff models, distributed hydrological models, and land surface models, (2 parameterizing hydrological models in ungauged catchments, (3 improving hydrological model structure, and (4 using new remote sensing precipitation data.

  4. Predictive control for stochastic systems based on multi-layer probabilistic sets

    Directory of Open Access Journals (Sweden)

    Huaqing LIANG

    2016-04-01

    Full Text Available Aiming at a class of discrete-time stochastic systems with Markov jump features, the state-feedback predictive control problem under probabilistic constraints of input variables is researched. On the basis of the concept and method of the multi-layer probabilistic sets, the predictive controller design algorithm with the soft constraints of different probabilities is presented. Under the control of the multi-step feedback laws, the system state moves to different ellipses with specified probabilities. The stability of the system is guaranteed, the feasible region of the control problem is enlarged, and the system performance is improved. Finally, a simulation example is given to prove the effectiveness of the proposed method.

  5. Initializing decadal climate predictions over the North Atlantic region

    Science.gov (United States)

    Matei, Daniela Mihaela; Pohlmann, Holger; Jungclaus, Johann; Müller, Wolfgang; Haak, Helmuth; Marotzke, Jochem

    2010-05-01

    Decadal climate prediction aims to predict the internally-generated decadal climate variability in addition to externally-forced climate change signal. In order to achieve this it is necessary to start the predictions from the current climate state. In this study we investigate the forecast skill of the North Atlantic decadal climate predictions using two different ocean initialization strategies. First we apply an assimilation of ocean synthesis data provided by the GECCO project (Köhl and Stammer, 2008) as initial conditions for the coupled model ECHAM5/MPI-OM. Hindcast experiments are then performed over the period 1952-2001. An alternative approach is one in which the subsurface ocean temperature and salinity are diagnosed from an ensemble of ocean model runs forced by the NCEP-NCAR atmospheric reanalyzes for the period 1948-2007, then nudge into the coupled model to produce initial conditions for the hindcast experiments. An anomaly coupling scheme is used in both approaches to avoid the hindcast drift and the associated initial shock. Differences between the two assimilation approaches are discussed by comparing them with the observational data in key regions and processes. We asses the skill of the initialized decadal hindcast experiments against the prediction skill of the non-initialized hindcasts simulation. We obtain an overview of the regions with the highest predictability from the regional distribution of the anomaly correlation coefficients and RMSE for the SAT. For the first year the hindcast skill is increased over almost all ocean regions in the NCEP-forced approach. This increase in the hindcast skill for the 1 year lead time is somewhat reduced in the GECCO approach. At lead time 5yr and 10yr, the skill enhancement is still found over the North Atlantic and North Pacific regions. We also consider the potential predictability of the Atlantic Meridional Overturning Circulation (AMOC) and Nordic Seas Overflow by comparing the predicted values to

  6. Adaptive filtering prediction and control

    CERN Document Server

    Goodwin, Graham C

    2009-01-01

    Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o

  7. Using MODIS Data to Predict Regional Corn Yields

    Directory of Open Access Journals (Sweden)

    Ho-Young Ban

    2016-12-01

    Full Text Available A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data, which are eight-day interval surface reflectance data, were obtained from day of the year (DOY 89 to 337 to calculate the leaf area index (LAI. The sum of the LAI from early in the season to a given date in the season (end of DOY (EOD was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD. A simple phenology model was derived to estimate the dates of emergence and maturity using the LAD-logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF were established using LAD as the variable to predict corn yield. The yield model YP used LAD from predicted emergence to maturity, and the yield model YF used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model, including the predicted phenology, performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed similar or better performance, even for the much earlier pre-harvest yield prediction at DOY 257. In addition, the model performance showed no difference between the two study regions with very different climates and cultivation methods, including cultivar and irrigation management. These results suggested that the approach described in this paper has potential for application to

  8. Controlling factors of uranium mineralization and prospect prediction in Qimantage area

    International Nuclear Information System (INIS)

    Yao Chunling; Zhu Pengfei; Cai Yuqi; Zhang Wenming; Zhao Yong'an; Song Jiye; Zhang Xiaojin

    2011-01-01

    Based on the analysis of regional geology in Qimantage area, the condition for uranium mineralization is summarized in regional geology setting, volcanic, granite and faults. This study shows that this area has favorable prospect for uranium mineralization. The metallogenic model is built up according to the controlling factors over uranium mineralization. Under this model, six potential areas are predicted in MRAS software with mineralization factors of synthetically geological information method. (authors)

  9. Model predictive control for a thermostatic controlled system

    DEFF Research Database (Denmark)

    Shafiei, Seyed Ehsan; Rasmussen, Henrik; Stoustrup, Jakob

    2013-01-01

    This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff temperat......This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff...

  10. Prediction of monthly regional groundwater levels through hybrid soft-computing techniques

    Science.gov (United States)

    Chang, Fi-John; Chang, Li-Chiu; Huang, Chien-Wei; Kao, I.-Feng

    2016-10-01

    Groundwater systems are intrinsically heterogeneous with dynamic temporal-spatial patterns, which cause great difficulty in quantifying their complex processes, while reliable predictions of regional groundwater levels are commonly needed for managing water resources to ensure proper service of water demands within a region. In this study, we proposed a novel and flexible soft-computing technique that could effectively extract the complex high-dimensional input-output patterns of basin-wide groundwater-aquifer systems in an adaptive manner. The soft-computing models combined the Self Organized Map (SOM) and the Nonlinear Autoregressive with Exogenous Inputs (NARX) network for predicting monthly regional groundwater levels based on hydrologic forcing data. The SOM could effectively classify the temporal-spatial patterns of regional groundwater levels, the NARX could accurately predict the mean of regional groundwater levels for adjusting the selected SOM, the Kriging was used to interpolate the predictions of the adjusted SOM into finer grids of locations, and consequently the prediction of a monthly regional groundwater level map could be obtained. The Zhuoshui River basin in Taiwan was the study case, and its monthly data sets collected from 203 groundwater stations, 32 rainfall stations and 6 flow stations during 2000 and 2013 were used for modelling purpose. The results demonstrated that the hybrid SOM-NARX model could reliably and suitably predict monthly basin-wide groundwater levels with high correlations (R2 > 0.9 in both training and testing cases). The proposed methodology presents a milestone in modelling regional environmental issues and offers an insightful and promising way to predict monthly basin-wide groundwater levels, which is beneficial to authorities for sustainable water resources management.

  11. Deep learning for predicting the monsoon over the homogeneous regions of India

    Science.gov (United States)

    Saha, Moumita; Mitra, Pabitra; Nanjundiah, Ravi S.

    2017-06-01

    Indian monsoon varies in its nature over the geographical regions. Predicting the rainfall not just at the national level, but at the regional level is an important task. In this article, we used a deep neural network, namely, the stacked autoencoder to automatically identify climatic factors that are capable of predicting the rainfall over the homogeneous regions of India. An ensemble regression tree model is used for monsoon prediction using the identified climatic predictors. The proposed model provides forecast of the monsoon at a long lead time which supports the government to implement appropriate policies for the economic growth of the country. The monsoon of the central, north-east, north-west, and south-peninsular India regions are predicted with errors of 4.1%, 5.1%, 5.5%, and 6.4%, respectively. The identified predictors show high skill in predicting the regional monsoon having high variability. The proposed model is observed to be competitive with the state-of-the-art prediction models.

  12. Model predictive Controller for Mobile Robot

    OpenAIRE

    Alireza Rezaee

    2017-01-01

    This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is...

  13. Quantitative Earthquake Prediction on Global and Regional Scales

    International Nuclear Information System (INIS)

    Kossobokov, Vladimir G.

    2006-01-01

    The Earth is a hierarchy of volumes of different size. Driven by planetary convection these volumes are involved into joint and relative movement. The movement is controlled by a wide variety of processes on and around the fractal mesh of boundary zones, and does produce earthquakes. This hierarchy of movable volumes composes a large non-linear dynamical system. Prediction of such a system in a sense of extrapolation of trajectory into the future is futile. However, upon coarse-graining the integral empirical regularities emerge opening possibilities of prediction in a sense of the commonly accepted consensus definition worked out in 1976 by the US National Research Council. Implications of the understanding hierarchical nature of lithosphere and its dynamics based on systematic monitoring and evidence of its unified space-energy similarity at different scales help avoiding basic errors in earthquake prediction claims. They suggest rules and recipes of adequate earthquake prediction classification, comparison and optimization. The approach has already led to the design of reproducible intermediate-term middle-range earthquake prediction technique. Its real-time testing aimed at prediction of the largest earthquakes worldwide has proved beyond any reasonable doubt the effectiveness of practical earthquake forecasting. In the first approximation, the accuracy is about 1-5 years and 5-10 times the anticipated source dimension. Further analysis allows reducing spatial uncertainty down to 1-3 source dimensions, although at a cost of additional failures-to-predict. Despite of limited accuracy a considerable damage could be prevented by timely knowledgeable use of the existing predictions and earthquake prediction strategies. The December 26, 2004 Indian Ocean Disaster seems to be the first indication that the methodology, designed for prediction of M8.0+ earthquakes can be rescaled for prediction of both smaller magnitude earthquakes (e.g., down to M5.5+ in Italy) and

  14. Quantitative Earthquake Prediction on Global and Regional Scales

    Science.gov (United States)

    Kossobokov, Vladimir G.

    2006-03-01

    The Earth is a hierarchy of volumes of different size. Driven by planetary convection these volumes are involved into joint and relative movement. The movement is controlled by a wide variety of processes on and around the fractal mesh of boundary zones, and does produce earthquakes. This hierarchy of movable volumes composes a large non-linear dynamical system. Prediction of such a system in a sense of extrapolation of trajectory into the future is futile. However, upon coarse-graining the integral empirical regularities emerge opening possibilities of prediction in a sense of the commonly accepted consensus definition worked out in 1976 by the US National Research Council. Implications of the understanding hierarchical nature of lithosphere and its dynamics based on systematic monitoring and evidence of its unified space-energy similarity at different scales help avoiding basic errors in earthquake prediction claims. They suggest rules and recipes of adequate earthquake prediction classification, comparison and optimization. The approach has already led to the design of reproducible intermediate-term middle-range earthquake prediction technique. Its real-time testing aimed at prediction of the largest earthquakes worldwide has proved beyond any reasonable doubt the effectiveness of practical earthquake forecasting. In the first approximation, the accuracy is about 1-5 years and 5-10 times the anticipated source dimension. Further analysis allows reducing spatial uncertainty down to 1-3 source dimensions, although at a cost of additional failures-to-predict. Despite of limited accuracy a considerable damage could be prevented by timely knowledgeable use of the existing predictions and earthquake prediction strategies. The December 26, 2004 Indian Ocean Disaster seems to be the first indication that the methodology, designed for prediction of M8.0+ earthquakes can be rescaled for prediction of both smaller magnitude earthquakes (e.g., down to M5.5+ in Italy) and

  15. Model predictive control using fuzzy decision functions

    NARCIS (Netherlands)

    Kaymak, U.; Costa Sousa, da J.M.

    2001-01-01

    Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the

  16. Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

    NARCIS (Netherlands)

    Tian, X.; Negenborn, R.R.; Van Overloop, P.J.A.T.M.; Mostert, E.

    2014-01-01

    In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for

  17. Dynamic Algorithm for LQGPC Predictive Control

    DEFF Research Database (Denmark)

    Hangstrup, M.; Ordys, A.W.; Grimble, M.J.

    1998-01-01

    In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated into the control......In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated...... into the controller design and the solution is derived using the method of Lagrange multipliers. It is shown how well-known GPC controller can be obtained as a special case of the LQGPC controller design. The important advantage of using the LQGPC framework for designing predictive, e.g. GPS is that LQGPC enables...

  18. Regional differences in brain volume predict the acquisition of skill in a complex real-time strategy videogame.

    Science.gov (United States)

    Basak, Chandramallika; Voss, Michelle W; Erickson, Kirk I; Boot, Walter R; Kramer, Arthur F

    2011-08-01

    Previous studies have found that differences in brain volume among older adults predict performance in laboratory tasks of executive control, memory, and motor learning. In the present study we asked whether regional differences in brain volume as assessed by the application of a voxel-based morphometry technique on high resolution MRI would also be useful in predicting the acquisition of skill in complex tasks, such as strategy-based video games. Twenty older adults were trained for over 20 h to play Rise of Nations, a complex real-time strategy game. These adults showed substantial improvements over the training period in game performance. MRI scans obtained prior to training revealed that the volume of a number of brain regions, which have been previously associated with subsets of the trained skills, predicted a substantial amount of variance in learning on the complex game. Thus, regional differences in brain volume can predict learning in complex tasks that entail the use of a variety of perceptual, cognitive and motor processes. Copyright © 2011 Elsevier Inc. All rights reserved.

  19. Assessment of protein disorder region predictions in CASP10

    KAUST Repository

    Monastyrskyy, Bohdan; Kryshtafovych, Andriy; Moult, John; Tramontano, Anna; Fidelis, Krzysztof

    2013-01-01

    The article presents the assessment of disorder region predictions submitted to CASP10. The evaluation is based on the three measures tested in previous CASPs: (i) balanced accuracy, (ii) the Matthews correlation coefficient for the binary predictions, and (iii) the area under the curve in the receiver operating characteristic (ROC) analysis of predictions using probability annotation. We also performed new analyses such as comparison of the submitted predictions with those obtained with a Naïve disorder prediction method and with predictions from the disorder prediction databases D2P2 and MobiDB. On average, the methods participating in CASP10 demonstrated slightly better performance than those in CASP9.

  20. Assessment of protein disorder region predictions in CASP10

    KAUST Repository

    Monastyrskyy, Bohdan

    2013-11-22

    The article presents the assessment of disorder region predictions submitted to CASP10. The evaluation is based on the three measures tested in previous CASPs: (i) balanced accuracy, (ii) the Matthews correlation coefficient for the binary predictions, and (iii) the area under the curve in the receiver operating characteristic (ROC) analysis of predictions using probability annotation. We also performed new analyses such as comparison of the submitted predictions with those obtained with a Naïve disorder prediction method and with predictions from the disorder prediction databases D2P2 and MobiDB. On average, the methods participating in CASP10 demonstrated slightly better performance than those in CASP9.

  1. Using the SIMGRO regional hydrological model to evaluate salinity control measures in an irrigation area

    NARCIS (Netherlands)

    Kupper, E.; Querner, E.P.; Morábito, J.A.; Menenti, M.

    2002-01-01

    In irrigated areas with drainage and an important interaction with the groundwater system, it is often difficult to predict effects of measures to control salinity. Therefore, in order to evaluate measures to control salinity the SIMGRO integrated regional hydrological model was extended with a

  2. Nonparametric conditional predictive regions for time series

    NARCIS (Netherlands)

    de Gooijer, J.G.; Zerom Godefay, D.

    2000-01-01

    Several nonparametric predictors based on the Nadaraya-Watson kernel regression estimator have been proposed in the literature. They include the conditional mean, the conditional median, and the conditional mode. In this paper, we consider three types of predictive regions for these predictors — the

  3. Wind turbine control and model predictive control for uncertain systems

    DEFF Research Database (Denmark)

    Thomsen, Sven Creutz

    as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...

  4. Improved fuzzy PID controller design using predictive functional control structure.

    Science.gov (United States)

    Wang, Yuzhong; Jin, Qibing; Zhang, Ridong

    2017-11-01

    In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Moment-ration imaging of seismic regions for earthquake prediction

    Science.gov (United States)

    Lomnitz, Cinna

    1993-10-01

    An algorithm for predicting large earthquakes is proposed. The reciprocal ratio (mri) of the residual seismic moment to the total moment release in a region is used for imaging seismic moment precursors. Peaks in mri predict recent major earthquakes, including the 1985 Michoacan, 1985 central Chile, and 1992 Eureka, California earthquakes.

  6. Advancing Environmental Prediction Capabilities for the Polar Regions and Beyond during The Year of Polar Prediction

    Science.gov (United States)

    Werner, Kirstin; Goessling, Helge; Hoke, Winfried; Kirchhoff, Katharina; Jung, Thomas

    2017-04-01

    Environmental changes in polar regions open up new opportunities for economic and societal operations such as vessel traffic related to scientific, fishery and tourism activities, and in the case of the Arctic also enhanced resource development. The availability of current and accurate weather and environmental information and forecasts will therefore play an increasingly important role in aiding risk reduction and safety management around the poles. The Year of Polar Prediction (YOPP) has been established by the World Meteorological Organization's World Weather Research Programme as the key activity of the ten-year Polar Prediction Project (PPP; see more on www.polarprediction.net). YOPP is an internationally coordinated initiative to significantly advance our environmental prediction capabilities for the polar regions and beyond, supporting improved weather and climate services. Scheduled to take place from mid-2017 to mid-2019, the YOPP core phase covers an extended period of intensive observing, modelling, prediction, verification, user-engagement and education activities in the Arctic and Antarctic, on a wide range of time scales from hours to seasons. The Year of Polar Prediction will entail periods of enhanced observational and modelling campaigns in both polar regions. With the purpose to close the gaps in the conventional polar observing systems in regions where the observation network is sparse, routine observations will be enhanced during Special Observing Periods for an extended period of time (several weeks) during YOPP. This will allow carrying out subsequent forecasting system experiments aimed at optimizing observing systems in the polar regions and providing insight into the impact of better polar observations on forecast skills in lower latitudes. With various activities and the involvement of a wide range of stakeholders, YOPP will contribute to the knowledge base needed to managing the opportunities and risks that come with polar climate change.

  7. Regional brain morphometry predicts memory rehabilitation outcome after traumatic brain injury

    Directory of Open Access Journals (Sweden)

    Gary E Strangman

    2010-10-01

    Full Text Available Cognitive deficits following traumatic brain injury (TBI commonly include difficulties with memory, attention, and executive dysfunction. These deficits are amenable to cognitive rehabilitation, but optimally selecting rehabilitation programs for individual patients remains a challenge. Recent methods for quantifying regional brain morphometry allow for automated quantification of tissue volumes in numerous distinct brain structures. We hypothesized that such quantitative structural information could help identify individuals more or less likely to benefit from memory rehabilitation. Fifty individuals with TBI of all severities who reported having memory difficulties first underwent structural MRI scanning. They then participated in a 12 session memory rehabilitation program emphasizing internal memory strategies (I-MEMS. Primary outcome measures (HVLT, RBMT were collected at the time of the MRI scan, immediately following therapy, and again at one month post-therapy. Regional brain volumes were used to predict outcome, adjusting for standard predictors (e.g., injury severity, age, education, pretest scores. We identified several brain regions that provided significant predictions of rehabilitation outcome, including the volume of the hippocampus, the lateral prefrontal cortex, the thalamus, and several subregions of the cingulate cortex. The prediction range of regional brain volumes were in some cases nearly equal in magnitude to prediction ranges provided by pretest scores on the outcome variable. We conclude that specific cerebral networks including these regions may contribute to learning during I-MEMS rehabilitation, and suggest that morphometric measures may provide substantial predictive value for rehabilitation outcome in other cognitive interventions as well.

  8. Analysis of the regional MiKlip decadal prediction system over Europe: skill, added value of regionalization, and ensemble size dependeny

    Science.gov (United States)

    Reyers, Mark; Moemken, Julia; Pinto, Joaquim; Feldmann, Hendrik; Kottmeier, Christoph; MiKlip Module-C Team

    2017-04-01

    Decadal climate predictions can provide a useful basis for decision making support systems for the public and private sectors. Several generations of decadal hindcasts and predictions have been generated throughout the German research program MiKlip. Together with the global climate predictions computed with MPI-ESM, the regional climate model (RCM) COSMO-CLM is used for regional downscaling by MiKlip Module-C. The RCMs provide climate information on spatial and temporal scales closer to the needs of potential users. In this study, two downscaled hindcast generations are analysed (named b0 and b1). The respective global generations are both initialized by nudging them towards different reanalysis anomaly fields. An ensemble of five starting years (1961, 1971, 1981, 1991, and 2001), each comprising ten ensemble members, is used for both generations in order to quantify the regional decadal prediction skill for precipitation and near-surface temperature and wind speed over Europe. All datasets (including hindcasts, observations, reanalysis, and historical MPI-ESM runs) are pre-processed in an analogue manner by (i) removing the long-term trend and (ii) re-gridding to a common grid. Our analysis shows that there is potential for skillful decadal predictions over Europe in the regional MiKlip ensemble, but the skill is not systematic and depends on the PRUDENCE region and the variable. Further, the differences between the two hindcast generations are mostly small. As we used detrended time series, the predictive skill found in our study can probably attributed to reasonable predictions of anomalies which are associated with the natural climate variability. In a sensitivity study, it is shown that the results may strongly change when the long-term trend is kept in the datasets, as here the skill of predicting the long-term trend (e.g. for temperature) also plays a major role. The regionalization of the global ensemble provides an added value for decadal predictions for

  9. Hybrid Predictive Control for Dynamic Transport Problems

    CERN Document Server

    Núñez, Alfredo A; Cortés, Cristián E

    2013-01-01

    Hybrid Predictive Control for Dynamic Transport Problems develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems. Operational and service policies are considered, as well as cost reduction. The control structure is based on a sound definition of the key variables and their evolution. A flexible objective function able to capture the predictive behaviour of the system variables is described. Coupled with efficient algorithms, mainly drawn from the area of computational intelligence, this is shown to optimize performance indices for real-time applications. The framework of the proposed predictive control methodology is generic and, being able to solve nonlinear mixed-integer optimization problems dynamically, is readily extendable to other industrial processes. The main topics of this book are: ●hybrid predictive control (HPC) ...

  10. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  11. Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus

    pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants). 2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system. 3.Develop optimization algorithms....... Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models. Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices...... that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected...

  12. Composite control for raymond mill based on model predictive control and disturbance observer

    Directory of Open Access Journals (Sweden)

    Dan Niu

    2016-03-01

    Full Text Available In the raymond mill grinding process, precise control of operating load is vital for the high product quality. However, strong external disturbances, such as variations of ore size and ore hardness, usually cause great performance degradation. It is not easy to control the current of raymond mill constant. Several control strategies have been proposed. However, most of them (such as proportional–integral–derivative and model predictive control reject disturbances just through feedback regulation, which may lead to poor control performance in the presence of strong disturbances. For improving disturbance rejection, a control method based on model predictive control and disturbance observer is put forward in this article. The scheme employs disturbance observer as feedforward compensation and model predictive control controller as feedback regulation. The test results illustrate that compared with model predictive control method, the proposed disturbance observer–model predictive control method can obtain significant superiority in disturbance rejection, such as shorter settling time and smaller peak overshoot under strong disturbances.

  13. Predicting Loss-of-Control Boundaries Toward a Piloting Aid

    Science.gov (United States)

    Barlow, Jonathan; Stepanyan, Vahram; Krishnakumar, Kalmanje

    2012-01-01

    This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture.

  14. A climate-based prediction model in the high-risk clusters of the Mekong Delta region, Vietnam: towards improving dengue prevention and control.

    Science.gov (United States)

    Phung, Dung; Talukder, Mohammad Radwanur Rahman; Rutherford, Shannon; Chu, Cordia

    2016-10-01

    To develop a prediction score scheme useful for prevention practitioners and authorities to implement dengue preparedness and controls in the Mekong Delta region (MDR). We applied a spatial scan statistic to identify high-risk dengue clusters in the MDR and used generalised linear-distributed lag models to examine climate-dengue associations using dengue case records and meteorological data from 2003 to 2013. The significant predictors were collapsed into categorical scales, and the β-coefficients of predictors were converted to prediction scores. The score scheme was validated for predicting dengue outbreaks using ROC analysis. The north-eastern MDR was identified as the high-risk cluster. A 1 °C increase in temperature at lag 1-4 and 5-8 weeks increased the dengue risk 11% (95% CI, 9-13) and 7% (95% CI, 6-8), respectively. A 1% rise in humidity increased dengue risk 0.9% (95% CI, 0.2-1.4) at lag 1-4 and 0.8% (95% CI, 0.2-1.4) at lag 5-8 weeks. Similarly, a 1-mm increase in rainfall increased dengue risk 0.1% (95% CI, 0.05-0.16) at lag 1-4 and 0.11% (95% CI, 0.07-0.16) at lag 5-8 weeks. The predicted scores performed with high accuracy in diagnosing the dengue outbreaks (96.3%). This study demonstrates the potential usefulness of a dengue prediction score scheme derived from complex statistical models for high-risk dengue clusters. We recommend a further study to examine the possibility of incorporating such a score scheme into the dengue early warning system in similar climate settings. © 2016 John Wiley & Sons Ltd.

  15. Intelligent Predictive Control of Nonlienar Processes Using

    DEFF Research Database (Denmark)

    Nørgård, Peter Magnus; Sørensen, Paul Haase; Poulsen, Niels Kjølstad

    1996-01-01

    This paper presents a novel approach to design of generalized predictive controllers (GPC) for nonlinear processes. A neural network is used for modelling the process and a gain-scheduling type of GPC is subsequently designed. The combination of neural network models and predictive control has...... frequently been discussed in the neural network community. This paper proposes an approximate scheme, the approximate predictive control (APC), which facilitates the implementation and gives a substantial reduction in the required amount of computations. The method is based on a technique for extracting...... linear models from a nonlinear neural network and using them in designing the control system. The performance of the controller is demonstrated in a simulation study of a pneumatic servo system...

  16. Ellipsoidal prediction regions for multivariate uncertainty characterization

    DEFF Research Database (Denmark)

    Golestaneh, Faranak; Pinson, Pierre; Azizipanah-Abarghooee, Rasoul

    2018-01-01

    While substantial advances are observed in probabilistic forecasting for power system operation and electricity market applications, most approaches are still developed in a univariate framework. This prevents from informing about the interdependence structure among locations, lead times and vari......While substantial advances are observed in probabilistic forecasting for power system operation and electricity market applications, most approaches are still developed in a univariate framework. This prevents from informing about the interdependence structure among locations, lead times...... probability guarantees and minimum conservativeness. A skill score is proposed for quantitative assessment of the quality of prediction ellipsoids. A set of experiments is used to illustrate the discrimination ability of the proposed scoring rule for potential misspecification of ellipsoidal prediction regions...

  17. Regional Differences in Brain Volume Predict the Acquisition of Skill in a Complex Real-Time Strategy Videogame

    Science.gov (United States)

    Basak, Chandramallika; Voss, Michelle W.; Erickson, Kirk I.; Boot, Walter R.; Kramer, Arthur F.

    2011-01-01

    Previous studies have found that differences in brain volume among older adults predict performance in laboratory tasks of executive control, memory, and motor learning. In the present study we asked whether regional differences in brain volume as assessed by the application of a voxel-based morphometry technique on high resolution MRI would also…

  18. Robust entry guidance using linear covariance-based model predictive control

    Directory of Open Access Journals (Sweden)

    Jianjun Luo

    2017-02-01

    Full Text Available For atmospheric entry vehicles, guidance design can be accomplished by solving an optimal issue using optimal control theories. However, traditional design methods generally focus on the nominal performance and do not include considerations of the robustness in the design process. This paper proposes a linear covariance-based model predictive control method for robust entry guidance design. Firstly, linear covariance analysis is employed to directly incorporate the robustness into the guidance design. The closed-loop covariance with the feedback updated control command is initially formulated to provide the expected errors of the nominal state variables in the presence of uncertainties. Then, the closed-loop covariance is innovatively used as a component of the cost function to guarantee the robustness to reduce its sensitivity to uncertainties. After that, the models predictive control is used to solve the optimal problem, and the control commands (bank angles are calculated. Finally, a series of simulations for different missions have been completed to demonstrate the high performance in precision and the robustness with respect to initial perturbations as well as uncertainties in the entry process. The 3σ confidence region results in the presence of uncertainties which show that the robustness of the guidance has been improved, and the errors of the state variables are decreased by approximately 35%.

  19. Ambient air quality predictions in the Athabasca oil sands region

    International Nuclear Information System (INIS)

    1996-01-01

    This report presents dispersion modelling predictions for SO 2 , NOx, CO, HC and particulate matter (PM), which complement regional monitoring observations. The air quality simulation models provide a scientific means of relating industrial emissions to changes in ambient air quality. The four models applied to the emission sources in the region were: (1) SCREEN3, (2) ISC3BE, (3) ADEPT2, and (4) the box model. Model predictions were compared to air quality guidelines. It was concluded that the largest SO 2 concentrations were associated with intermittent flaring, and with the Suncor Powerhouse whose emissions are continuous. 45 refs., 36 tabs., 40 figs

  20. Individual differences in brainstem and basal ganglia structure predict postural control and balance loss in young and older adults.

    Science.gov (United States)

    Boisgontier, Matthieu P; Cheval, Boris; Chalavi, Sima; van Ruitenbeek, Peter; Leunissen, Inge; Levin, Oron; Nieuwboer, Alice; Swinnen, Stephan P

    2017-02-01

    It remains unclear which specific brain regions are the most critical for human postural control and balance, and whether they mediate the effect of age. Here, associations between postural performance and corticosubcortical brain regions were examined in young and older adults using multiple structural imaging and linear mixed models. Results showed that of the regions involved in posture, the brainstem was the strongest predictor of postural control and balance: lower brainstem volume predicted larger center of pressure deviation and higher odds of balance loss. Analyses of white and gray matter in the brainstem showed that the pedunculopontine nucleus area appeared to be critical for postural control in both young and older adults. In addition, the brainstem mediated the effect of age on postural control, underscoring the brainstem's fundamental role in aging. Conversely, lower basal ganglia volume predicted better postural performance, suggesting an association between greater neural resources in the basal ganglia and greater movement vigor, resulting in exaggerated postural adjustments. Finally, results showed that practice, shorter height and heavier weight (i.e., higher body mass index), higher total physical activity, and larger ankle active (but not passive) range of motion were predictive of more stable posture, irrespective of age. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. Modeling and Control of CSTR using Model based Neural Network Predictive Control

    OpenAIRE

    Shrivastava, Piyush

    2012-01-01

    This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some commen...

  2. [Predictive ocular motor control in Parkinson's disease].

    Science.gov (United States)

    Ying, Li; Liu, Zhen-Guo; Chen, Wei; Gan, Jing; Wang, Wen-An

    2008-02-19

    To investigate the changes of predictive ocular motor function in the patients with Parkinson's disease (PD), and to discuss its clinical value. Videonystagmography (VNG) was used to examine 24 patients with idiopathic Parkinson's disease, 15 males and 9 females, aged 61 +/- 6 (50-69), and 24 sex and age-matched healthy control subjects on random ocular saccade (with the target moving at random intervals to random positions) and predictive ocular saccade (with the 1.25-second light target moving 10 degrees right or left from the center). In the random ocular saccade program, the latency of saccade of the PD patients was 284 ms +/- 58 ms, significantly longer than that of the healthy controls (236 ms +/- 37 ms, P = 0.003). In the predictive ocular saccade pattern, the latency of saccades the PD patients was 150 ms +/- 138 ms, significantly longer than that of the healthy controls (59 ms +/- 102 ms, P = 0.002). The appearance rate of predictive saccades (with the latency of saccade <80 ms) in the PD group was 21%, significantly lower than that in the control group (31%, P = 0.003). There is dysfunction of predictive ocular motor control in the PD patients, and the cognitive function may be impaired at the early stage of PD.

  3. Model Predictive Control application for real time operation of controlled structures for the Water Authority Noorderzijlvest, The Netherlands

    Science.gov (United States)

    van Heeringen, Klaas-Jan; Gooijer, Jan; Knot, Floris; Talsma, Jan

    2015-04-01

    In the Netherlands, flood protection has always been a key issue to protect settlements against storm surges and riverine floods. Whereas flood protection traditionally focused on structural measures, nowadays the availability of meteorological and hydrological forecasts enable the application of more advanced real-time control techniques for operating the existing hydraulic infrastructure in an anticipatory and more efficient way. Model Predictive Control (MPC) is a powerful technique to derive optimal control variables with the help of model based predictions evaluated against a control objective. In a project for the regional water authority Noorderzijlvest in the north of the Netherlands, it has been shown that MPC can increase the safety level of the system during flood events by an anticipatory pre-release of water. Furthermore, energy costs of pumps can be reduced by making tactical use of the water storage and shifting pump activities during normal operating conditions to off-peak hours. In this way cheap energy is used in combination of gravity flow through gates during low tide periods. MPC has now been implemented for daily operational use of the whole water system of the water authority Noorderzijlvest. The system developed to a real time decision support system which not only supports the daily operation but is able to directly implement the optimal control settings at the structures. We explain how we set-up and calibrated a prediction model (RTC-Tools) that is accurate and fast enough for optimization purposes, and how we integrated it in the operational flood early warning system (Delft-FEWS). Beside the prediction model, the weights and the factors of the objective function are an important element of MPC, since they shape the control objective. We developed special features in Delft-FEWS to allow the operators to adjust the objective function in order to meet changing requirements and to evaluate different control strategies.

  4. Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current

    Directory of Open Access Journals (Sweden)

    Wenbiao Li

    2016-04-01

    Full Text Available Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC balancing. To solve these problems, multi-objective predictive balancing control (MOPBC based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process.

  5. Regional prediction of basin-scale brown trout habitat suitability

    Directory of Open Access Journals (Sweden)

    S. Ceola

    2014-09-01

    Full Text Available In this study we propose a novel method for the estimation of ecological indices describing the habitat suitability of brown trout (Salmo trutta. Traditional hydrological tools are coupled with an innovative regional geostatistical technique, aiming at the prediction of the brown trout habitat suitability index where partial or totally ungauged conditions occur. Several methods for the assessment of ecological indices are already proposed in the scientific literature, but the possibility of exploiting a geostatistical prediction model, such as Topological Kriging, has never been investigated before. In order to develop a regional habitat suitability model we use the habitat suitability curve, obtained from measured data of brown trout adult individuals collected in several river basins across the USA. The Top-kriging prediction model is then employed to assess the spatial correlation between upstream and downstream habitat suitability indices. The study area is the Metauro River basin, located in the central part of Italy (Marche region, for which both water depth and streamflow data were collected. The present analysis focuses on discharge values corresponding to the 0.1-, 0.5-, 0.9-empirical quantiles derived from flow-duration curves available for seven gauging stations located within the study area, for which three different suitability indices (i.e. ψ10, ψ50 and ψ90 are evaluated. The results of this preliminary analysis are encouraging showing Nash-Sutcliffe efficiencies equal to 0.52, 0.65, and 0.69, respectively.

  6. Regional prediction of basin-scale brown trout habitat suitability

    Science.gov (United States)

    Ceola, S.; Pugliese, A.

    2014-09-01

    In this study we propose a novel method for the estimation of ecological indices describing the habitat suitability of brown trout (Salmo trutta). Traditional hydrological tools are coupled with an innovative regional geostatistical technique, aiming at the prediction of the brown trout habitat suitability index where partial or totally ungauged conditions occur. Several methods for the assessment of ecological indices are already proposed in the scientific literature, but the possibility of exploiting a geostatistical prediction model, such as Topological Kriging, has never been investigated before. In order to develop a regional habitat suitability model we use the habitat suitability curve, obtained from measured data of brown trout adult individuals collected in several river basins across the USA. The Top-kriging prediction model is then employed to assess the spatial correlation between upstream and downstream habitat suitability indices. The study area is the Metauro River basin, located in the central part of Italy (Marche region), for which both water depth and streamflow data were collected. The present analysis focuses on discharge values corresponding to the 0.1-, 0.5-, 0.9-empirical quantiles derived from flow-duration curves available for seven gauging stations located within the study area, for which three different suitability indices (i.e. ψ10, ψ50 and ψ90) are evaluated. The results of this preliminary analysis are encouraging showing Nash-Sutcliffe efficiencies equal to 0.52, 0.65, and 0.69, respectively.

  7. Robot trajectory tracking with self-tuning predicted control

    Science.gov (United States)

    Cui, Xianzhong; Shin, Kang G.

    1988-01-01

    A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.

  8. HotRegion: a database of predicted hot spot clusters.

    Science.gov (United States)

    Cukuroglu, Engin; Gursoy, Attila; Keskin, Ozlem

    2012-01-01

    Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. We propose a database called HotRegion, which provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided. HotRegion is accessible at http://prism.ccbb.ku.edu.tr/hotregion.

  9. Prediction and control of rock burst of coal seam contacting gas in deep mining

    Energy Technology Data Exchange (ETDEWEB)

    En-yuan Wang; Xiao-fei Liu; En-lai Zhao; Zhen-tang Liu [China University of Mining and Technology, Xuzhou (China). School of Safety Engineering

    2009-06-15

    By analyzing the characteristics and the production mechanism of rock burst that goes with abnormal gas emission in deep coal seams, the essential method of eliminating abnormal gas emission by eliminating the occurrence of rock burst or depressing the magnitude of rock burst was considered. The No.237 working face in Nanshan coal mine was selected as the typical working face contacting gas in deep mining; aimed at this working face, a system of rock burst prediction and control for coal seam contacting gas in deep mining was established using the three-dimensional distinct element code software 3DEC. This system includes three parts: (1) regional prediction of rock burst hazard before mining; (2) local prediction of rock burst hazard during mining; and (3) rock burts control by an electromagnetic radiation method and specific drilling method. 8 refs., 4 figs., 1 tab.

  10. Predictive Variable Gain Iterative Learning Control for PMSM

    Directory of Open Access Journals (Sweden)

    Huimin Xu

    2015-01-01

    Full Text Available A predictive variable gain strategy in iterative learning control (ILC is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.

  11. 40 CFR 81.112 - Charleston Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.112 Charleston Intrastate Air Quality Control Region. The Charleston Intrastate Air Quality Control Region (South Carolina) consists of the territorial area encompassed by the... Quality Control Region: Region 1. 81.107Greenwood Intrastate Air Quality Control Region: Region 2. 81...

  12. Predictive Control of Networked Multiagent Systems via Cloud Computing.

    Science.gov (United States)

    Liu, Guo-Ping

    2017-01-18

    This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.

  13. Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.

    Directory of Open Access Journals (Sweden)

    Waqasuddin Khan

    Full Text Available Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions.We assembled a redundancy reduced dataset of SLiM (Short Linear Motif containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58.Next, we trained a bidirectional recurrent neural network (BRNN using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72 showing that multiple sources of information can be combined to produce results which are clearly superior to any single source.We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors.

  14. One Layer Nonlinear Economic Closed-Loop Generalized Predictive Control for a Wastewater Treatment Plant

    Directory of Open Access Journals (Sweden)

    Hicham El bahja

    2018-04-01

    Full Text Available The main scope of this paper is the proposal of a new single layer Nonlinear Economic Closed-Loop Generalized Predictive Control (NECLGPC as an efficient advanced control technique for improving economics in the operation of nonlinear plants. Instead of the classic dual-mode MPC (model predictive controller schemes, where the terminal control law defined in the terminal region is obtained offline solving a linear quadratic regulator problem, here the terminal control law in the NECLGPC is determined online by an unconstrained Nonlinear Generalized Predictive Control (NGPC. In order to make the optimization problem more tractable two considerations have been made in the present work. Firstly, the prediction model consisting of a nonlinear phenomenological model of the plant is expressed with linear structure and state dependent matrices. Secondly, instead of including the nonlinear economic cost in the objective function, an approximation of the reduced gradient of the economic function is used. These assumptions allow us to design an economic unconstrained nonlinear GPC analytically and to state the NECLGPC allow for the design of an economic problem as a QP (Quadratic Programing problem each sampling time. Four controllers based on GPC that differ in designs and structures are compared with the proposed control technique in terms of process performance and energy costs. Particularly, the methodology is implemented in the N-Removal process of a Wastewater Treatment Plant (WWTP and the results prove the efficiency of the method and that it can be used profitably in practical cases.

  15. Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

    Science.gov (United States)

    Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

    2017-10-01

    Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

  16. Prediction of lake depth across a 17-state region in the United States

    Science.gov (United States)

    Oliver, Samantha K.; Soranno, Patricia A.; Fergus, C. Emi; Wagner, Tyler; Winslow, Luke A.; Scott, Caren E.; Webster, Katherine E.; Downing, John A.; Stanley, Emily H.

    2016-01-01

    Lake depth is an important characteristic for understanding many lake processes, yet it is unknown for the vast majority of lakes globally. Our objective was to develop a model that predicts lake depth using map-derived metrics of lake and terrestrial geomorphic features. Building on previous models that use local topography to predict lake depth, we hypothesized that regional differences in topography, lake shape, or sedimentation processes could lead to region-specific relationships between lake depth and the mapped features. We therefore used a mixed modeling approach that included region-specific model parameters. We built models using lake and map data from LAGOS, which includes 8164 lakes with maximum depth (Zmax) observations. The model was used to predict depth for all lakes ≥4 ha (n = 42 443) in the study extent. Lake surface area and maximum slope in a 100 m buffer were the best predictors of Zmax. Interactions between surface area and topography occurred at both the local and regional scale; surface area had a larger effect in steep terrain, so large lakes embedded in steep terrain were much deeper than those in flat terrain. Despite a large sample size and inclusion of regional variability, model performance (R2 = 0.29, RMSE = 7.1 m) was similar to other published models. The relative error varied by region, however, highlighting the importance of taking a regional approach to lake depth modeling. Additionally, we provide the largest known collection of observed and predicted lake depth values in the United States.

  17. Finite-Control-Set Model Predictive Control (FCS-MPC) for Islanded Hybrid Microgrids

    OpenAIRE

    Yi, Zhehan; Babqi, Abdulrahman J.; Wang, Yishen; Shi, Di; Etemadi, Amir H.; Wang, Zhiwei; Huang, Bibin

    2018-01-01

    Microgrids consisting of multiple distributed energy resources (DERs) provide a promising solution to integrate renewable energies, e.g., solar photovoltaic (PV) systems. Hybrid AC/DC microgrids leverage the merits of both AC and DC power systems. In this paper, a control strategy for islanded multi-bus hybrid microgrids is proposed based on the Finite-Control-Set Model Predictive Control (FCS-MPC) technologies. The control loops are expedited by predicting the future states and determining t...

  18. Wind turbine control with constraint handling: a model predictive control approach

    DEFF Research Database (Denmark)

    Henriksen, Lars Christian; Hansen, Morten Hartvig; Poulsen, Niels Kjølstad

    2012-01-01

    on model predictive control, a control method well suited for constraint handling. The performance of the presented controller during an extreme operating gust is compared to that of a proportional-integral controller with integrator anti-windup. Furthermore, the presented controller-s capability...

  19. Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs

    Directory of Open Access Journals (Sweden)

    Ruan Jishou

    2007-04-01

    Full Text Available Abstract Background Traditionally, it is believed that the native structure of a protein corresponds to a global minimum of its free energy. However, with the growing number of known tertiary (3D protein structures, researchers have discovered that some proteins can alter their structures in response to a change in their surroundings or with the help of other proteins or ligands. Such structural shifts play a crucial role with respect to the protein function. To this end, we propose a machine learning method for the prediction of the flexible/rigid regions of proteins (referred to as FlexRP; the method is based on a novel sequence representation and feature selection. Knowledge of the flexible/rigid regions may provide insights into the protein folding process and the 3D structure prediction. Results The flexible/rigid regions were defined based on a dataset, which includes protein sequences that have multiple experimental structures, and which was previously used to study the structural conservation of proteins. Sequences drawn from this dataset were represented based on feature sets that were proposed in prior research, such as PSI-BLAST profiles, composition vector and binary sequence encoding, and a newly proposed representation based on frequencies of k-spaced amino acid pairs. These representations were processed by feature selection to reduce the dimensionality. Several machine learning methods for the prediction of flexible/rigid regions and two recently proposed methods for the prediction of conformational changes and unstructured regions were compared with the proposed method. The FlexRP method, which applies Logistic Regression and collocation-based representation with 95 features, obtained 79.5% accuracy. The two runner-up methods, which apply the same sequence representation and Support Vector Machines (SVM and Naïve Bayes classifiers, obtained 79.2% and 78.4% accuracy, respectively. The remaining considered methods are

  20. Climate Prediction Center - Monitoring and Data - Regional Climate Maps:

    Science.gov (United States)

    National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site government Web resources and services. HOME > Monitoring and Data > U.S. Climate Data > ; Precipitation & Temperature > Regional Climate Maps: USA Menu Weekly 1-Month 3-Month 12-Month Weekly

  1. DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach.

    Directory of Open Access Journals (Sweden)

    Zhiheng Wang

    Full Text Available The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database.The DisoMCS is available at http://cal.tongji.edu.cn/disorder/.

  2. Evaluation of performance of seasonal precipitation prediction at regional scale over India

    Science.gov (United States)

    Mohanty, U. C.; Nageswararao, M. M.; Sinha, P.; Nair, A.; Singh, A.; Rai, R. K.; Kar, S. C.; Ramesh, K. J.; Singh, K. K.; Ghosh, K.; Rathore, L. S.; Sharma, R.; Kumar, A.; Dhekale, B. S.; Maurya, R. K. S.; Sahoo, R. K.; Dash, G. P.

    2018-03-01

    The seasonal scale precipitation amount is an important ingredient in planning most of the agricultural practices (such as a type of crops, and showing and harvesting schedules). India being an agroeconomic country, the seasonal scale prediction of precipitation is directly linked to the socioeconomic growth of the nation. At present, seasonal precipitation prediction at regional scale is a challenging task for the scientific community. In the present study, an attempt is made to develop multi-model dynamical-statistical approach for seasonal precipitation prediction at the regional scale (meteorological subdivisions) over India for four prominent seasons which are winter (from December to February; DJF), pre-monsoon (from March to May; MAM), summer monsoon (from June to September; JJAS), and post-monsoon (from October to December; OND). The present prediction approach is referred as extended range forecast system (ERFS). For this purpose, precipitation predictions from ten general circulation models (GCMs) are used along with the India Meteorological Department (IMD) rainfall analysis data from 1982 to 2008 for evaluation of the performance of the GCMs, bias correction of the model results, and development of the ERFS. An extensive evaluation of the performance of the ERFS is carried out with dependent data (1982-2008) as well as independent predictions for the period 2009-2014. In general, the skill of the ERFS is reasonably better and consistent for all the seasons and different regions over India as compared to the GCMs and their simple mean. The GCM products failed to explain the extreme precipitation years, whereas the bias-corrected GCM mean and the ERFS improved the prediction and well represented the extremes in the hindcast period. The peak intensity, as well as regions of maximum precipitation, is better represented by the ERFS than the individual GCMs. The study highlights the improvement of forecast skill of the ERFS over 34 meteorological subdivisions

  3. Controlling cyclic combustion timing variations using a symbol-statistics predictive approach in an HCCI engine

    International Nuclear Information System (INIS)

    Ghazimirsaied, Ahmad; Koch, Charles Robert

    2012-01-01

    Highlights: ► Misfire reduction in a combustion engine based on chaotic theory methods. ► Chaotic theory analysis of cyclic variation of a HCCI engine near misfire. ► Symbol sequence approach is used to predict ignition timing one cycle-ahead. ► Prediction is combined with feedback control to lower HCCI combustion variation. ► Feedback control extends the HCCI operating range into the misfire region. -- Abstract: Cyclic variation of a Homogeneous Charge Compression Ignition (HCCI) engine near misfire is analyzed using chaotic theory methods and feedback control is used to stabilize high cyclic variations. Variation of consecutive cycles of θ Pmax (the crank angle of maximum cylinder pressure over an engine cycle) for a Primary Reference Fuel engine is analyzed near misfire operation for five test points with similar conditions but different octane numbers. The return map of the time series of θ Pmax at each combustion cycle reveals the deterministic and random portions of the dynamics near misfire for this HCCI engine. A symbol-statistic approach is used to predict θ Pmax one cycle-ahead. Predicted θ Pmax has similar dynamical behavior to the experimental measurements. Based on this cycle ahead prediction, and using fuel octane as the input, feedback control is used to stabilize the instability of θ Pmax variations at this engine condition near misfire.

  4. Predictive torque and flux control of an induction machine drive ...

    Indian Academy of Sciences (India)

    Finite-state model predictive control; fuzzy decision making; multi-objective optimization; predictive torque control. Abstract. Among the numerous direct torque control techniques, the finite-state predictive torque control (FS-PTC) has emerged as a powerful alternative as it offers the fast dynamic response and the flexibility to ...

  5. Rate-Based Model Predictive Control of Turbofan Engine Clearance

    Science.gov (United States)

    DeCastro, Jonathan A.

    2006-01-01

    An innovative model predictive control strategy is developed for control of nonlinear aircraft propulsion systems and sub-systems. At the heart of the controller is a rate-based linear parameter-varying model that propagates the state derivatives across the prediction horizon, extending prediction fidelity to transient regimes where conventional models begin to lose validity. The new control law is applied to a demanding active clearance control application, where the objectives are to tightly regulate blade tip clearances and also anticipate and avoid detrimental blade-shroud rub occurrences by optimally maintaining a predefined minimum clearance. Simulation results verify that the rate-based controller is capable of satisfying the objectives during realistic flight scenarios where both a conventional Jacobian-based model predictive control law and an unconstrained linear-quadratic optimal controller are incapable of doing so. The controller is evaluated using a variety of different actuators, illustrating the efficacy and versatility of the control approach. It is concluded that the new strategy has promise for this and other nonlinear aerospace applications that place high importance on the attainment of control objectives during transient regimes.

  6. Domestic appliances energy optimization with model predictive control

    International Nuclear Information System (INIS)

    Rodrigues, E.M.G.; Godina, R.; Pouresmaeil, E.; Ferreira, J.R.; Catalão, J.P.S.

    2017-01-01

    Highlights: • An alternative power management control for home appliances that require thermal regulation is presented. • A Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat. • Problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. • A modulation scheme of a two-level Model Predictive Control signal as an interface block is presented. • The implementation costs in home appliances with thermal regulation requirements are reduced. - Abstract: A vital element in making a sustainable world is correctly managing the energy in the domestic sector. Thus, this sector evidently stands as a key one for to be addressed in terms of climate change goals. Increasingly, people are aware of electricity savings by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Much of the reduction was due to technological improvements, however with the advancing of the years new types of control arise. Domestic appliances with the purpose of heating and cooling rely on thermostatic regulation technique. The study in this paper is focused on the subject of an alternative power management control for home appliances that require thermal regulation. In this paper a Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat with the aim of minimizing the cooling energy consumption through the minimization of the energy cost while satisfying the adequate temperature range for the human comfort. In addition, the Model Predictive Control problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. For this purpose, the typical consumption of a 24 h period of a summer day was simulated a three-level tariff scheme was used. The new

  7. A balance of activity in brain control and reward systems predicts self-regulatory outcomes.

    Science.gov (United States)

    Lopez, Richard B; Chen, Pin-Hao A; Huckins, Jeremy F; Hofmann, Wilhelm; Kelley, William M; Heatherton, Todd F

    2017-05-01

    Previous neuroimaging work has shown that increased reward-related activity following exposure to food cues is predictive of self-control failure. The balance model suggests that self-regulation failures result from an imbalance in reward and executive control mechanisms. However, an open question is whether the relative balance of activity in brain systems associated with executive control (vs reward) supports self-regulatory outcomes when people encounter tempting cues in daily life. Sixty-nine chronic dieters, a population known for frequent lapses in self-control, completed a food cue-reactivity task during an fMRI scanning session, followed by a weeklong sampling of daily eating behaviors via ecological momentary assessment. We related participants' food cue activity in brain systems associated with executive control and reward to real-world eating patterns. Specifically, a balance score representing the amount of activity in brain regions associated with self-regulatory control, relative to automatic reward-related activity, predicted dieters' control over their eating behavior during the following week. This balance measure may reflect individual self-control capacity and be useful for examining self-regulation success in other domains and populations. © The Author (2017). Published by Oxford University Press.

  8. Model predictive control for spacecraft rendezvous in elliptical orbit

    Science.gov (United States)

    Li, Peng; Zhu, Zheng H.

    2018-05-01

    This paper studies the control of spacecraft rendezvous with attitude stable or spinning targets in an elliptical orbit. The linearized Tschauner-Hempel equation is used to describe the motion of spacecraft and the problem is formulated by model predictive control. The control objective is to maximize control accuracy and smoothness simultaneously to avoid unexpected change or overshoot of trajectory for safe rendezvous. It is achieved by minimizing the weighted summations of control errors and increments. The effects of two sets of horizons (control and predictive horizons) in the model predictive control are examined in terms of fuel consumption, rendezvous time and computational effort. The numerical results show the proposed control strategy is effective.

  9. Neural Generalized Predictive Control of a non-linear Process

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1998-01-01

    The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability qu...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem.......The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability...... qualities. The controller is a non-linear version of the well-known generalized predictive controller developed in linear control theory. It involves minimization of a cost function which in the present case has to be done numerically. Therefore, we develop the numerical algorithms necessary in substantial...

  10. Applying model predictive control to power system frequency control

    OpenAIRE

    Ersdal, AM; Imsland, L; Cecilio, IM; Fabozzi, D; Thornhill, NF

    2013-01-01

    16.07.14 KB Ok to add accepted version to Spiral Model predictive control (MPC) is investigated as a control method which may offer advantages in frequency control of power systems than the control methods applied today, especially in presence of increased renewable energy penetration. The MPC includes constraints on both generation amount and generation rate of change, and it is tested on a one-area system. The proposed MPC is tested against a conventional proportional-integral (PI) cont...

  11. 40 CFR 81.88 - Billings Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.88 Billings Intrastate Air Quality Control Region. The Metropolitan Billings Intrastate Air Quality Control Region (Montana) has been renamed the Billings Intrastate Air Quality Control... to by Montana authorities as follows: Sec. 481.168Great Falls Intrastate Air Quality Control Region...

  12. COLLISION-AVOIDANCE FOR MOBILE ROBOTS USING REGION OF CERTAINTY: A PREDICTIVE APPROACH

    Directory of Open Access Journals (Sweden)

    B. MANUP

    2016-01-01

    Full Text Available In on-line environment, obstacles may exhibit different trajectory. Trajectory analysis of the obstacle is essential in determining its future location. If this analysis is accurate the futuristic region where robot and obstacle collision is likely to occur can be estimated. This enables the mobile robot to take corrective action prior to collision. In this approach, the motion pattern of the obstacle is analysed by taking into account the past co-ordinates traversed by the obstacle. Then the futuristic region where the obstacle is likely to occupy is predicted. This region is termed as region of certainty. Simulation results shows that the approach gives more reliable prediction as many number of sample points representing the past positions travelled by the obstacles are taken into consideration. The algorithm yielded better performance under higher obstacle velocity conditions and the results were compared with distance time transform method.

  13. Tube Model Predictive Control with an Auxiliary Sliding Mode Controller

    Directory of Open Access Journals (Sweden)

    Miodrag Spasic

    2016-07-01

    Full Text Available This paper studies Tube Model Predictive Control (MPC with a Sliding Mode Controller (SMC as an auxiliary controller. It is shown how to calculate the tube widths under SMC control, and thus how much the constraints of the nominal MPC have to be tightened in order to achieve robust stability and constraint fulfillment. The analysis avoids the assumption of infinitely fast switching in the SMC controller.

  14. 40 CFR 81.51 - Portland Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.51 Portland Interstate Air Quality Control Region. The Portland Interstate Air Quality Control Region (Oregon-Washington) has been revised to consist of the territorial area... Portland Interstate Air Quality Control Region (Oregon-Washington) will be referred to by Washington...

  15. Dynamics and control of quadcopter using linear model predictive control approach

    Science.gov (United States)

    Islam, M.; Okasha, M.; Idres, M. M.

    2017-12-01

    This paper investigates the dynamics and control of a quadcopter using the Model Predictive Control (MPC) approach. The dynamic model is of high fidelity and nonlinear, with six degrees of freedom that include disturbances and model uncertainties. The control approach is developed based on MPC to track different reference trajectories ranging from simple ones such as circular to complex helical trajectories. In this control technique, a linearized model is derived and the receding horizon method is applied to generate the optimal control sequence. Although MPC is computer expensive, it is highly effective to deal with the different types of nonlinearities and constraints such as actuators’ saturation and model uncertainties. The MPC parameters (control and prediction horizons) are selected by trial-and-error approach. Several simulation scenarios are performed to examine and evaluate the performance of the proposed control approach using MATLAB and Simulink environment. Simulation results show that this control approach is highly effective to track a given reference trajectory.

  16. 40 CFR 81.36 - Maricopa Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.36 Maricopa Intrastate Air Quality Control Region. The Phoenix-Tucson Intrastate Air Quality Control Region has been renamed the Maricopa Intrastate Air Quality Control Region... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Maricopa Intrastate Air Quality...

  17. Attention and predictions: control of spatial attention beyond the endogenous-exogenous dichotomy

    Directory of Open Access Journals (Sweden)

    Emiliano eMacaluso

    2013-10-01

    Full Text Available The mechanisms of attention control have been extensively studied with a variety of methodologies in animals and in humans. Human studies using non-invasive imaging techniques highlighted a remarkable difference between the pattern of responses in dorsal fronto-parietal regions vs. ventral fronto-parietal regions, primarily lateralized to the right hemisphere. Initially, this distinction at the neuro-physiological level has been related to the distinction between cognitive processes associated with strategic/endogenous vs. stimulus-driven/exogenous of attention control. Nonetheless, quite soon it has become evident that, in almost any situation, attention control entails a complex combination of factors related to both the current sensory input and endogenous aspects associated with the experimental context. Here, we review several of these aspects first discussing the joint contribution of endogenous and stimulus-driven factors during spatial orienting in complex environments and, then, turning to the role of expectations and predictions in spatial re-orienting. We emphasize that strategic factors play a pivotal role for the activation of the ventral system during stimulus-driven control, and that the dorsal system makes use of stimulus-driven signals for top-down control. We conclude that both the dorsal and the ventral fronto-parietal networks integrate endogenous and exogenous signals during spatial attention control and that future investigations should manipulate both these factors concurrently, so as to reveal to full extent of these interactions.

  18. Attention and predictions: control of spatial attention beyond the endogenous-exogenous dichotomy

    Science.gov (United States)

    Macaluso, Emiliano; Doricchi, Fabrizio

    2013-01-01

    The mechanisms of attention control have been extensively studied with a variety of methodologies in animals and in humans. Human studies using non-invasive imaging techniques highlighted a remarkable difference between the pattern of responses in dorsal fronto-parietal regions vs. ventral fronto-parietal (vFP) regions, primarily lateralized to the right hemisphere. Initially, this distinction at the neuro-physiological level has been related to the distinction between cognitive processes associated with strategic/endogenous vs. stimulus-driven/exogenous of attention control. Nonetheless, quite soon it has become evident that, in almost any situation, attention control entails a complex combination of factors related to both the current sensory input and endogenous aspects associated with the experimental context. Here, we review several of these aspects first discussing the joint contribution of endogenous and stimulus-driven factors during spatial orienting in complex environments and, then, turning to the role of expectations and predictions in spatial re-orienting. We emphasize that strategic factors play a pivotal role for the activation of the ventral system during stimulus-driven control, and that the dorsal system makes use of stimulus-driven signals for top-down control. We conclude that both the dorsal and the vFP networks integrate endogenous and exogenous signals during spatial attention control and that future investigations should manipulate both these factors concurrently, so as to reveal to full extent of these interactions. PMID:24155707

  19. Attention and predictions: control of spatial attention beyond the endogenous-exogenous dichotomy.

    Science.gov (United States)

    Macaluso, Emiliano; Doricchi, Fabrizio

    2013-01-01

    The mechanisms of attention control have been extensively studied with a variety of methodologies in animals and in humans. Human studies using non-invasive imaging techniques highlighted a remarkable difference between the pattern of responses in dorsal fronto-parietal regions vs. ventral fronto-parietal (vFP) regions, primarily lateralized to the right hemisphere. Initially, this distinction at the neuro-physiological level has been related to the distinction between cognitive processes associated with strategic/endogenous vs. stimulus-driven/exogenous of attention control. Nonetheless, quite soon it has become evident that, in almost any situation, attention control entails a complex combination of factors related to both the current sensory input and endogenous aspects associated with the experimental context. Here, we review several of these aspects first discussing the joint contribution of endogenous and stimulus-driven factors during spatial orienting in complex environments and, then, turning to the role of expectations and predictions in spatial re-orienting. We emphasize that strategic factors play a pivotal role for the activation of the ventral system during stimulus-driven control, and that the dorsal system makes use of stimulus-driven signals for top-down control. We conclude that both the dorsal and the vFP networks integrate endogenous and exogenous signals during spatial attention control and that future investigations should manipulate both these factors concurrently, so as to reveal to full extent of these interactions.

  20. 40 CFR 81.77 - Puerto Rico Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Puerto Rico Air Quality Control Region... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.77 Puerto Rico Air Quality Control Region. The Puerto Rico Air Quality Control Region...

  1. Failed rib region prediction in a human body model during crash events with precrash braking.

    Science.gov (United States)

    Guleyupoglu, B; Koya, B; Barnard, R; Gayzik, F S

    2018-02-28

    The objective of this study is 2-fold. We used a validated human body finite element model to study the predicted chest injury (focusing on rib fracture as a function of element strain) based on varying levels of simulated precrash braking. Furthermore, we compare deterministic and probabilistic methods of rib injury prediction in the computational model. The Global Human Body Models Consortium (GHBMC) M50-O model was gravity settled in the driver position of a generic interior equipped with an advanced 3-point belt and airbag. Twelve cases were investigated with permutations for failure, precrash braking system, and crash severity. The severities used were median (17 kph), severe (34 kph), and New Car Assessment Program (NCAP; 56.4 kph). Cases with failure enabled removed rib cortical bone elements once 1.8% effective plastic strain was exceeded. Alternatively, a probabilistic framework found in the literature was used to predict rib failure. Both the probabilistic and deterministic methods take into consideration location (anterior, lateral, and posterior). The deterministic method is based on a rubric that defines failed rib regions dependent on a threshold for contiguous failed elements. The probabilistic method depends on age-based strain and failure functions. Kinematics between both methods were similar (peak max deviation: ΔX head = 17 mm; ΔZ head = 4 mm; ΔX thorax = 5 mm; ΔZ thorax = 1 mm). Seat belt forces at the time of probabilistic failed region initiation were lower than those at deterministic failed region initiation. The probabilistic method for rib fracture predicted more failed regions in the rib (an analog for fracture) than the deterministic method in all but 1 case where they were equal. The failed region patterns between models are similar; however, there are differences that arise due to stress reduced from element elimination that cause probabilistic failed regions to continue to rise after no deterministic failed region would be

  2. Performance of Globally Linearized Controller and Two Region Fuzzy Logic Controller on a Nonlinear Process

    Directory of Open Access Journals (Sweden)

    N. Jaya

    2008-10-01

    Full Text Available In this work, a design and implementation of a Conventional PI controller, single region fuzzy logic controller, two region fuzzy logic controller and Globally Linearized Controller (GLC for a two capacity interacting nonlinear process is carried out. The performance of this process using single region FLC, two region FLC and GLC are compared with the performance of conventional PI controller about an operating point of 50 %. It has been observed that GLC and two region FLC provides better performance. Further, this procedure is also validated by real time experimentation using dSPACE.

  3. Evaluating the ability of regional models to predict local avian abundance

    Science.gov (United States)

    LeBrun, Jaymi J.; Thogmartin, Wayne E.; Miller, James R.

    2012-01-01

    Spatial modeling over broad scales can potentially direct conservation efforts to areas with high species-specific abundances. We examined the performance of regional models for predicting bird abundance at spatial scales typically addressed in conservation planning. Specifically, we used point count data on wood thrush (Hylocichla mustelina) and blue-winged warbler (Vermivora cyanoptera) from 2 time periods (1995-1998 and 2006-2007) to evaluate the ability of regional models derived via Bayesian hierarchical techniques to predict bird abundance. We developed models for each species within Bird Conservation Region (BCR) 23 in the upper midwestern United States at 800-ha, 8,000-ha, and approximately 80,000-ha scales. We obtained count data from the Breeding Bird Survey and land cover data from the National Land Cover Dataset (1992). We evaluated predictions from the best models, as defined by an information-theoretic criterion, using point count data collected within an ecological subregion of BCR 23 at 131 count stations in the 1990s and again in 2006-2007. Competing (Deviance Information Criteria rs = 0.57; P = 0.14), the survey period that most closely aligned with the time period of data used for regional model construction. Wood thrush models exhibited positive correlations with point count data for all survey areas and years combined (rs = 0.58, P ≤ 0.001). In comparison, blue-winged warbler models performed worse as time increased between the point count surveys and vintage of the model building data (rs = 0.03, P = 0.92 for Iowa and rs = 0.13, P = 0.51 for all areas, 2006-2007), likely related to the ephemeral nature of their preferred early successional habitat. Species abundance and sensitivity to changing habitat conditions seems to be an important factor in determining the predictive ability of regional models. Hierarchical models can be a useful tool for concentrating efforts at the scale of management units and should be one of many tools used by

  4. Frequency weighted model predictive control of wind turbine

    DEFF Research Database (Denmark)

    Klauco, Martin; Poulsen, Niels Kjølstad; Mirzaei, Mahmood

    2013-01-01

    This work is focused on applying frequency weighted model predictive control (FMPC) on three blade horizontal axis wind turbine (HAWT). A wind turbine is a very complex, non-linear system influenced by a stochastic wind speed variation. The reduced dynamics considered in this work are the rotatio...... predictive controller are presented. Statistical comparison between frequency weighted MPC, standard MPC and baseline PI controller is shown as well.......This work is focused on applying frequency weighted model predictive control (FMPC) on three blade horizontal axis wind turbine (HAWT). A wind turbine is a very complex, non-linear system influenced by a stochastic wind speed variation. The reduced dynamics considered in this work...... are the rotational degree of freedom of the rotor and the tower for-aft movement. The MPC design is based on a receding horizon policy and a linearised model of the wind turbine. Due to the change of dynamics according to wind speed, several linearisation points must be considered and the control design adjusted...

  5. Predicting the spread of sudden oak death in California (2010-2030): epidemic outcomes under no control

    Science.gov (United States)

    Ross K. Meentemeyer; Nik Cunniffe; Alex Cook; David M. Rizzo; Chris A. Gilligan

    2010-01-01

    Landscape- to regional-scale models of plant epidemics are direly needed to predict largescale impacts of disease and assess practicable options for control. While landscape heterogeneity is recognized as a major driver of disease dynamics, epidemiological models are rarely applied to realistic landscape conditions due to computational and data limitations. Here we...

  6. Validation of the mortality prediction equation for damage control ...

    African Journals Online (AJOL)

    , preoperative lowest pH and lowest core body temperature to derive an equation for the purpose of predicting mortality in damage control surgery. It was shown to reliably predict death despite damage control surgery. The equation derivation ...

  7. Some considerations regarding earthquake prediction - The case of Vrancea region -

    International Nuclear Information System (INIS)

    Enescu, Bogdan; Enescu, Dumitru

    2000-01-01

    Earthquake prediction research has been conducted for over 100 years with no obvious success. In the last year, the new modern concepts regarding the earthquake dynamics added another source of skepticism regarding the possibility of predicting earthquakes. However there are some recognizable trends, optimized in the recent years, which may give rise to more reliable and solid approaches to deal with this complex subject. In the light of these trends, emphasized by Aki, we try to analyze the new developments in the field, especially concerning the Vrancea region. (authors)

  8. Fault Tolerant Flight Control Using Sliding Modes and Subspace Identification-Based Predictive Control

    KAUST Repository

    Siddiqui, Bilal A.; El-Ferik, Sami; Abdelkader, Mohamed

    2016-01-01

    In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.

  9. Fault Tolerant Flight Control Using Sliding Modes and Subspace Identification-Based Predictive Control

    KAUST Repository

    Siddiqui, Bilal A.

    2016-07-26

    In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.

  10. [Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

    Science.gov (United States)

    Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

    2012-02-01

    In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

  11. Prediction of forces and moments for flight vehicle control effectors. Part 1: Validation of methods for predicting hypersonic vehicle controls forces and moments

    Science.gov (United States)

    Maughmer, Mark D.; Ozoroski, L.; Ozoroski, T.; Straussfogel, D.

    1990-01-01

    Many types of hypersonic aircraft configurations are currently being studied for feasibility of future development. Since the control of the hypersonic configurations throughout the speed range has a major impact on acceptable designs, it must be considered in the conceptual design stage. The ability of the aerodynamic analysis methods contained in an industry standard conceptual design system, APAS II, to estimate the forces and moments generated through control surface deflections from low subsonic to high hypersonic speeds is considered. Predicted control forces and moments generated by various control effectors are compared with previously published wind tunnel and flight test data for three configurations: the North American X-15, the Space Shuttle Orbiter, and a hypersonic research airplane concept. Qualitative summaries of the results are given for each longitudinal force and moment and each control derivative in the various speed ranges. Results show that all predictions of longitudinal stability and control derivatives are acceptable for use at the conceptual design stage. Results for most lateral/directional control derivatives are acceptable for conceptual design purposes; however, predictions at supersonic Mach numbers for the change in yawing moment due to aileron deflection and the change in rolling moment due to rudder deflection are found to be unacceptable. Including shielding effects in the analysis is shown to have little effect on lift and pitching moment predictions while improving drag predictions.

  12. Model Predictive Control of a Wave Energy Converter

    DEFF Research Database (Denmark)

    Andersen, Palle; Pedersen, Tom Søndergård; Nielsen, Kirsten Mølgaard

    2015-01-01

    In this paper reactive control and Model Predictive Control (MPC) for a Wave Energy Converter (WEC) are compared. The analysis is based on a WEC from Wave Star A/S designed as a point absorber. The model predictive controller uses wave models based on the dominating sea states combined with a model...... connecting undisturbed wave sequences to sequences of torque. Losses in the conversion from mechanical to electrical power are taken into account in two ways. Conventional reactive controllers are tuned for each sea state with the assumption that the converter has the same efficiency back and forth. MPC...

  13. Predicted Habitat Suitability for Leptoseris in the Au'au Channel Region

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This raster denotes predicted habitat suitability for Leptoseris in the Au'au Channel region. Maximum Entropy (MaxEnt) modeling software was used to create this...

  14. Predicted Habitat Suitability for Porites in the Au'au Channel Region

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This raster denotes predicted habitat suitability for Porites in the Au'au Channel region. Maximum Entropy (MaxEnt) modeling software was used to create this...

  15. Robust nonlinear model predictive control for nuclear power plants in load following operations with bounded xenon oscillations

    International Nuclear Information System (INIS)

    Eliasi, H.; Menhaj, M.B.; Davilu, H.

    2011-01-01

    Research highlights: → In this work, a robust nonlinear model predictive control algorithm is developed. → This algorithm is applied to control the power level for load following. → The state constraints are imposed on the predicted trajectory during optimization. → The xenon oscillations are the main constraint for the load following problem. → In this algorithm, xenon oscillations are bounded within acceptable limits. - Abstract: One of the important operations in nuclear power plants is load-following in which imbalance of axial power distribution induces xenon oscillations. These oscillations must be maintained within acceptable limits otherwise the nuclear power plant could become unstable. Therefore, bounded xenon oscillation considered to be a constraint for the load-following operation. In this paper, a robust nonlinear model predictive control for the load-following operation problem is proposed that ensures xenon oscillations are kept bounded within acceptable limits. The proposed controller uses constant axial offset (AO) strategy to maintain xenon oscillations to be bounded. The constant AO is a robust state constraint for load-following problem. The controller imposes restricted state constraints on the predicted trajectory during optimization which guarantees robust satisfaction of state constraints without restoring to a min-max optimization problem. Simulation results show that the proposed controller for the load-following operation is so effective so that the xenon oscillations kept bounded in the given region.

  16. Fuzzy model predictive control algorithm applied in nuclear power plant

    International Nuclear Information System (INIS)

    Zuheir, Ahmad

    2006-01-01

    The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

  17. Fault Tolerant Control Using Gaussian Processes and Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Yang Xiaoke

    2015-03-01

    Full Text Available Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.

  18. Model Predictive Control with Constraints of a Wind Turbine

    DEFF Research Database (Denmark)

    Henriksen, Lars Christian; Poulsen, Niels Kjølstad

    2007-01-01

    Model predictive control of wind turbines offer a more systematic approach of constructing controllers that handle constraints while focusing on the main control objective. In this article several controllers are designed for different wind conditions and appropriate switching conditions ensure a...... an efficient control of the wind turbine over the entire range of wind speeds. Both onshore and floating offshore wind turbines are tested with the controllers.......Model predictive control of wind turbines offer a more systematic approach of constructing controllers that handle constraints while focusing on the main control objective. In this article several controllers are designed for different wind conditions and appropriate switching conditions ensure...

  19. Rainfall prediction of Cimanuk watershed regions with canonical correlation analysis (CCA)

    Science.gov (United States)

    Rustiana, Shailla; Nurani Ruchjana, Budi; Setiawan Abdullah, Atje; Hermawan, Eddy; Berliana Sipayung, Sinta; Gede Nyoman Mindra Jaya, I.; Krismianto

    2017-10-01

    Rainfall prediction in Indonesia is very influential on various development sectors, such as agriculture, fisheries, water resources, industry, and other sectors. The inaccurate predictions can lead to negative effects. Cimanuk watershed is one of the main pillar of water resources in West Java. This watersheds divided into three parts, which is a headwater of Cimanuk sub-watershed, Middle of Cimanuk sub-watershed and downstream of Cimanuk sub- watershed. The flow of this watershed will flow through the Jatigede reservoir and will supply water to the north-coast area in the next few years. So, the reliable model of rainfall prediction is very needed in this watershed. Rainfall prediction conducted with Canonical Correlation Analysis (CCA) method using Climate Predictability Tool (CPT) software. The prediction is every 3months on 2016 (after January) based on Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over West Java. Predictors used in CPT were the monthly data index of Nino3.4, Dipole Mode (DMI), and Monsoon Index (AUSMI-ISMI-WNPMI-WYMI) with initial condition January. The initial condition is chosen by the last data update. While, the predictant were monthly rainfall data CHIRPS region of West Java. The results of prediction rainfall showed by skill map from Pearson Correlation. High correlation of skill map are on MAM (Mar-Apr-May), AMJ (Apr-May-Jun), and JJA (Jun-Jul-Aug) which means the model is reliable to forecast rainfall distribution over Cimanuk watersheds region (over West Java) on those seasons. CCA score over those season prediction mostly over 0.7. The accuracy of the model CPT also indicated by the Relative Operating Characteristic (ROC) curve of the results of Pearson correlation 3 representative point of sub-watershed (Sumedang, Majalengka, and Cirebon), were mostly located in the top line of non-skill, and evidenced by the same of rainfall patterns between observation and forecast. So, the model of CPT with CCA method

  20. DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields

    Directory of Open Access Journals (Sweden)

    Sheng Wang

    2015-07-01

    Full Text Available Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields, to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors.

  1. Predicting redox conditions in groundwater at a regional scale

    Science.gov (United States)

    Tesoriero, Anthony J.; Terziotti, Silvia; Abrams, Daniel B.

    2015-01-01

    Defining the oxic-suboxic interface is often critical for determining pathways for nitrate transport in groundwater and to streams at the local scale. Defining this interface on a regional scale is complicated by the spatial variability of reaction rates. The probability of oxic groundwater in the Chesapeake Bay watershed was predicted by relating dissolved O2 concentrations in groundwater samples to indicators of residence time and/or electron donor availability using logistic regression. Variables that describe surficial geology, position in the flow system, and soil drainage were important predictors of oxic water. The probability of encountering oxic groundwater at a 30 m depth and the depth to the bottom of the oxic layer were predicted for the Chesapeake Bay watershed. The influence of depth to the bottom of the oxic layer on stream nitrate concentrations and time lags (i.e., time period between land application of nitrogen and its effect on streams) are illustrated using model simulations for hypothetical basins. Regional maps of the probability of oxic groundwater should prove useful as indicators of groundwater susceptibility and stream susceptibility to contaminant sources derived from groundwater.

  2. Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

    Science.gov (United States)

    Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

    2014-05-01

    This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Nonlinear predictive control in the LHC accelerator

    CERN Document Server

    Blanco, E; Cristea, S; Casas, J

    2009-01-01

    This paper describes the application of a nonlinear model-based control strategy in a real challenging process. A predictive controller based on a nonlinear model derived from physical relationships, mainly heat and mass balances, has been developed and commissioned in the inner triplet heat exchanger unit (IT-HXTU) of the large hadron collider (LHC) particle accelerator at European Center for Nuclear Research (CERN). The advanced regulation\\ maintains the magnets temperature at about 1.9 K. The development includes a constrained nonlinear state estimator with a receding horizon estimation procedure to improve the regulator predictions.

  4. Generalized predictive control in the delta-domain

    DEFF Research Database (Denmark)

    Lauritsen, Morten Bach; Jensen, Morten Rostgaard; Poulsen, Niels Kjølstad

    1995-01-01

    This paper describes new approaches to generalized predictive control formulated in the delta (δ) domain. A new δ-domain version of the continuous-time emulator-based predictor is presented. It produces the optimal estimate in the deterministic case whenever the predictor order is chosen greater...... than or equal to the number of future predicted samples, however a “good” estimate is usually obtained in a much longer range of samples. This is particularly advantageous at fast sampling rates where a “conventional” predictor is bound to become very computationally demanding. Two controllers...

  5. Genome-scale prediction of proteins with long intrinsically disordered regions.

    Science.gov (United States)

    Peng, Zhenling; Mizianty, Marcin J; Kurgan, Lukasz

    2014-01-01

    Proteins with long disordered regions (LDRs), defined as having 30 or more consecutive disordered residues, are abundant in eukaryotes, and these regions are recognized as a distinct class of biologically functional domains. LDRs facilitate various cellular functions and are important for target selection in structural genomics. Motivated by the lack of methods that directly predict proteins with LDRs, we designed Super-fast predictor of proteins with Long Intrinsically DisordERed regions (SLIDER). SLIDER utilizes logistic regression that takes an empirically chosen set of numerical features, which consider selected physicochemical properties of amino acids, sequence complexity, and amino acid composition, as its inputs. Empirical tests show that SLIDER offers competitive predictive performance combined with low computational cost. It outperforms, by at least a modest margin, a comprehensive set of modern disorder predictors (that can indirectly predict LDRs) and is 16 times faster compared to the best currently available disorder predictor. Utilizing our time-efficient predictor, we characterized abundance and functional roles of proteins with LDRs over 110 eukaryotic proteomes. Similar to related studies, we found that eukaryotes have many (on average 30.3%) proteins with LDRs with majority of proteomes having between 25 and 40%, where higher abundance is characteristic to proteomes that have larger proteins. Our first-of-its-kind large-scale functional analysis shows that these proteins are enriched in a number of cellular functions and processes including certain binding events, regulation of catalytic activities, cellular component organization, biogenesis, biological regulation, and some metabolic and developmental processes. A webserver that implements SLIDER is available at http://biomine.ece.ualberta.ca/SLIDER/. Copyright © 2013 Wiley Periodicals, Inc.

  6. Predictive models of glucose control: roles for glucose-sensing neurones

    Science.gov (United States)

    Kosse, C.; Gonzalez, A.; Burdakov, D.

    2018-01-01

    The brain can be viewed as a sophisticated control module for stabilizing blood glucose. A review of classical behavioural evidence indicates that central circuits add predictive (feedforward/anticipatory) control to the reactive (feedback/compensatory) control by peripheral organs. The brain/cephalic control is constructed and engaged, via associative learning, by sensory cues predicting energy intake or expenditure (e.g. sight, smell, taste, sound). This allows rapidly measurable sensory information (rather than slowly generated internal feedback signals, e.g. digested nutrients) to control food selection, glucose supply for fight-or-flight responses or preparedness for digestion/absorption. Predictive control is therefore useful for preventing large glucose fluctuations. We review emerging roles in predictive control of two classes of widely projecting hypothalamic neurones, orexin/hypocretin (ORX) and melanin-concentrating hormone (MCH) cells. Evidence is cited that ORX neurones (i) are activated by sensory cues (e.g. taste, sound), (ii) drive hepatic production, and muscle uptake, of glucose, via sympathetic nerves, (iii) stimulate wakefulness and exploration via global brain projections and (iv) are glucose-inhibited. MCH neurones are (i) glucose-excited, (ii) innervate learning and reward centres to promote synaptic plasticity, learning and memory and (iii) are critical for learning associations useful for predictive control (e.g. using taste to predict nutrient value of food). This evidence is unified into a model for predictive glucose control. During associative learning, inputs from some glucose-excited neurones may promote connections between the ‘fast’ senses and reward circuits, constructing neural shortcuts for efficient action selection. In turn, glucose-inhibited neurones may engage locomotion/exploration and coordinate the required fuel supply. Feedback inhibition of the latter neurones by glucose would ensure that glucose fluxes they

  7. Predictive models of glucose control: roles for glucose-sensing neurones.

    Science.gov (United States)

    Kosse, C; Gonzalez, A; Burdakov, D

    2015-01-01

    The brain can be viewed as a sophisticated control module for stabilizing blood glucose. A review of classical behavioural evidence indicates that central circuits add predictive (feedforward/anticipatory) control to the reactive (feedback/compensatory) control by peripheral organs. The brain/cephalic control is constructed and engaged, via associative learning, by sensory cues predicting energy intake or expenditure (e.g. sight, smell, taste, sound). This allows rapidly measurable sensory information (rather than slowly generated internal feedback signals, e.g. digested nutrients) to control food selection, glucose supply for fight-or-flight responses or preparedness for digestion/absorption. Predictive control is therefore useful for preventing large glucose fluctuations. We review emerging roles in predictive control of two classes of widely projecting hypothalamic neurones, orexin/hypocretin (ORX) and melanin-concentrating hormone (MCH) cells. Evidence is cited that ORX neurones (i) are activated by sensory cues (e.g. taste, sound), (ii) drive hepatic production, and muscle uptake, of glucose, via sympathetic nerves, (iii) stimulate wakefulness and exploration via global brain projections and (iv) are glucose-inhibited. MCH neurones are (i) glucose-excited, (ii) innervate learning and reward centres to promote synaptic plasticity, learning and memory and (iii) are critical for learning associations useful for predictive control (e.g. using taste to predict nutrient value of food). This evidence is unified into a model for predictive glucose control. During associative learning, inputs from some glucose-excited neurones may promote connections between the 'fast' senses and reward circuits, constructing neural shortcuts for efficient action selection. In turn, glucose-inhibited neurones may engage locomotion/exploration and coordinate the required fuel supply. Feedback inhibition of the latter neurones by glucose would ensure that glucose fluxes they stimulate

  8. Robust predictive control strategy applied for propofol dosing using BIS as a controlled variable during anesthesia

    NARCIS (Netherlands)

    Ionescu, Clara A.; De Keyser, Robin; Torrico, Bismark Claure; De Smet, Tom; Struys, Michel M. R. F.; Normey-Rico, Julio E.

    This paper presents the application of predictive control to drug dosing during anesthesia in patients undergoing surgery. The performance of a generic predictive control strategy in drug dosing control, with a previously reported anesthesia-specific control algorithm, has been evaluated. The

  9. 40 CFR 81.104 - Central Pennsylvania Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.104 Section 81.104 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.104 Central Pennsylvania Intrastate Air Quality Control Region. The Central Pennsylvania Intrastate Air Quality Control Region consists of the territorial area encompassed by...

  10. 40 CFR 81.43 - Metropolitan Toledo Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.43 Section 81.43 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.43 Metropolitan Toledo Interstate Air Quality Control Region. The Metropolitan Toledo Interstate Air Quality Control Region (Ohio-Michigan) consists of the territorial area...

  11. 40 CFR 81.31 - Metropolitan Providence Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.31 Section 81.31 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.31 Metropolitan Providence Interstate Air Quality Control Region. The Metropolitan Providence Interstate Air Quality Control Region (Rhode Island-Massachusetts) consists of the...

  12. 40 CFR 81.90 - Androscoggin Valley Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.90 Section 81.90 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.90 Androscoggin Valley Interstate Air Quality Control Region. The Androscoggin Valley Interstate Air Quality Control Region (Maine-New Hampshire) consists of the territorial...

  13. 40 CFR 81.78 - Metropolitan Portland Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.78 Section 81.78 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.78 Metropolitan Portland Intrastate Air Quality Control Region. The Metropolitan Portland Intrastate Air Quality Control Region (Maine) consists of the territorial area...

  14. 40 CFR 81.30 - Southeastern Wisconsin Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.30 Section 81.30 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.30 Southeastern Wisconsin Intrastate Air Quality Control Region. The Metropolitan Milwaukee Intrastate Air Quality Control Region (Wisconsin) has been renamed the Southeastern...

  15. 40 CFR 81.16 - Metropolitan Denver Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.16 Section 81.16 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.16 Metropolitan Denver Intrastate Air Quality Control Region. The Metropolitan Denver Intrastate Air Quality Control Region (Colorado) consists of the territorial area...

  16. 40 CFR 81.47 - Central Oklahoma Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.47 Section 81.47 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.47 Central Oklahoma Intrastate Air Quality Control Region. The Metropolitan Oklahoma Intrastate Air Quality Control Region has been renamed the Central Oklahoma Intrastate...

  17. 40 CFR 81.29 - Metropolitan Indianapolis Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Air Quality Control Region. 81.29 Section 81.29 Protection of Environment ENVIRONMENTAL PROTECTION... Designation of Air Quality Control Regions § 81.29 Metropolitan Indianapolis Intrastate Air Quality Control Region. The Metropolitan Indianapolis Intrastate Air Quality Control Region consists of the territorial...

  18. 40 CFR 81.101 - Metropolitan Dubuque Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.101 Section 81.101 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.101 Metropolitan Dubuque Interstate Air Quality Control Region. The Metropolitan Dubuque Interstate Air Quality Control Region (Illinois-Iowa-Wisconsin) consists of the...

  19. 40 CFR 81.79 - Northeastern Oklahoma Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.79 Section 81.79 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.79 Northeastern Oklahoma Intrastate Air Quality Control Region. The Metropolitan Tulsa Intrastate Air Quality Control Region has been renamed the Northeastern Oklahoma Intrastate...

  20. 40 CFR 81.24 - Niagara Frontier Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.24 Section 81.24 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.24 Niagara Frontier Intrastate Air Quality Control Region. The Niagara Frontier Intrastate Air Quality Control Region (New York) consists of the territorial area...

  1. 40 CFR 81.106 - Greenville-Spartanburg Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.106 Section 81.106 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.106 Greenville-Spartanburg Intrastate Air Quality Control Region. The Greenville-Spartanburg Intrastate Air Quality Control Region (South Carolina) consists of the territorial...

  2. 40 CFR 81.44 - Metropolitan Memphis Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.44 Section 81.44 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.44 Metropolitan Memphis Interstate Air Quality Control Region. The Metropolitan Memphis Interstate Air Quality Control Region (Arkansas-Mississippi-Tennessee) consists of the...

  3. 40 CFR 81.19 - Metropolitan Boston Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.19 Section 81.19 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.19 Metropolitan Boston Intrastate Air Quality Control Region. The Metropolitan Boston Intrastate Air Quality Control Region (Massachusetts) consists of the territorial area...

  4. 40 CFR 81.28 - Metropolitan Baltimore Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.28 Section 81.28 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.28 Metropolitan Baltimore Intrastate Air Quality Control Region. The Metropolitan Baltimore Intrastate Air Quality Control Region (Maryland) consists of the territorial area...

  5. 40 CFR 81.119 - Western Tennessee Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.119 Section 81.119 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.119 Western Tennessee Intrastate Air Quality Control Region. The Western Tennessee Intrastate Air Quality Control Region consists of the territorial area encompassed by...

  6. 40 CFR 81.89 - Metropolitan Cheyenne Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.89 Section 81.89 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.89 Metropolitan Cheyenne Intrastate Air Quality Control Region. The Metropolitan Cheyenne Intrastate Air Quality Control Region (Wyoming) consists of the territorial area...

  7. 40 CFR 81.87 - Metropolitan Boise Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.87 Section 81.87 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.87 Metropolitan Boise Intrastate Air Quality Control Region. The Metropolitan Boise Intrastate Air Quality Control Region (Idaho) consists of the territorial area encompassed...

  8. 40 CFR 81.23 - Southwest Pennsylvania Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.23 Section 81.23 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.23 Southwest Pennsylvania Intrastate Air Quality Control Region. The Southwest Pennsylvania Intrastate Air Quality Control Region is redesignated to consist of the territorial...

  9. 40 CFR 81.75 - Metropolitan Charlotte Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.75 Section 81.75 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.75 Metropolitan Charlotte Interstate Air Quality Control Region. The Metropolitan Charlotte Interstate Air Quality Control Region (North Carolina-South Carolina) has been revised...

  10. 40 CFR 81.120 - Middle Tennessee Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.120 Section 81.120 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.120 Middle Tennessee Intrastate Air Quality Control Region. The Middle Tennessee Intrastate Air Quality Control Region consists of the territorial area encompassed by...

  11. Cognitive control of drug craving inhibits brain reward regions in cocaine abusers

    International Nuclear Information System (INIS)

    Volkow, N.D.; Fowler, J.; Wang, G.J.; Telang, F.; Logan, J.; Jayne, M.; Ma, Y.; Pradhan, K.; Wong, C.T.; Swanson, J.M.

    2010-01-01

    Loss of control over drug taking is considered a hallmark of addiction and is critical in relapse. Dysfunction of frontal brain regions involved with inhibitory control may underlie this behavior. We evaluated whether addicted subjects when instructed to purposefully control their craving responses to drug-conditioned stimuli can inhibit limbic brain regions implicated in drug craving. We used PET and 2-deoxy-2[18F]fluoro-D-glucose to measure brain glucose metabolism (marker of brain function) in 24 cocaine abusers who watched a cocaine-cue video and compared brain activation with and without instructions to cognitively inhibit craving. A third scan was obtained at baseline (without video). Statistical parametric mapping was used for analysis and corroborated with regions of interest. The cocaine-cue video increased craving during the no-inhibition condition (pre 3 ± 3, post 6 ± 3; p < 0.001) but not when subjects were instructed to inhibit craving (pre 3 ± 2, post 3 ± 3). Comparisons with baseline showed visual activation for both cocaine-cue conditions and limbic inhibition (accumbens, orbitofrontal, insula, cingulate) when subjects purposefully inhibited craving (p < 0.001). Comparison between cocaine-cue conditions showed lower metabolism with cognitive inhibition in right orbitofrontal cortex and right accumbens (p < 0.005), which was associated with right inferior frontal activation (r = -0.62, p < 0.005). Decreases in metabolism in brain regions that process the predictive (nucleus accumbens) and motivational value (orbitofrontal cortex) of drug-conditioned stimuli were elicited by instruction to inhibit cue-induced craving. This suggests that cocaine abusers may retain some ability to inhibit craving and that strengthening fronto-accumbal regulation may be therapeutically beneficial in addiction.

  12. Cognitive control of drug craving inhibits brain reward regions in cocaine abusers

    Energy Technology Data Exchange (ETDEWEB)

    Volkow, N.D.; Fowler, J.; Wang, G.J.; Telang, F.; Logan, J.; Jayne, M.; Ma, Y.; Pradhan, K.; Wong, C.T.; Swanson, J.M.

    2010-01-01

    Loss of control over drug taking is considered a hallmark of addiction and is critical in relapse. Dysfunction of frontal brain regions involved with inhibitory control may underlie this behavior. We evaluated whether addicted subjects when instructed to purposefully control their craving responses to drug-conditioned stimuli can inhibit limbic brain regions implicated in drug craving. We used PET and 2-deoxy-2[18F]fluoro-D-glucose to measure brain glucose metabolism (marker of brain function) in 24 cocaine abusers who watched a cocaine-cue video and compared brain activation with and without instructions to cognitively inhibit craving. A third scan was obtained at baseline (without video). Statistical parametric mapping was used for analysis and corroborated with regions of interest. The cocaine-cue video increased craving during the no-inhibition condition (pre 3 {+-} 3, post 6 {+-} 3; p < 0.001) but not when subjects were instructed to inhibit craving (pre 3 {+-} 2, post 3 {+-} 3). Comparisons with baseline showed visual activation for both cocaine-cue conditions and limbic inhibition (accumbens, orbitofrontal, insula, cingulate) when subjects purposefully inhibited craving (p < 0.001). Comparison between cocaine-cue conditions showed lower metabolism with cognitive inhibition in right orbitofrontal cortex and right accumbens (p < 0.005), which was associated with right inferior frontal activation (r = -0.62, p < 0.005). Decreases in metabolism in brain regions that process the predictive (nucleus accumbens) and motivational value (orbitofrontal cortex) of drug-conditioned stimuli were elicited by instruction to inhibit cue-induced craving. This suggests that cocaine abusers may retain some ability to inhibit craving and that strengthening fronto-accumbal regulation may be therapeutically beneficial in addiction.

  13. Power Admission Control with Predictive Thermal Management in Smart Buildings

    DEFF Research Database (Denmark)

    Yao, Jianguo; Costanzo, Giuseppe Tommaso; Zhu, Guchuan

    2015-01-01

    This paper presents a control scheme for thermal management in smart buildings based on predictive power admission control. This approach combines model predictive control with budget-schedulability analysis in order to reduce peak power consumption as well as ensure thermal comfort. First...

  14. Optimization control of LNG regasification plant using Model Predictive Control

    Science.gov (United States)

    Wahid, A.; Adicandra, F. F.

    2018-03-01

    Optimization of liquified natural gas (LNG) regasification plant is important to minimize costs, especially operational costs. Therefore, it is important to choose optimum LNG regasification plant design and maintaining the optimum operating conditions through the implementation of model predictive control (MPC). Optimal tuning parameter for MPC such as P (prediction horizon), M (control of the horizon) and T (sampling time) are achieved by using fine-tuning method. The optimal criterion for design is the minimum amount of energy used and for control is integral of square error (ISE). As a result, the optimum design is scheme 2 which is developed by Devold with an energy savings of 40%. To maintain the optimum conditions, required MPC with P, M and T as follows: tank storage pressure: 90, 2, 1; product pressure: 95, 2, 1; temperature vaporizer: 65, 2, 2; and temperature heater: 35, 6, 5, with ISE value at set point tracking respectively 0.99, 1792.78, 34.89 and 7.54, or improvement of control performance respectively 4.6%, 63.5%, 3.1% and 58.2% compared to PI controller performance. The energy savings that MPC controllers can make when there is a disturbance in temperature rise 1°C of sea water is 0.02 MW.

  15. 40 CFR 81.117 - Southeast Missouri Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.117 Section 81.117 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.117 Southeast Missouri Intrastate Air Quality Control Region. The Southeast Missouri Intrastate Air Quality Control Region consists of the territorial area encompassed by the...

  16. 40 CFR 81.45 - Metropolitan Atlanta Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.45 Section 81.45 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.45 Metropolitan Atlanta Intrastate Air Quality Control Region. The Metropolitan Atlanta Intrastate Air Quality Control Region (Georgia) has been revised to consist of the...

  17. 40 CFR 81.123 - Southeastern Oklahoma Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.123 Section 81.123 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.123 Southeastern Oklahoma Intrastate Air Quality Control Region. The Southeastern Oklahoma Intrastate Air Quality Control Region consists of the territorial area encompassed by the...

  18. 40 CFR 81.98 - Burlington-Keokuk Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.98 Section 81.98 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.98 Burlington-Keokuk Interstate Air Quality Control Region. The Burlington-Keokuk Interstate Air Quality Control Region (Illinois-Iowa) is revised to consist of the...

  19. 40 CFR 81.49 - Southeast Florida Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.49 Section 81.49 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.49 Southeast Florida Intrastate Air Quality Control Region. The Southeast Florida Intrastate Air Quality Control Region is redesignated to consist of the territorial area...

  20. 40 CFR 81.59 - Cumberland-Keyser Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.59 Section 81.59 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.59 Cumberland-Keyser Interstate Air Quality Control Region. The Cumberland-Keyser Interstate Air Quality Control Region (Maryland-West Virginia) has been revised to consist...

  1. 40 CFR 81.20 - Metropolitan Cincinnati Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.20 Section 81.20 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.20 Metropolitan Cincinnati Interstate Air Quality Control Region. The Metropolitan Cincinnati Interstate Air Quality Control Region (Ohio-Kentucky-Indiana) is revised to consist of...

  2. 40 CFR 81.97 - Southwest Florida Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.97 Section 81.97 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.97 Southwest Florida Intrastate Air Quality Control Region. The Southwest Florida Intrastate Air Quality Control Region consists of the territorial area encompassed by the...

  3. 40 CFR 81.116 - Northern Missouri Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.116 Section 81.116 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.116 Northern Missouri Intrastate Air Quality Control Region. The Northern Missouri Intrastate Air Quality Control Region consists of the territorial area encompassed by the...

  4. 40 CFR 81.67 - Lake Michigan Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.67 Lake Michigan Intrastate Air Quality Control Region. The Menominee-Escanaba (Michigan)-Marinette (Wisconsin) Interstate Air Quality Control Region has been renamed the Lake Michigan Intrastate Air Quality Control Region (Wisconsin) and revised to consist of the territorial area...

  5. 40 CFR 81.34 - Metropolitan Dayton Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.34 Section 81.34 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.34 Metropolitan Dayton Intrastate Air Quality Control Region. The Metropolitan Dayton Intrastate Air Quality Control Region consists of the territorial area encompassed by the...

  6. 40 CFR 81.115 - Northwest Nevada Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.115 Section 81.115 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.115 Northwest Nevada Intrastate Air Quality Control Region. The Northwest Nevada Intrastate Air Quality Control Region consists of the territorial area encompassed by the...

  7. 40 CFR 81.41 - Metropolitan Birmingham Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.41 Section 81.41 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.41 Metropolitan Birmingham Intrastate Air Quality Control Region. The Metropolitan Birmingham Intrastate Air Quality Control Region (Alabama) has been revised to consist of the...

  8. 40 CFR 81.14 - Metropolitan Chicago Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.14 Section 81.14 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.14 Metropolitan Chicago Interstate Air Quality Control Region. The Metropolitan Chicago Interstate Air Quality Control Region (Illinois-Indiana) is revised to consist of the...

  9. 40 CFR 81.118 - Southwest Missouri Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.118 Section 81.118 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.118 Southwest Missouri Intrastate Air Quality Control Region. The Southwest Missouri Intrastate Air Quality Control Region consists of the territorial area encompassed by the...

  10. 40 CFR 81.122 - Mississippi Delta Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.122 Section 81.122 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.122 Mississippi Delta Intrastate Air Quality Control Region. The Mississippi Delta Intrastate Air Quality Control Region consists of the territorial area encompassed by the...

  11. High level model predictive control for plug-and-play process control with stability guaranty

    DEFF Research Database (Denmark)

    Michelsen, Axel Gottlieb; Stoustrup, Jakob

    2010-01-01

    In this paper a method for designing a stabilizing high level model predictive controller for a hierarchical plug- and-play process is presented. This is achieved by abstracting the lower layers of the controller structure as low order models with uncertainty and by using a robust model predictive...... controller for generating the references for these. A simulation example, in which the actuators in a process control system are changed, is reported to show the potential of this approach for plug and play process control....

  12. Fuzzy predictive filtering in nonlinear economic model predictive control for demand response

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.

    2016-01-01

    problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...

  13. Control of the low-load region in partially premixed combustion

    Science.gov (United States)

    Ingesson, Gabriel; Yin, Lianhao; Johansson, Rolf; Tunestal, Per

    2016-09-01

    Partially premixed combustion (PPC) is a low temperature, direct-injection combustion concept that has shown to give promising emission levels and efficiencies over a wide operating range. In this concept, high EGR ratios, high octane-number fuels and early injection timings are used to slow down the auto-ignition reactions and to enhance the fuel and are mixing before the start of combustion. A drawback with this concept is the combustion stability in the low-load region where a high octane-number fuel might cause misfire and low combustion efficiency. This paper investigates the problem of low-load PPC controller design for increased engine efficiency. First, low-load PPC data, obtained from a multi-cylinder heavy- duty engine is presented. The data shows that combustion efficiency could be increased by using a pilot injection and that there is a non-linearity in the relation between injection and combustion timing. Furthermore, intake conditions should be set in order to avoid operating points with unfavourable global equivalence ratio and in-cylinder temperature combinations. Model predictive control simulations were used together with a calibrated engine model to find a gas-system controller that fulfilled this task. The findings are then summarized in a suggested engine controller design. Finally, an experimental performance evaluation of the suggested controller is presented.

  14. Predicting domestic and community violence by soldiers living in a conflict region.

    Science.gov (United States)

    Nandi, Corina; Elbert, Thomas; Bambonye, Manassé; Weierstall, Roland; Reichert, Manfred; Zeller, Anja; Crombach, Anselm

    2017-11-01

    Past research revealed war trauma and posttraumatic stress disorder (PTSD) symptoms as potential predictors for domestic and community violence in crisis regions and among soldiers in different armed conflicts. The impact of family violence and other adversities experienced in childhood as well as of a combat-enhanced appeal for aggressive behavior (appetitive aggression) remains to be specified. In the present study, the authors separately predicted violence against children, intimate partner violence and community violence in 381 Burundian soldiers returning from foreign deployment and living in a post- conflict region. Using path analysis, they aimed to disentangle the independent contributions and pathways of the following variables: Exposure to war trauma and childhood familial violence, PTSD and depression symptom severity, and appetitive aggression. Childhood familial violence had an independent effect on all contexts of violence and was the only significant predictor for violence against the soldiers' own children. Intimate partner violence was additionally predicted by depression symptom severity, while community violence was additionally predicted by PTSD symptom severity and appetitive aggression. Besides war-related mental ill-health and appetitive aggression, violent experiences during childhood development must not be overlooked as a factor fueling the cycle of violence in conflict regions. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  15. 40 CFR 81.48 - Champlain Valley Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.48 Section 81.48 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.48 Champlain Valley Interstate Air Quality Control Region. The Champlain Valley Interstate Air Quality Control Region (Vermont-New York) has been revised to consist of the...

  16. 40 CFR 81.111 - Georgetown Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.111 Georgetown Intrastate Air Quality Control Region. The Georgetown Intrastate Air Quality Control Region (South Carolina) consists of the territorial area encompassed by the... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Georgetown Intrastate Air Quality...

  17. 40 CFR 81.107 - Greenwood Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.107 Greenwood Intrastate Air Quality Control Region. The Greenwood Intrastate Air Quality Control Region (South Carolina) consists of the territorial area encompassed by the... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Greenwood Intrastate Air Quality...

  18. 40 CFR 81.108 - Columbia Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.108 Columbia Intrastate Air Quality Control Region. The Columbia Intrastate Air Quality Control Region (South Carolina) consists of the territorial area encompassed by the... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Columbia Intrastate Air Quality...

  19. 40 CFR 81.109 - Florence Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.109 Florence Intrastate Air Quality Control Region. The Florence Intrastate Air Quality Control Region (South Carolina) consists of the territorial area encompassed by the... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Florence Intrastate Air Quality...

  20. 40 CFR 81.35 - Louisville Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.35 Louisville Interstate Air Quality Control Region. The Louisville Interstate Air Quality Control Region (Kentucky-Indiana) consists of the territorial area encompassed by the... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Louisville Interstate Air Quality...

  1. Transcription-factor occupancy at HOT regions quantitatively predicts RNA polymerase recruitment in five human cell lines.

    KAUST Repository

    Foley, Joseph W; Sidow, Arend

    2013-01-01

    BACKGROUND: High-occupancy target (HOT) regions are compact genome loci occupied by many different transcription factors (TFs). HOT regions were initially defined in invertebrate model organisms, and we here show that they are a ubiquitous feature of the human gene-regulation landscape. RESULTS: We identified HOT regions by a comprehensive analysis of ChIP-seq data from 96 DNA-associated proteins in 5 human cell lines. Most HOT regions co-localize with RNA polymerase II binding sites, but many are not near the promoters of annotated genes. At HOT promoters, TF occupancy is strongly predictive of transcription preinitiation complex recruitment and moderately predictive of initiating Pol II recruitment, but only weakly predictive of elongating Pol II and RNA transcript abundance. TF occupancy varies quantitatively within human HOT regions; we used this variation to discover novel associations between TFs. The sequence motif associated with any given TF's direct DNA binding is somewhat predictive of its empirical occupancy, but a great deal of occupancy occurs at sites without the TF's motif, implying indirect recruitment by another TF whose motif is present. CONCLUSIONS: Mammalian HOT regions are regulatory hubs that integrate the signals from diverse regulatory pathways to quantitatively tune the promoter for RNA polymerase II recruitment.

  2. Transcription-factor occupancy at HOT regions quantitatively predicts RNA polymerase recruitment in five human cell lines.

    KAUST Repository

    Foley, Joseph W

    2013-10-20

    BACKGROUND: High-occupancy target (HOT) regions are compact genome loci occupied by many different transcription factors (TFs). HOT regions were initially defined in invertebrate model organisms, and we here show that they are a ubiquitous feature of the human gene-regulation landscape. RESULTS: We identified HOT regions by a comprehensive analysis of ChIP-seq data from 96 DNA-associated proteins in 5 human cell lines. Most HOT regions co-localize with RNA polymerase II binding sites, but many are not near the promoters of annotated genes. At HOT promoters, TF occupancy is strongly predictive of transcription preinitiation complex recruitment and moderately predictive of initiating Pol II recruitment, but only weakly predictive of elongating Pol II and RNA transcript abundance. TF occupancy varies quantitatively within human HOT regions; we used this variation to discover novel associations between TFs. The sequence motif associated with any given TF\\'s direct DNA binding is somewhat predictive of its empirical occupancy, but a great deal of occupancy occurs at sites without the TF\\'s motif, implying indirect recruitment by another TF whose motif is present. CONCLUSIONS: Mammalian HOT regions are regulatory hubs that integrate the signals from diverse regulatory pathways to quantitatively tune the promoter for RNA polymerase II recruitment.

  3. Implementation of neural network based non-linear predictive control

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1999-01-01

    This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...... on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system....

  4. Toward Process-resolving Synthesis and Prediction of Arctic Climate Change Using the Regional Arctic System Model

    Science.gov (United States)

    Maslowski, W.

    2017-12-01

    The Regional Arctic System Model (RASM) has been developed to better understand the operation of Arctic System at process scale and to improve prediction of its change at a spectrum of time scales. RASM is a pan-Arctic, fully coupled ice-ocean-atmosphere-land model with marine biogeochemistry extension to the ocean and sea ice models. The main goal of our research is to advance a system-level understanding of critical processes and feedbacks in the Arctic and their links with the Earth System. The secondary, an equally important objective, is to identify model needs for new or additional observations to better understand such processes and to help constrain models. Finally, RASM has been used to produce sea ice forecasts for September 2016 and 2017, in contribution to the Sea Ice Outlook of the Sea Ice Prediction Network. Future RASM forecasts, are likely to include increased resolution for model components and ecosystem predictions. Such research is in direct support of the US environmental assessment and prediction needs, including those of the U.S. Navy, Department of Defense, and the recent IARPC Arctic Research Plan 2017-2021. In addition to an overview of RASM technical details, selected model results are presented from a hierarchy of climate models together with available observations in the region to better understand potential oceanic contributions to polar amplification. RASM simulations are analyzed to evaluate model skill in representing seasonal climatology as well as interannual and multi-decadal climate variability and predictions. Selected physical processes and resulting feedbacks are discussed to emphasize the need for fully coupled climate model simulations, high model resolution and sensitivity of simulated sea ice states to scale dependent model parameterizations controlling ice dynamics, thermodynamics and coupling with the atmosphere and ocean.

  5. Nonparametric predictive inference in statistical process control

    NARCIS (Netherlands)

    Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.

    2000-01-01

    New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on

  6. 40 CFR 81.42 - Chattanooga Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.42 Chattanooga Interstate Air Quality Control Region. The Chattanooga Interstate Air Quality Control Region (Georgia-Tennessee) has been revised to consist of the territorial area... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Chattanooga Interstate Air Quality...

  7. Small Body GN&C Research Report: A Robust Model Predictive Control Algorithm with Guaranteed Resolvability

    Science.gov (United States)

    Acikmese, Behcet A.; Carson, John M., III

    2005-01-01

    A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees the resolvability of the associated finite-horizon optimal control problem in a receding-horizon implementation. The control consists of two components; (i) feedforward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives, and derivatives in polytopes. An illustrative numerical example is also provided.

  8. Study of Model Predictive Control for Path-Following Autonomous Ground Vehicle Control under Crosswind Effect

    Directory of Open Access Journals (Sweden)

    Fitri Yakub

    2016-01-01

    Full Text Available We present a comparative study of model predictive control approaches of two-wheel steering, four-wheel steering, and a combination of two-wheel steering with direct yaw moment control manoeuvres for path-following control in autonomous car vehicle dynamics systems. Single-track mode, based on a linearized vehicle and tire model, is used. Based on a given trajectory, we drove the vehicle at low and high forward speeds and on low and high road friction surfaces for a double-lane change scenario in order to follow the desired trajectory as close as possible while rejecting the effects of wind gusts. We compared the controller based on both simple and complex bicycle models without and with the roll vehicle dynamics for different types of model predictive control manoeuvres. The simulation result showed that the model predictive control gave a better performance in terms of robustness for both forward speeds and road surface variation in autonomous path-following control. It also demonstrated that model predictive control is useful to maintain vehicle stability along the desired path and has an ability to eliminate the crosswind effect.

  9. When predictions take control: The effect of task predictions on task switching performance

    Directory of Open Access Journals (Sweden)

    Wout eDuthoo

    2012-08-01

    Full Text Available In this paper, we aimed to investigate the role of self-generated predictions in the flexible control of behaviour. Therefore, we ran a task switching experiment in which participants were asked to try to predict the upcoming task in three conditions varying in switch rate (30%, 50% and 70%. Irrespective of their predictions, the colour of the target indicated which task participants had to perform. In line with previous studies (Mayr, 2006; Monsell & Mizon, 2006, the switch cost was attenuated as the switch rate increased. Importantly, a clear task repetition bias was found in all conditions, yet the task repetition prediction rate dropped from 78% over 66% to 49% with increasing switch probability in the three conditions. Irrespective of condition, the switch cost was strongly reduced in expectation of a task alternation compared to the cost of an unexpected task alternation following repetition predictions. Hence, our data suggest that the reduction in the switch cost with increasing switch probability is caused by a diminished expectancy for the task to repeat. Taken together, this paper highlights the importance of predictions in the flexible control of behaviour, and suggests a crucial role for task repetition expectancy in the context-sensitive adjusting of task switching performance.

  10. Expected packing density allows prediction of both amyloidogenic and disordered regions in protein chains

    Energy Technology Data Exchange (ETDEWEB)

    Galzitskaya, Oxana V; Garbuzynskiy, Sergiy O; Lobanov, Michail Yu [Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region (Russian Federation)

    2007-07-18

    The determination of factors that influence conformational changes in proteins is very important for the identification of potentially amyloidogenic and disordered regions in polypeptide chains. In our work we introduce a new parameter, mean packing density, to detect both amyloidogenic and disordered regions in a protein sequence. It has been shown that regions with strong expected packing density are responsible for amyloid formation. Our predictions are consistent with known disease-related amyloidogenic regions for 9 of 12 amyloid-forming proteins and peptides in which the positions of amyloidogenic regions have been revealed experimentally. Our findings support the concept that the mechanism of formation of amyloid fibrils is similar for different peptides and proteins. Moreover, we have demonstrated that regions with weak expected packing density are responsible for the appearance of disordered regions. Our method has been tested on datasets of globular proteins and long disordered protein segments, and it shows improved performance over other widely used methods. Thus, we demonstrate that the expected packing density is a useful value for predicting both disordered and amyloidogenic regions of a protein based on sequence alone. Our results are important for understanding the structural characteristics of protein folding and misfolding.

  11. Just-In-Time predictive control for a two-wheeled robot

    OpenAIRE

    Nakpong, Nuttapun; Yamamoto, Shigeru

    2012-01-01

    In this paper, we introduce the use of Just-In-Time predictive control to enhance the stability of a two-wheeled robot. Just-In-Time predictive control uses a database which includes a huge amounts of input-output data of the two-wheeled robot and predicts its future movements based on a Just-In-Time algorithm. © 2012 IEEE.

  12. A Robust Practical Generalized Predictive Control for BoilerSuper Heater Temperature Control

    OpenAIRE

    Zaki Maki Mohialdeen

    2015-01-01

    A practical method of robust generalized predictive controller (GPC) application is developed using a combination of Ziegler-Nichols type functions relating the GPC controller parameters to a first order with time delay process parameters and a model matching controller. The GPC controller and the model matching controller are used in a master/slave configuration, with the GPC as the master controller and the model matching controller as the slave controlle...

  13. Model Predictive Controller Combined with LQG Controller and Velocity Feedback to Control the Stewart Platform

    DEFF Research Database (Denmark)

    Nadimi, Esmaeil Sharak; Bak, Thomas; Izadi-Zamanabadi, Roozbeh

    2006-01-01

    The main objective of this paper is to investigate the erformance and applicability of two GPC (generalized predictive control) based control methods on a complete benchmark model of the Stewart platform made in MATLAB V6.5. The first method involves an LQG controller (Linear Quadratic Gaussian...

  14. Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

    Science.gov (United States)

    Maljaars, E.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J. M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A. A.; Vu, N. M. T.; The EUROfusion MST1-team; The TCV-team

    2017-12-01

    The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.

  15. Force Control for a Pneumatic Cylinder Using Generalized Predictive Controller Approach

    OpenAIRE

    Mohd Faudzi, Ahmad ’Athif; Mustafa, Nu’man Din; Osman, Khairuddin

    2014-01-01

    Pneumatic cylinder is a well-known device because of its high power to weight ratio, easy use, and environmental safety. Pneumatic cylinder uses air as its power source and converts it to a possible movement such as linear and rotary movement. In order to control the pneumatic cylinder, controller algorithm is needed to control the on-off solenoid valve with encoder and pressure sensor as the feedback inputs. In this paper, generalized predictive controller (GPC) is proposed as the control st...

  16. Saturated properties prediction in critical region by a quartic equation of state

    Directory of Open Access Journals (Sweden)

    Yong Wang

    2011-08-01

    Full Text Available A diverse substance library containing extensive PVT data for 77 pure components was used to critically evaluate the performance of a quartic equation of state and other four famous cubic equations of state in critical region. The quartic EOS studied in this work was found to significantly superior to the others in both vapor pressure prediction and saturated volume prediction in vicinity of critical point.

  17. General predictive control using the delta operator

    DEFF Research Database (Denmark)

    Jensen, Morten Rostgaard; Poulsen, Niels Kjølstad; Ravn, Ole

    1993-01-01

    This paper deals with two-discrete-time operators, the conventional forward shift-operator and the δ-operator. Both operators are treated in view of construction of suitable solutions to the Diophantine equation for the purpose of prediction. A general step-recursive scheme is presented. Finally...... a general predictive control (GPC) is formulated and applied adaptively to a continuous-time plant...

  18. Model predictive control for Z-source power converter

    DEFF Research Database (Denmark)

    Mo, W.; Loh, P.C.; Blaabjerg, Frede

    2011-01-01

    This paper presents Model Predictive Control (MPC) of impedance-source (commonly known as Z-source) power converter. Output voltage control and current control for Z-source inverter are analyzed and simulated. With MPC's ability of multi- system variables regulation, load current and voltage...

  19. Predictive control and identification: Applications to steering dynamics

    DEFF Research Database (Denmark)

    Hansen, Anca Daniela

    1996-01-01

    and of the loss function, which defines the optimality of the control. Some guidelines on how to choose the design parameters, depending on the type of process to be controlled and on the required control performance, are presented. A predictive track keeping system for a Mariner Class Vessel is formulated based...... the under- standing of the connection between identification and control, analysed in Chapter 7. Chapter 7 focuses on how to make the on-line identification for predictive control more robust towards unmodelled dynamics. The theory is verified via simulation studies on a Mariner Class Vessel. The effects...... and the need of a prefilter in the estimation are analysed and illustrated. Based on the idea that the control criterion must be dual to the estimation criterion, an iterative optimal prefilter is designed. This seems to be an appealing way to tune the model towards the objective for which the model...

  20. Catalytic cracking models developed for predictive control purposes

    Directory of Open Access Journals (Sweden)

    Dag Ljungqvist

    1993-04-01

    Full Text Available The paper deals with state-space modeling issues in the context of model-predictive control, with application to catalytic cracking. Emphasis is placed on model establishment, verification and online adjustment. Both the Fluid Catalytic Cracking (FCC and the Residual Catalytic Cracking (RCC units are discussed. Catalytic cracking units involve complex interactive processes which are difficult to operate and control in an economically optimal way. The strong nonlinearities of the FCC process mean that the control calculation should be based on a nonlinear model with the relevant constraints included. However, the model can be simple compared to the complexity of the catalytic cracking plant. Model validity is ensured by a robust online model adjustment strategy. Model-predictive control schemes based on linear convolution models have been successfully applied to the supervisory dynamic control of catalytic cracking units, and the control can be further improved by the SSPC scheme.

  1. Economic Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus

    Model Predictive Control (MPC) can be used to control the energy distribution in a Smart Grid with a high share of stochastic energy production from renewable energy sources like wind. Heat pumps for heating residential buildings can exploit the slow heat dynamics of a building to store heat and ...... and hereby shift the heat pump power consumption to periods with both low electricity prices and a high fraction of green energy in the grid.......Model Predictive Control (MPC) can be used to control the energy distribution in a Smart Grid with a high share of stochastic energy production from renewable energy sources like wind. Heat pumps for heating residential buildings can exploit the slow heat dynamics of a building to store heat...

  2. Multimodel Predictive Control Approach for UAV Formation Flight

    Directory of Open Access Journals (Sweden)

    Chang-jian Ru

    2014-01-01

    Full Text Available Formation flight problem is the most important and interesting problem of multiple UAVs (unmanned aerial vehicles cooperative control. In this paper, a novel approach for UAV formation flight based on multimodel predictive control is designed. Firstly, the state equation of relative motion is obtained and then discretized. By the geometrical method, the characteristic points of state are determined. Afterwards, based on the linearization technique, the standard linear discrete model is obtained at each characteristic state point. Then, weighted model set is proposed using the idea of T-S (Takagi-Sugeno fuzzy control and the predictive control is carried out based on the multimodel method. Finally, to verify the performance of the proposed method, two different simulation scenarios are performed.

  3. From field to region yield predictions in response to pedo-climatic variations in Eastern Canada

    Science.gov (United States)

    JÉGO, G.; Pattey, E.; Liu, J.

    2013-12-01

    to improve regional crop modelling. Although several aspects of the coupling between crop models and RS data have been investigated (e.g., inversion of radiative transfer models and neural networks), extensive verification using multiple years and sites is required prior to implementation of the RS assimilation at the regional scale, in order to evaluate the model robustness and consistency in response to climatic variations. STICS performance was evaluated over several years for spring wheat, soybean and corn yield predictions over two small rainfed agricultural region of Eastern Canada exhibiting a high degree of pedodiversity when LAI derived from EO data was assimilated. Averaged yield predictions of the three crops compared well with measurements (10%controlling N budget, use high-spatial-resolution gridded weather data as input data, and updated soil databases using EO.

  4. Bilingualism alters brain functional connectivity between "control" regions and "language" regions: Evidence from bimodal bilinguals.

    Science.gov (United States)

    Li, Le; Abutalebi, Jubin; Zou, Lijuan; Yan, Xin; Liu, Lanfang; Feng, Xiaoxia; Wang, Ruiming; Guo, Taomei; Ding, Guosheng

    2015-05-01

    Previous neuroimaging studies have revealed that bilingualism induces both structural and functional neuroplasticity in the dorsal anterior cingulate cortex (dACC) and the left caudate nucleus (LCN), both of which are associated with cognitive control. Since these "control" regions should work together with other language regions during language processing, we hypothesized that bilingualism may also alter the functional interaction between the dACC/LCN and language regions. Here we tested this hypothesis by exploring the functional connectivity (FC) in bimodal bilinguals and monolinguals using functional MRI when they either performed a picture naming task with spoken language or were in resting state. We found that for bimodal bilinguals who use spoken and sign languages, the FC of the dACC with regions involved in spoken language (e.g. the left superior temporal gyrus) was stronger in performing the task, but weaker in the resting state as compared to monolinguals. For the LCN, its intrinsic FC with sign language regions including the left inferior temporo-occipital part and right inferior and superior parietal lobules was increased in the bilinguals. These results demonstrate that bilingual experience may alter the brain functional interaction between "control" regions and "language" regions. For different control regions, the FC alters in different ways. The findings also deepen our understanding of the functional roles of the dACC and LCN in language processing. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars

    2017-01-01

    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  6. Predicted Habitat Suitability for Montipora Corals in the Au'au Channel Region

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This raster denotes predicted habitat suitability for Montipora in the Au'au Channel region. Maximum Entropy (MaxEnt) modeling software was used to create this...

  7. Predicted Habitat Suitability for Leptoseris Corals in the Au'au Channel Region

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This raster denotes predicted habitat suitability for Leptoseris in the Au'au Channel region. Maximum Entropy (MaxEnt) modeling software was used to create this...

  8. Model complexity control for hydrologic prediction

    NARCIS (Netherlands)

    Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

    2008-01-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

  9. Adjusting the Stems Regional Forest Growth Model to Improve Local Predictions

    Science.gov (United States)

    W. Brad Smith

    1983-01-01

    A simple procedure using double sampling is described for adjusting growth in the STEMS regional forest growth model to compensate for subregional variations. Predictive accuracy of the STEMS model (a distance-independent, individual tree growth model for Lake States forests) was improved by using this procedure

  10. Nonparametric predictive inference in statistical process control

    NARCIS (Netherlands)

    Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.

    2004-01-01

    Statistical process control (SPC) is used to decide when to stop a process as confidence in the quality of the next item(s) is low. Information to specify a parametric model is not always available, and as SPC is of a predictive nature, we present a control chart developed using nonparametric

  11. A regional neural network model for predicting mean daily river water temperature

    Science.gov (United States)

    Wagner, Tyler; DeWeber, Jefferson Tyrell

    2014-01-01

    Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May–October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate

  12. Generalized Predictive Control for Non-Stationary Systems

    DEFF Research Database (Denmark)

    Palsson, Olafur Petur; Madsen, Henrik; Søgaard, Henning Tangen

    1994-01-01

    This paper shows how the generalized predictive control (GPC) can be extended to non-stationary (time-varying) systems. If the time-variation is slow, then the classical GPC can be used in context with an adaptive estimation procedure of a time-invariant ARIMAX model. However, in this paper prior...... knowledge concerning the nature of the parameter variations is assumed available. The GPC is based on the assumption that the prediction of the system output can be expressed as a linear combination of present and future controls. Since the Diophantine equation cannot be used due to the time......-variation of the parameters, the optimal prediction is found as the general conditional expectation of the system output. The underlying model is of an ARMAX-type instead of an ARIMAX-type as in the original version of the GPC (Clarke, D. W., C. Mohtadi and P. S. Tuffs (1987). Automatica, 23, 137-148) and almost all later...

  13. Finite Control Set Model Predictive Control for Multiple Distributed Generators Microgrids

    Science.gov (United States)

    Babqi, Abdulrahman Jamal

    This dissertation proposes two control strategies for AC microgrids that consist of multiple distributed generators (DGs). The control strategies are valid for both grid-connected and islanded modes of operation. In general, microgrid can operate as a stand-alone system (i.e., islanded mode) or while it is connected to the utility grid (i.e., grid connected mode). To enhance the performance of a micrgorid, a sophisticated control scheme should be employed. The control strategies of microgrids can be divided into primary and secondary controls. The primary control regulates the output active and reactive powers of each DG in grid-connected mode as well as the output voltage and frequency of each DG in islanded mode. The secondary control is responsible for regulating the microgrid voltage and frequency in the islanded mode. Moreover, it provides power sharing schemes among the DGs. In other words, the secondary control specifies the set points (i.e. reference values) for the primary controllers. In this dissertation, Finite Control Set Model Predictive Control (FCS-MPC) was proposed for controlling microgrids. FCS-MPC was used as the primary controller to regulate the output power of each DG (in the grid-connected mode) or the voltage of the point of DG coupling (in the islanded mode of operation). In the grid-connected mode, Direct Power Model Predictive Control (DPMPC) was implemented to manage the power flow between each DG and the utility grid. In the islanded mode, Voltage Model Predictive Control (VMPC), as the primary control, and droop control, as the secondary control, were employed to control the output voltage of each DG and system frequency. The controller was equipped with a supplementary current limiting technique in order to limit the output current of each DG in abnormal incidents. The control approach also enabled smooth transition between the two modes. The performance of the control strategy was investigated and verified using PSCAD/EMTDC software

  14. Distributed Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus Fogtmann; Vandenberghe, Lieven; Poulsen, Niels Kjølstad

    2016-01-01

    Integration of a large number of flexible consumers in a smart grid requires a scalable power balancing strategy. We formulate the control problem as an optimization problem to be solved repeatedly by the aggregator in a model predictive control framework. To solve the large-scale control problem...

  15. Offset-Free Direct Power Control of DFIG Under Continuous-Time Model Predictive Control

    DEFF Research Database (Denmark)

    Errouissi, Rachid; Al-Durra, Ahmed; Muyeen, S.M.

    2017-01-01

    This paper presents a robust continuous-time model predictive direct power control for doubly fed induction generator (DFIG). The proposed approach uses Taylor series expansion to predict the stator current in the synchronous reference frame over a finite time horizon. The predicted stator current...... is directly used to compute the required rotor voltage in order to minimize the difference between the actual stator currents and their references over the predictive time. However, as the proposed strategy is sensitive to parameter variations and external disturbances, a disturbance observer is embedded...... into the control loop to remove the steady-state error of the stator current. It turns out that the steady-state and the transient performances can be identified by simple design parameters. In this paper, the reference of the stator current is directly calculated from the desired stator active and reactive powers...

  16. Use of Plant Hydraulic Theory to Predict Ecosystem Fluxes Across Mountainous Gradients in Environmental Controls and Insect Disturbances

    Science.gov (United States)

    Ewers, B. E.; Pendall, E.; Reed, D. E.; Barnard, H. R.; Whitehouse, F.; Frank, J. M.; Massman, W. J.; Brooks, P. D.; Biederman, J. A.; Harpold, A. A.; Naithani, K. J.; Mitra, B.; Mackay, D. S.; Norton, U.; Borkhuu, B.

    2011-12-01

    While mountainous areas are critical for providing numerous ecosystem benefits at the regional scale, the strong gradients in environmental controls make predictions difficult. A key part of the problem is quantifying and predicting the feedback between mountain gradients and plant function which then controls ecosystem cycling. The emerging theory of plant hydraulics provides a rigorous yet simple platform from which to generate testable hypotheses and predictions of ecosystem pools and fluxes. Plant hydraulic theory predicts that plant controls over carbon, water, energy and nutrient fluxes can be derived from the limitation of plant water transport from the soil through xylem and out of stomata. In addition, the limit to plant water transport can be predicted by combining plant structure (e.g. xylem diameters or root-to-shoot ratios) and plant function (response of stomatal conductance to vapor pressure deficit or root vulnerability to cavitation). We evaluate the predictions of the plant hydraulic theory by testing it against data from a mountain gradient encompassing sagebrush steppe through subalpine forests (2700 to 3400 m). We further test the theory by predicting the carbon, water and nutrient exchanges from several coniferous trees in the same gradient that are dying from xylem dysfunction caused by blue-stain fungi carried by bark beetles. The common theme of both of these data sets is a change in water limitation caused by either changing precipitation along the mountainous gradient or lack of access to soil water from xylem-occluding fungi. Across all of the data sets which range in scale from individual plants to hillslopes, the data fit the predictions of plant hydraulic theory. Namely, there was a proportional tradeoff between the reference canopy stomatal conductance to water vapor and the sensitivity of that conductance to vapor pressure deficit that quantitatively fits the predictions of plant hydraulic theory. Incorporating this result into

  17. Quantized Predictive Control over Erasure Channels

    DEFF Research Database (Denmark)

    E. Quevedo, Daniel; Østergaard, Jan

    2009-01-01

    .i.d. dropouts, the controller transmits data packets containing quantized plant input predictions. These minimize a finite horizon cost function and are provided by an appropriate optimal entropy coded dithered lattice vector quantizer. Within this context, we derive an equivalent noise-shaping model...

  18. Current error vector based prediction control of the section winding permanent magnet linear synchronous motor

    Energy Technology Data Exchange (ETDEWEB)

    Hong Junjie, E-mail: hongjjie@mail.sysu.edu.cn [School of Engineering, Sun Yat-Sen University, Guangzhou 510006 (China); Li Liyi, E-mail: liliyi@hit.edu.cn [Dept. Electrical Engineering, Harbin Institute of Technology, Harbin 150000 (China); Zong Zhijian; Liu Zhongtu [School of Engineering, Sun Yat-Sen University, Guangzhou 510006 (China)

    2011-10-15

    Highlights: {yields} The structure of the permanent magnet linear synchronous motor (SW-PMLSM) is new. {yields} A new current control method CEVPC is employed in this motor. {yields} The sectional power supply method is different to the others and effective. {yields} The performance gets worse with voltage and current limitations. - Abstract: To include features such as greater thrust density, higher efficiency without reducing the thrust stability, this paper proposes a section winding permanent magnet linear synchronous motor (SW-PMLSM), whose iron core is continuous, whereas winding is divided. The discrete system model of the motor is derived. With the definition of the current error vector and selection of the value function, the theory of the current error vector based prediction control (CEVPC) for the motor currents is explained clearly. According to the winding section feature, the motion region of the mover is divided into five zones, in which the implementation of the current predictive control method is proposed. Finally, the experimental platform is constructed and experiments are carried out. The results show: the current control effect has good dynamic response, and the thrust on the mover remains constant basically.

  19. Robust Predictive Functional Control for Flight Vehicles Based on Nonlinear Disturbance Observer

    Directory of Open Access Journals (Sweden)

    Yinhui Zhang

    2015-01-01

    Full Text Available A novel robust predictive functional control based on nonlinear disturbance observer is investigated in order to address the control system design for flight vehicles with significant uncertainties, external disturbances, and measurement noise. Firstly, the nonlinear longitudinal dynamics of the flight vehicle are transformed into linear-like state-space equations with state-dependent coefficient matrices. And then the lumped disturbances are considered in the linear structure predictive model of the predictive functional control to increase the precision of the predictive output and resolve the intractable mismatched disturbance problem. As the lumped disturbances cannot be derived or measured directly, the nonlinear disturbance observer is applied to estimate the lumped disturbances, which are then introduced to the predictive functional control to replace the unknown actual lumped disturbances. Consequently, the robust predictive functional control for the flight vehicle is proposed. Compared with the existing designs, the effectiveness and robustness of the proposed flight control are illustrated and validated in various simulation conditions.

  20. Relationship between the prognostic and predictive value of the intrinsic subtypes and a validated gene profile predictive of loco-regional control and benefit from post-mastectomy radiotherapy in patients with high-risk breast cancer

    DEFF Research Database (Denmark)

    Tramm, Trine; Kyndi, Marianne; Myhre, Simen

    2014-01-01

    , and has shown prognostic impact in terms of loco-regional failure and predictive impact for PMRT. Reports have also shown predictive value in terms of benefit of PMRT from intrinsic subtypes and derived approximations. The aim of this study was to examine: 1) the agreement between various methods...... for determining the intrinsic subtypes; and 2) the relationship between the prognostic and predictive impact of the DBCG-RT profile and the intrinsic subtypes. MATERIAL AND METHODS: Intrinsic subtypes and the DBCG-RT profile was determined from microarray analysis based on fresh frozen tissue from 191 patients...... and predictive information obtained from the DBCG-RT profile cannot be substituted by any approximation of the tumors intrinsic subtype. The predictive value of the intrinsic subtypes in terms of PMRT was influenced by the method used for assignment to the intrinsic subtypes....

  1. Fourier transform wavefront control with adaptive prediction of the atmosphere.

    Science.gov (United States)

    Poyneer, Lisa A; Macintosh, Bruce A; Véran, Jean-Pierre

    2007-09-01

    Predictive Fourier control is a temporal power spectral density-based adaptive method for adaptive optics that predicts the atmosphere under the assumption of frozen flow. The predictive controller is based on Kalman filtering and a Fourier decomposition of atmospheric turbulence using the Fourier transform reconstructor. It provides a stable way to compensate for arbitrary numbers of atmospheric layers. For each Fourier mode, efficient and accurate algorithms estimate the necessary atmospheric parameters from closed-loop telemetry and determine the predictive filter, adjusting as conditions change. This prediction improves atmospheric rejection, leading to significant improvements in system performance. For a 48x48 actuator system operating at 2 kHz, five-layer prediction for all modes is achievable in under 2x10(9) floating-point operations/s.

  2. Modeling, robust and distributed model predictive control for freeway networks

    NARCIS (Netherlands)

    Liu, S.

    2016-01-01

    In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

  3. Gain-Scheduled Model Predictive Control of Wind Turbines using Laguerre Functions

    DEFF Research Database (Denmark)

    Adegas, Fabiano Daher; Wisniewski, Rafal; Larsen, Lars Finn Sloth

    2014-01-01

    This paper presents a systematic approach to design gain-scheduled predictive controllers for wind turbines. The predictive control law is based on Laguerre functions to parameterize control signals and a parameter-dependent cost function that is analytically determined from turbine data....... These properties facilitate the design of speed controllers by placement of the closed-loop poles (when constraints are not active) and systematic adaptation towards changes in the operating point. Vibration control of undamped modes is achieved by imposing a certain degree of stability to the closed-loop system....... The approach can be utilized to the design of new controllers and to represent existing gain-scheduled controllers as predictive controllers. The numerical example and simulations illustrate the design of a speed controller augmented with active damping of the tower fore-aft displacement....

  4. Prediction based chaos control via a new neural network

    International Nuclear Information System (INIS)

    Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

    2008-01-01

    In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network

  5. Fractional-Order Generalized Predictive Control: Application for Low-Speed Control of Gasoline-Propelled Cars

    Directory of Open Access Journals (Sweden)

    M. Romero

    2013-01-01

    Full Text Available There is an increasing interest in using fractional calculus applied to control theory generalizing classical control strategies as the PID controller and developing new ones with the intention of taking advantage of characteristics supplied by this mathematical tool for the controller definition. In this work, the fractional generalization of the successful and spread control strategy known as model predictive control is applied to drive autonomously a gasoline-propelled vehicle at low speeds. The vehicle is a Citroën C3 Pluriel that was modified to act over the throttle and brake pedals. Its highly nonlinear dynamics are an excellent test bed for applying beneficial characteristics of fractional predictive formulation to compensate unmodeled dynamics and external disturbances.

  6. Including model uncertainty in the model predictive control with output feedback

    Directory of Open Access Journals (Sweden)

    Rodrigues M.A.

    2002-01-01

    Full Text Available This paper addresses the development of an efficient numerical output feedback robust model predictive controller for open-loop stable systems. Stability of the closed loop is guaranteed by using an infinite horizon predictive controller and a stable state observer. The performance and the computational burden of this approach are compared to a robust predictive controller from the literature. The case used for this study is based on an industrial gasoline debutanizer column.

  7. Selection of References in Wind Turbine Model Predictive Control Design

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Hovgaard, Tobias

    2015-01-01

    a model predictive controller for a wind turbine. One of the important aspects for a tracking control problem is how to setup the optimal reference tracking problem, as it might be relevant to track, e.g., the three concurrent references: optimal pitch angle, optimal rotational speed, and optimal power......Lowering the cost of energy is one of the major focus areas in the wind turbine industry. Recent research has indicated that wind turbine controllers based on model predictive control methods can be useful in obtaining this objective. A number of design considerations have to be made when designing....... The importance if the individual references differ depending in particular on the wind speed. In this paper we investigate the performance of a reference tracking model predictive controller with two different setups of the used optimal reference signals. The controllers are evaluated using an industrial high...

  8. Contrasting predictability of summer monsoon rainfall ISOs over the northeastern and western Himalayan region: an application of Hurst exponent

    Science.gov (United States)

    Mukherjee, Sandipan

    2017-09-01

    Due to heterogeneous nonlinear forcing of complex geomorphological features, predictability of monsoon rainfall 10-90-day intra-seasonal oscillations (ISO) over the complex terrain of northeastern and western Himalayan region (NEH and WH) remained poorly quantified. Using 72 and 61 number of station observations of monsoon rainfall ISOs of NEH and WH, respectively, this study attempts to investigate variation in the regional scale predictability of monsoon rainfall ISOs with respect to changing geomorphological features and monsoon rainfall characteristics. In view of the bimodal nonlinear dynamical structure of monsoon rainfall ISO, the fractal dynamical Hurst exponent-based predictability indices are estimated as an indicator of predictability for station observations of NEH and WH, and relationships with elevations, slopes, aspects, and average numbers of occurrences of long (short) spell of active (break) phases are investigated. Results show 10-90-day ISOs are anti-persistent throughout the IHR, although, predictability of 10-90-day ISOs is higher over the NEH region than WH. Predictabilities of ISOs are found to decrease with increasing elevation and slope for both NEH and WH regions. Predictabilities of ISOs over both regions are also found to increase linearly as the number of occurrences of monsoon rainfall ISO phases (active/break) increases.

  9. Switch region for pathogenic structural change in conformational disease and its prediction.

    Directory of Open Access Journals (Sweden)

    Xin Liu

    2010-01-01

    Full Text Available Many diseases are believed to be related to abnormal protein folding. In the first step of such pathogenic structural changes, misfolding occurs in regions important for the stability of the native structure. This destabilizes the normal protein conformation, while exposing the previously hidden aggregation-prone regions, leading to subsequent errors in the folding pathway. Sites involved in this first stage can be deemed switch regions of the protein, and can represent perfect binding targets for drugs to block the abnormal folding pathway and prevent pathogenic conformational changes. In this study, a prediction algorithm for the switch regions responsible for the start of pathogenic structural changes is introduced. With an accuracy of 94%, this algorithm can successfully find short segments covering sites significant in triggering conformational diseases (CDs and is the first that can predict switch regions for various CDs. To illustrate its effectiveness in dealing with urgent public health problems, the reason of the increased pathogenicity of H5N1 influenza virus is analyzed; the mechanisms of the pandemic swine-origin 2009 A(H1N1 influenza virus in overcoming species barriers and in infecting large number of potential patients are also suggested. It is shown that the algorithm is a potential tool useful in the study of the pathology of CDs because: (1 it can identify the origin of pathogenic structural conversion with high sensitivity and specificity, and (2 it provides an ideal target for clinical treatment.

  10. Neural Network Predictive Control for Vanadium Redox Flow Battery

    Directory of Open Access Journals (Sweden)

    Hai-Feng Shen

    2013-01-01

    Full Text Available The vanadium redox flow battery (VRB is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.

  11. Simulation research on multivariable fuzzy model predictive control of nuclear power plant

    International Nuclear Information System (INIS)

    Su Jie

    2012-01-01

    To improve the dynamic control capabilities of the nuclear power plant, the algorithm of the multivariable nonlinear predictive control based on the fuzzy model was applied in the main parameters control of the nuclear power plant, including control structure and the design of controller in the base of expounding the math model of the turbine and the once-through steam generator. The simulation results show that the respond of the change of the gas turbine speed and the steam pressure under the algorithm of multivariable fuzzy model predictive control is faster than that under the PID control algorithm, and the output value of the gas turbine speed and the steam pressure under the PID control algorithm is 3%-5% more than that under the algorithm of multi-variable fuzzy model predictive control. So it shows that the algorithm of multi-variable fuzzy model predictive control can control the output of the main parameters of the nuclear power plant well and get better control effect. (author)

  12. 40 CFR 81.62 - Northeast Mississippi Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Northeast Mississippi Intrastate Air... Air Quality Control Regions § 81.62 Northeast Mississippi Intrastate Air Quality Control Region. The Alabama-Mississippi-Tennessee Interstate Air Quality Control Region has been renamed the Northeast...

  13. 40 CFR 81.216 - Northeast Indiana Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Northeast Indiana Intrastate Air... Air Quality Control Regions § 81.216 Northeast Indiana Intrastate Air Quality Control Region. The Northeast Indiana Intrastate Air Quality Control Region (Indiana) consists of the territorial area...

  14. 40 CFR 81.162 - Northeast Plateau Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Northeast Plateau Intrastate Air... Air Quality Control Regions § 81.162 Northeast Plateau Intrastate Air Quality Control Region. The Northeast Plateau Intrastate Air Quality Control Region (California) consists of the territorial area...

  15. Model Predictive Control Algorithms for Pen and Pump Insulin Administration

    DEFF Research Database (Denmark)

    Boiroux, Dimitri

    at mealtime, and the case where the insulin sensitivity increases during the night. This thesis consists of a summary report, glucose and insulin proles of the clinical studies and research papers submitted, peer-reviewed and/or published in the period September 2009 - September 2012....... of current closed-loop controllers. In this thesis, we present different control strategies based on Model Predictive Control (MPC) for an artificial pancreas. We use Nonlinear Model Predictive Control (NMPC) in order to determine the optimal insulin and blood glucose profiles. The optimal control problem...

  16. An Improved Continuous-Time Model Predictive Control of Permanent Magnetic Synchronous Motors for a Wide-Speed Range

    Directory of Open Access Journals (Sweden)

    Dandan Su

    2017-12-01

    Full Text Available This paper proposes an improved continuous-time model predictive control (CTMPC of permanent magnetic synchronous motors (PMSMs for a wide-speed range, including the constant torque region and the flux-weakening (FW region. In the constant torque region, the mathematic models of PMSMs in dq-axes are decoupled without the limitation of DC-link voltage. However, in the FW region, the mathematic models of PMSMs in dq-axes are cross-coupled together with the limitation of DC-link voltage. A nonlinear PMSMs mathematic model in the FW region is presented based on the voltage angle. The solving of the nonlinear mathematic model of PMSMs in FW region will lead to heavy computation load for digital signal processing (DSP. To overcome such a problem, a linearization method of the voltage angle is also proposed to reduce the computation load. The selection of transiting points between the constant torque region and FW regions is researched to improve the performance of the driven system. Compared with the proportional integral (PI controller, the proposed CTMPC has obvious advantages in dealing with systems’ nonlinear constraints and improving system performance by restraining overshoot current under step torque changing. Both simulation and experimental results confirm the effectiveness of the proposed method in achieving good steady-state performance and smooth switching between the constant torque and FW regions.

  17. Efficient predictive model-based and fuzzy control for green urban mobility

    NARCIS (Netherlands)

    Jamshidnejad, A.

    2017-01-01

    In this thesis, we develop efficient predictive model-based control approaches, including model-predictive control (MPC) andmodel-based fuzzy control, for application in urban traffic networks with the aim of reducing a combination of the total time spent by the vehicles within the network and the

  18. 40 CFR 81.17 - Metropolitan Los Angeles Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.17 Metropolitan Los Angeles Air Quality Control Region. The Metropolitan Los Angeles Air Quality Control Region consists of the following territorial area (including the territorial... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Metropolitan Los Angeles Air Quality...

  19. 40 CFR 81.32 - Puget Sound Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.32 Puget Sound Intrastate Air Quality Control Region. The Puget Sound Intrastate Air Quality Control Region (Washington) consists of the territorial area encompassed by the... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Puget Sound Intrastate Air Quality...

  20. 40 CFR 81.256 - Northeast Iowa Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Northeast Iowa Intrastate Air Quality Control Region. 81.256 Section 81.256 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... Quality Control Regions § 81.256 Northeast Iowa Intrastate Air Quality Control Region. The Northeast Iowa...

  1. 40 CFR 81.237 - Northeast Georgia Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Northeast Georgia Intrastate Air... Air Quality Control Regions § 81.237 Northeast Georgia Intrastate Air Quality Control Region. The Northeast Georgia Intrastate Air Quality Control Region consists of the territorial area encompassed by the...

  2. 40 CFR 81.139 - Northeast Arkansas Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Northeast Arkansas Intrastate Air... Air Quality Control Regions § 81.139 Northeast Arkansas Intrastate Air Quality Control Region. The Northeast Arkansas Intrastate Air Quality Control Region consists of the territorial area encompassed by the...

  3. 40 CFR 81.251 - Northeast Kansas Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Northeast Kansas Intrastate Air... Air Quality Control Regions § 81.251 Northeast Kansas Intrastate Air Quality Control Region. The Northeast Kansas Intrastate Air Quality Control Region consists of the territorial area encompassed by the...

  4. 40 CFR 81.134 - Austin-Waco Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Austin-Waco Intrastate Air Quality Control Region. 81.134 Section 81.134 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... Quality Control Regions § 81.134 Austin-Waco Intrastate Air Quality Control Region. The Austin-Waco...

  5. 40 CFR 81.121 - Four Corners Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Four Corners Interstate Air Quality Control Region. 81.121 Section 81.121 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... Quality Control Regions § 81.121 Four Corners Interstate Air Quality Control Region. The Four Corners...

  6. 40 CFR 81.93 - Hampton Roads Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Hampton Roads Intrastate Air Quality Control Region. 81.93 Section 81.93 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... Quality Control Regions § 81.93 Hampton Roads Intrastate Air Quality Control Region. The Metropolitan...

  7. Distributed Model Predictive Control for Active Power Control of Wind Farm

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Rasmussen, Claus Nygaard

    2014-01-01

    This paper presents the active power control of a wind farm using the Distributed Model Predictive Controller (D- MPC) via dual decomposition. Different from the conventional centralized wind farm control, multiple objectives such as power reference tracking performance and wind turbine load can...... be considered to achieve a trade-off between them. Additionally, D- MPC is based on communication among the subsystems. Through the interaction among the neighboring subsystems, the global optimization could be achieved, which significantly reduces the computation burden. It is suitable for the modern large......-scale wind farm control....

  8. Wind farms production: Control and prediction

    Science.gov (United States)

    El-Fouly, Tarek Hussein Mostafa

    Wind energy resources, unlike dispatchable central station generation, produce power dependable on external irregular source and that is the incident wind speed which does not always blow when electricity is needed. This results in the variability, unpredictability, and uncertainty of wind resources. Therefore, the integration of wind facilities to utility electrical grid presents a major challenge to power system operator. Such integration has significant impact on the optimum power flow, transmission congestion, power quality issues, system stability, load dispatch, and economic analysis. Due to the irregular nature of wind power production, accurate prediction represents the major challenge to power system operators. Therefore, in this thesis two novel models are proposed for wind speed and wind power prediction. One proposed model is dedicated to short-term prediction (one-hour ahead) and the other involves medium term prediction (one-day ahead). The accuracy of the proposed models is revealed by comparing their results with the corresponding values of a reference prediction model referred to as the persistent model. Utility grid operation is not only impacted by the uncertainty of the future production of wind farms, but also by the variability of their current production and how the active and reactive power exchange with the grid is controlled. To address this particular task, a control technique for wind turbines, driven by doubly-fed induction generators (DFIGs), is developed to regulate the terminal voltage by equally sharing the generated/absorbed reactive power between the rotor-side and the gridside converters. To highlight the impact of the new developed technique in reducing the power loss in the generator set, an economic analysis is carried out. Moreover, a new aggregated model for wind farms is proposed that accounts for the irregularity of the incident wind distribution throughout the farm layout. Specifically, this model includes the wake effect

  9. Prediction of active control of subsonic centrifugal compressor rotating stall

    Science.gov (United States)

    Lawless, Patrick B.; Fleeter, Sanford

    1993-01-01

    A mathematical model is developed to predict the suppression of rotating stall in a centrifugal compressor with a vaned diffuser. This model is based on the employment of a control vortical waveform generated upstream of the impeller inlet to damp weak potential disturbances that are the early stages of rotating stall. The control system is analyzed by matching the perturbation pressure in the compressor inlet and exit flow fields with a model for the unsteady behavior of the compressor. The model was effective at predicting the stalling behavior of the Purdue Low Speed Centrifugal Compressor for two distinctly different stall patterns. Predictions made for the effect of a controlled inlet vorticity wave on the stability of the compressor show that for minimum control wave magnitudes, on the order of the total inlet disturbance magnitude, significant damping of the instability can be achieved. For control waves of sufficient amplitude, the control phase angle appears to be the most important factor in maintaining a stable condition in the compressor.

  10. 40 CFR 81.110 - Camden-Sumter Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.110 Camden-Sumter Intrastate Air Quality Control Region. The Camden-Sumter Intrastate Air Quality Control Region (South Carolina) consists of the territorial area encompassed by the... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Camden-Sumter Intrastate Air Quality...

  11. 40 CFR 81.52 - Wasatch Front Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.52 Wasatch Front Intrastate Air Quality Control Region. The Wasatch Front Intrastate Air Quality Control Region (Utah) consists of the territorial area encompassed by the boundaries... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Wasatch Front Intrastate Air Quality...

  12. 40 CFR 81.54 - Cook Inlet Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.54 Cook Inlet Intrastate Air Quality Control Region. The Cook Inlet Intrastate Air Quality Control Region (Alaska) consists of the territorial area encompassed by the boundaries... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Cook Inlet Intrastate Air Quality...

  13. 40 CFR 81.95 - Central Florida Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.95 Central Florida Intrastate Air Quality Control Region. The Central Florida Intrastate Air Quality Control Region consists of the territorial area encompassed by the boundaries of the... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Central Florida Intrastate Air Quality...

  14. 40 CFR 81.176 - San Luis Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false San Luis Intrastate Air Quality Control Region. 81.176 Section 81.176 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... Quality Control Regions § 81.176 San Luis Intrastate Air Quality Control Region. The San Luis Intrastate...

  15. The influence of active region information on the prediction of solar flares: an empirical model using data mining

    Directory of Open Access Journals (Sweden)

    M. Núñez

    2005-11-01

    Full Text Available Predicting the occurrence of solar flares is a challenge of great importance for many space weather scientists and users. We introduce a data mining approach, called Behavior Pattern Learning (BPL, for automatically discovering correlations between solar flares and active region data, in order to predict the former. The goal of BPL is to predict the interval of time to the next solar flare and provide a confidence value for the associated prediction. The discovered correlations are described in terms of easy-to-read rules. The results indicate that active region dynamics is essential for predicting solar flares.

  16. Stability of the spreading in small-world network with predictive controller

    International Nuclear Information System (INIS)

    Bao, Z.J.; Jiang, Q.Y.; Yan, W.J.; Cao, Y.J.

    2010-01-01

    In this Letter, we apply the predictive control strategy to suppress the propagation of diseases or viruses in small-world network. The stability of small-world spreading model with predictive controller is investigated. The sufficient and necessary stability condition is given, which is closely related to the controller parameters and small-world rewiring probability p. Our simulations discover a phenomenon that, with the fixed predictive controller parameters, the spreading dynamics become more and more stable when p decreases from a larger value to a smaller one, and the suitable controller parameters can effectively suppress the spreading behaviors even when p varies within the whole spectrum, and the unsuitable controller parameters can lead to oscillation when p lies within a certain range.

  17. 40 CFR 81.80 - Las Vegas Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Regions § 81.80 Las Vegas Intrastate Air Quality Control Region. The Las Vegas Intrastate Air Quality Control Region (Nevada) has been revised to consist of the territorial area encompassed by... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Las Vegas Intrastate Air Quality...

  18. Quality by control: Towards model predictive control of mammalian cell culture bioprocesses.

    Science.gov (United States)

    Sommeregger, Wolfgang; Sissolak, Bernhard; Kandra, Kulwant; von Stosch, Moritz; Mayer, Martin; Striedner, Gerald

    2017-07-01

    The industrial production of complex biopharmaceuticals using recombinant mammalian cell lines is still mainly built on a quality by testing approach, which is represented by fixed process conditions and extensive testing of the end-product. In 2004 the FDA launched the process analytical technology initiative, aiming to guide the industry towards advanced process monitoring and better understanding of how critical process parameters affect the critical quality attributes. Implementation of process analytical technology into the bio-production process enables moving from the quality by testing to a more flexible quality by design approach. The application of advanced sensor systems in combination with mathematical modelling techniques offers enhanced process understanding, allows on-line prediction of critical quality attributes and subsequently real-time product quality control. In this review opportunities and unsolved issues on the road to a successful quality by design and dynamic control implementation are discussed. A major focus is directed on the preconditions for the application of model predictive control for mammalian cell culture bioprocesses. Design of experiments providing information about the process dynamics upon parameter change, dynamic process models, on-line process state predictions and powerful software environments seem to be a prerequisite for quality by control realization. © 2017 The Authors. Biotechnology Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. A LIDAR-assisted model predictive controller added on a traditional wind turbine controller

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Hansen, Morten Hartvig

    2016-01-01

    control and opens the market of retrofitting existing wind turbines with the new technology. In this paper, we suggest a model predictive controller (MPC) that is added to the basic gain scheduled PI controller of a WT to enhance the performance of the closed loop system using LIDAR measurements...

  20. Predictive Function Control for Communication-Based Train Control (CBTC Systems

    Directory of Open Access Journals (Sweden)

    Bing Bu

    2013-01-01

    Full Text Available In Communication-Based Train Control (CBTC systems, random transmission delays and packet drops are inevitable in the wireless networks, which could result in unnecessary traction, brakes or even emergency brakes of trains, losses of line capacity and passenger dissatisfaction. This paper applies predictive function control technology with a mixed H2/∞ control approach to improve the control performances. The controller is in the state feedback form and satisfies the requirement of quadratic input and state constraints. A linear matrix inequality (LMI approach is developed to solve the control problem. The proposed method attenuates disturbances by incorporating H2/∞ into the control scheme. The control command from the automatic train operation (ATO is included in the reward function to optimize the train's running profile. The influence of transmission delays and packet drops is alleviated through improving the performances of the controller. Simulation results show that the method is effective to improve the performances and robustness of CBTC systems.

  1. Stability of a neural predictive controller scheme on a neural model

    DEFF Research Database (Denmark)

    Luther, Jim Benjamin; Sørensen, Paul Haase

    2009-01-01

    In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue....... The resulting controller is tested on a nonlinear pneumatic servo system.......In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue...... has not been addressed specifically for these controllers. On the other hand a number of results concerning the stability of receding horizon controllers on a nonlinear system exist. In this paper we present a proof of stability for a predictive controller controlling a neural network model...

  2. Regional dialogue and multilateral arms control efforts today

    International Nuclear Information System (INIS)

    Graham, T. Jr.

    1994-01-01

    The significance of arms control and non-proliferation tasks is stressed emphasising what has been done, and-more important-what must still be done. Although tangible developments at the regional level may seem at times to be slow in coming, it is important to remember that the United States-Soviet transition from voluntary declarations to detailed arms control agreements with intrusive verification regimes spanned the entire length of cold war. Given the instabilities afflicting these regions, establishing a regional dialogue is itself a confidence-building measure. The experience of the United States and the former Soviet Union amply demonstrates that mere existence of regular dialogue can reduce tension by providing a platform for communication among military and Government participants even when political crisis prevents diplomatic contacts at senior levels. The international community should be encouraged by the progress that has been made to date to address regional instabilities. Such steps are an integral part of the international effort, carried out both at the United Nations and elsewhere, to adopt acceptable levels of conventional military forces and to eliminate weapons of mass destruction. Given the instabilities afflicting these regions, establishing a regional dialogue is itself a confidence-building measure. World changes, while potentially dangerous and certainly challenging, offer the hope of replacing antagonism with cooperation, creating a progressively more democratic global environment, preventing proliferation, and dampening regional conflicts. Continuing to make effective use of the entire mix of arms control and confidence-building tools will help to realize these hopes

  3. Modeling, Prediction, and Control of Heating Temperature for Tube Billet

    Directory of Open Access Journals (Sweden)

    Yachun Mao

    2015-01-01

    Full Text Available Annular furnaces have multivariate, nonlinear, large time lag, and cross coupling characteristics. The prediction and control of the exit temperature of a tube billet are important but difficult. We establish a prediction model for the final temperature of a tube billet through OS-ELM-DRPLS method. We address the complex production characteristics, integrate the advantages of PLS and ELM algorithms in establishing linear and nonlinear models, and consider model update and data lag. Based on the proposed model, we design a prediction control algorithm for tube billet temperature. The algorithm is validated using the practical production data of Baosteel Co., Ltd. Results show that the model achieves the precision required in industrial applications. The temperature of the tube billet can be controlled within the required temperature range through compensation control method.

  4. Two stage neural network modelling for robust model predictive control.

    Science.gov (United States)

    Patan, Krzysztof

    2018-01-01

    The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Hybrid model predictive control applied to switching control of burner load for a compact marine boiler design

    DEFF Research Database (Denmark)

    Solberg, Brian; Andersen, Palle; Maciejowski, Jan

    2008-01-01

    This paper discusses the application of hybrid model predictive control to control switching between different burner modes in a novel compact marine boiler design. A further purpose of the present work is to point out problems with finite horizon model predictive control applied to systems for w...

  6. Range-Space Predictive Control for Optimal Robot Motion

    Czech Academy of Sciences Publication Activity Database

    Belda, Květoslav; Böhm, Josef

    2008-01-01

    Roč. 1, č. 1 (2008), s. 1-7 ISSN 1998-0140 R&D Projects: GA ČR GP102/06/P275 Institutional research plan: CEZ:AV0Z10750506 Keywords : Accurate manipulation * Industrial robotics * Predictive control * Range-space control Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/historie/belda-0305644.pdf

  7. Predicting worsening asthma control following the common cold

    NARCIS (Netherlands)

    Walter, M. J.; Castro, M.; Kunselman, S. J.; Chinchilli, V. M.; Reno, M.; Ramkumar, T. P.; Avila, P. C.; Boushey, H. A.; Ameredes, B. T.; Bleecker, E. R.; Calhoun, W. J.; Cherniack, R. M.; Craig, T. J.; Denlinger, L. C.; Israel, E.; Fahy, J. V.; Jarjour, N. N.; Kraft, M.; Lazarus, S. C.; Lemanske, R. F.; Martin, R. J.; Peters, S. P.; Ramsdell, J. W.; Sorkness, C. A.; Sutherland, E. R.; Szefler, S. J.; Wasserman, S. I.; Wechsler, M. E.

    2008-01-01

    The asthmatic response to the common cold is highly variable, and early characteristics that predict worsening of asthma control following a cold have not been identified. In this prospective multicentric cohort study of 413 adult subjects with asthma, the mini-Asthma Control Questionnaire

  8. Prediction Governors for Input-Affine Nonlinear Systems and Application to Automatic Driving Control

    Directory of Open Access Journals (Sweden)

    Yuki Minami

    2018-04-01

    Full Text Available In recent years, automatic driving control has attracted attention. To achieve a satisfactory driving control performance, the prediction accuracy of the traveling route is important. If a highly accurate prediction method can be used, an accurate traveling route can be obtained. Despite the considerable efforts that have been invested in improving prediction methods, prediction errors do occur in general. Thus, a method to minimize the influence of prediction errors on automatic driving control systems is required. This need motivated us to focus on the design of a mechanism for shaping prediction signals, which is called a prediction governor. In this study, we first extended our previous study to the input-affine nonlinear system case. Then, we analytically derived a solution to an optimal design problem of prediction governors. Finally, we applied the solution to an automatic driving control system, and demonstrated its usefulness through a numerical example and an experiment using a radio controlled car.

  9. One-Step-Ahead Predictive Control for Hydroturbine Governor

    Directory of Open Access Journals (Sweden)

    Zhihuai Xiao

    2015-01-01

    Full Text Available The hydroturbine generator regulating system can be considered as one system synthetically integrating water, machine, and electricity. It is a complex and nonlinear system, and its configuration and parameters are time-dependent. A one-step-ahead predictive control based on on-line trained neural networks (NNs for hydroturbine governor with variation in gate position is described in this paper. The proposed control algorithm consists of a one-step-ahead neuropredictor that tracks the dynamic characteristics of the plant and predicts its output and a neurocontroller to generate the optimal control signal. The weights of two NNs, initially trained off-line, are updated on-line according to the scalar error. The proposed controller can thus track operating conditions in real-time and produce the optimal control signal over the wide operating range. Only the inputs and outputs of the generator are measured and there is no need to determine the other states of the generator. Simulations have been performed with varying operating conditions and different disturbances to compare the performance of the proposed controller with that of a conventional PID controller and validate the feasibility of the proposed approach.

  10. Distributed model predictive control made easy

    CERN Document Server

    Negenborn, Rudy

    2014-01-01

    The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.   This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...

  11. Robust Output Model Predictive Control of an Unstable Rijke Tube

    Directory of Open Access Journals (Sweden)

    Fabian Jarmolowitz

    2012-01-01

    Full Text Available This work investigates the active control of an unstable Rijke tube using robust output model predictive control (RMPC. As internal model a polytopic linear system with constraints is assumed to account for uncertainties. For guaranteed stability, a linear state feedback controller is designed using linear matrix inequalities and used within a feedback formulation of the model predictive controller. For state estimation a robust gain-scheduled observer is developed. It is shown that the proposed RMPC ensures robust stability under constraints over the considered operating range.

  12. Characterization and evolution of the mitochondrial DNA control region in hornbills (Bucerotiformes).

    Science.gov (United States)

    Delport, Wayne; Ferguson, J Willem H; Bloomer, Paulette

    2002-06-01

    We determined the mitochondrial DNA control region sequences of six Bucerotiformes. Hornbills have the typical avian gene order and their control region is similar to other avian control regions in that it is partitioned into three domains: two variable domains that flank a central conserved domain. Two characteristics of the hornbill control region sequence differ from that of other birds. First, domain I is AT rich as opposed to AC rich, and second, the control region is approximately 500 bp longer than that of other birds. Both these deviations from typical avian control region sequence are explainable on the basis of repeat motifs in domain I of the hornbill control region. The repeat motifs probably originated from a duplication of CSB-1 as has been determined in chicken, quail, and snowgoose. Furthermore, the hornbill repeat motifs probably arose before the divergence of hornbills from each other but after the divergence of hornbills from other avian taxa. The mitochondrial control region of hornbills is suitable for both phylogenetic and population studies, with domains I and II probably more suited to population and phylogenetic analyses, respectively.

  13. Sparsely-Packetized Predictive Control by Orthogonal Matching Pursuit

    DEFF Research Database (Denmark)

    Nagahara, Masaaki; Quevedo, Daniel; Østergaard, Jan

    2012-01-01

    We study packetized predictive control, known to be robust against packet dropouts in networked systems. To obtain sparse packets for rate-limited networks, we design control packets via an ℓ0 optimization, which can be eectively solved by orthogonal matching pursuit. Our formulation ensures...

  14. Repetitive model predictive approach to individual pitch control of wind turbines

    DEFF Research Database (Denmark)

    Adegas, Fabiano Daher; Stoustrup, Jakob; Odgaard, Peter Fogh

    2011-01-01

    prediction. As a consequence, individual pitch feed-forward control action is generated by the controller, taking ”future” wind disturbance into account. Information about the estimated wind spatial distribution one blade experience can be used in the prediction model to better control the next passing blade......Wind turbines are inherently exposed to nonuniform wind fields with of wind shear, tower shadow, and possible wake contributions. Asymmetrical aerodynamic rotor loads are a consequence of such periodic, repetitive wind disturbances experienced by the blades. A controller may estimate and use...... this peculiar disturbance pattern to better attenuate loads and regulate power by controlling the blade pitch angles individually. A novel model predictive (MPC) approach for individual pitch control of wind turbines is proposed in this paper. A repetitive wind disturbance model is incorporated into the MPC...

  15. Regional Longitudinal Deformation Improves Prediction of Ventricular Tachyarrhythmias in Patients With Heart Failure With Reduced Ejection Fraction

    DEFF Research Database (Denmark)

    Biering-Sørensen, Tor; Knappe, Dorit; Pouleur, Anne-Catherine

    2017-01-01

    BACKGROUND: Left ventricular dysfunction is a known predictor of ventricular arrhythmias. We hypothesized that measures of regional longitudinal deformation by speckle-tracking echocardiography predict ventricular tachyarrhythmias and provide incremental prognostic information over clinical...... in the model, only a decreasing myocardial function in the inferior myocardial wall predicted VT/VF (hazard ratio, 1.05 [1.00-1.11]; P=0.039). Only strain obtained from the inferior myocardial wall provided incremental prognostic information for VT/VF over clinical and echocardiographic parameters (C statistic...... 0.71 versus 0.69; P=0.005). CONCLUSIONS: Assessment of regional longitudinal myocardial deformation in the inferior region provided incremental prognostic information over clinical and echocardiographic risk factors in predicting ventricular tachyarrhythmias. CLINICAL TRIAL REGISTRATION: URL: http...

  16. Artificial neural network implementation of a near-ideal error prediction controller

    Science.gov (United States)

    Mcvey, Eugene S.; Taylor, Lynore Denise

    1992-01-01

    A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error

  17. Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors

    Science.gov (United States)

    Mercer, Joey; Hunt-Espinosa, Sarah; Bienert, Nancy; Laraway, Sean

    2016-01-01

    A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASA's Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlanta's Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the study's trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 30-knot over-predictions and 30-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?

  18. Tailored high-resolution numerical weather forecasts for energy efficient predictive building control

    Science.gov (United States)

    Stauch, V. J.; Gwerder, M.; Gyalistras, D.; Oldewurtel, F.; Schubiger, F.; Steiner, P.

    2010-09-01

    The high proportion of the total primary energy consumption by buildings has increased the public interest in the optimisation of buildings' operation and is also driving the development of novel control approaches for the indoor climate. In this context, the use of weather forecasts presents an interesting and - thanks to advances in information and predictive control technologies and the continuous improvement of numerical weather prediction (NWP) models - an increasingly attractive option for improved building control. Within the research project OptiControl (www.opticontrol.ethz.ch) predictive control strategies for a wide range of buildings, heating, ventilation and air conditioning (HVAC) systems, and representative locations in Europe are being investigated with the aid of newly developed modelling and simulation tools. Grid point predictions for radiation, temperature and humidity of the high-resolution limited area NWP model COSMO-7 (see www.cosmo-model.org) and local measurements are used as disturbances and inputs into the building system. The control task considered consists in minimizing energy consumption whilst maintaining occupant comfort. In this presentation, we use the simulation-based OptiControl methodology to investigate the impact of COSMO-7 forecasts on the performance of predictive building control and the resulting energy savings. For this, we have selected building cases that were shown to benefit from a prediction horizon of up to 3 days and therefore, are particularly suitable for the use of numerical weather forecasts. We show that the controller performance is sensitive to the quality of the weather predictions, most importantly of the incident radiation on differently oriented façades. However, radiation is characterised by a high temporal and spatial variability in part caused by small scale and fast changing cloud formation and dissolution processes being only partially represented in the COSMO-7 grid point predictions. On the

  19. Predicted Habitat Suitability for All Mesophotic Corals in the Au'au Channel Region

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This raster denotes predicted habitat suitability for all mesophotic corals in the Au'au Channel region. Maximum Entropy (MaxEnt) modeling software was used to...

  20. Region based Brain Computer Interface for a home control application.

    Science.gov (United States)

    Akman Aydin, Eda; Bay, Omer Faruk; Guler, Inan

    2015-08-01

    Environment control is one of the important challenges for disabled people who suffer from neuromuscular diseases. Brain Computer Interface (BCI) provides a communication channel between the human brain and the environment without requiring any muscular activation. The most important expectation for a home control application is high accuracy and reliable control. Region-based paradigm is a stimulus paradigm based on oddball principle and requires selection of a target at two levels. This paper presents an application of region based paradigm for a smart home control application for people with neuromuscular diseases. In this study, a region based stimulus interface containing 49 commands was designed. Five non-disabled subjects were attended to the experiments. Offline analysis results of the experiments yielded 95% accuracy for five flashes. This result showed that region based paradigm can be used to select commands of a smart home control application with high accuracy in the low number of repetitions successfully. Furthermore, a statistically significant difference was not observed between the level accuracies.

  1. Model Predictive Control for Connected Hybrid Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Kaijiang Yu

    2015-01-01

    Full Text Available This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV for Pbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electric vehicle is developed using the maps of the engine efficiency characteristics. Fourth, simulations and analysis (under different parameters, i.e., road conditions, vehicle state of charge, etc. are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method.

  2. Parameter transferability within homogeneous regions and comparisons with predictions from a priori parameters in the eastern United States

    Science.gov (United States)

    Chouaib, Wafa; Alila, Younes; Caldwell, Peter V.

    2018-05-01

    The need for predictions of flow time-series persists at ungauged catchments, motivating the research goals of our study. By means of the Sacramento model, this paper explores the use of parameter transfer within homogeneous regions of similar climate and flow characteristics and makes comparisons with predictions from a priori parameters. We assessed the performance using the Nash-Sutcliffe (NS), bias, mean monthly hydrograph and flow duration curve (FDC). The study was conducted on a large dataset of 73 catchments within the eastern US. Two approaches to the parameter transferability were developed and evaluated; (i) the within homogeneous region parameter transfer using one donor catchment specific to each region, (ii) the parameter transfer disregarding the geographical limits of homogeneous regions, where one donor catchment was common to all regions. Comparisons between both parameter transfers enabled to assess the gain in performance from the parameter regionalization and its respective constraints and limitations. The parameter transfer within homogeneous regions outperformed the a priori parameters and led to a decrease in bias and increase in efficiency reaching a median NS of 0.77 and a NS of 0.85 at individual catchments. The use of FDC revealed the effect of bias on the inaccuracy of prediction from parameter transfer. In one specific region, of mountainous and forested catchments, the prediction accuracy of the parameter transfer was less satisfactory and equivalent to a priori parameters. In this region, the parameter transfer from the outsider catchment provided the best performance; less-biased with smaller uncertainty in medium flow percentiles (40%-60%). The large disparity of energy conditions explained the lack of performance from parameter transfer in this region. Besides, the subsurface stormflow is predominant and there is a likelihood of lateral preferential flow, which according to its specific properties further explained the reduced

  3. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....

  4. Importance of Foliar Nitrogen Concentration to Predict Forest Productivity in the Mid-Atlantic Region

    Science.gov (United States)

    Yude Pan; John Hom; Jennifer Jenkins; Richard Birdsey

    2004-01-01

    To assess what difference it might make to include spatially defined estimates of foliar nitrogen in the regional application of a forest ecosystem model (PnET-II), we composed model predictions of wood production from extensive ground-based forest inventory analysis data across the Mid-Atlantic region. Spatial variation in foliar N concentration was assigned based on...

  5. Predictive IP controller for robust position control of linear servo system.

    Science.gov (United States)

    Lu, Shaowu; Zhou, Fengxing; Ma, Yajie; Tang, Xiaoqi

    2016-07-01

    Position control is a typical application of linear servo system. In this paper, to reduce the system overshoot, an integral plus proportional (IP) controller is used in the position control implementation. To further improve the control performance, a gain-tuning IP controller based on a generalized predictive control (GPC) law is proposed. Firstly, to represent the dynamics of the position loop, a second-order linear model is used and its model parameters are estimated on-line by using a recursive least squares method. Secondly, based on the GPC law, an optimal control sequence is obtained by using receding horizon, then directly supplies the IP controller with the corresponding control parameters in the real operations. Finally, simulation and experimental results are presented to show the efficiency of proposed scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  6. Factors controlling the regional distribution of vanadium in ground water

    Science.gov (United States)

    Wright, Michael T.; Belitz, Kenneth

    2010-01-01

    Although the ingestion of vanadium (V) in drinking water may have possible adverse health effects, there have been relatively few studies of V in groundwater. Given the importance of groundwater as a source of drinking water in many areas of the world, this study examines the potential sources and geochemical processes that control the distribution of V in groundwater on a regional scale. Potential sources of V to groundwater include dissolution of V rich rocks, and waste streams from industrial processes. Geochemical processes such as adsorption/desorption, precipitation/dissolution, and chemical transformations control V concentrations in groundwater. Based on thermodynamic data and laboratory studies, V concentrations are expected to be highest in samples collected from oxic and alkaline groundwater. However, the extent to which thermodynamic data and laboratory results apply to the actual distribution of V in groundwater is not well understood. More than 8400 groundwater samples collected in California were used in this study. Of these samples, high (> or = 50 μg/L) and moderate (25 to 49 μg/L) V concentrations were most frequently detected in regions where both source rock and favorable geochemical conditions occurred. The distribution of V concentrations in groundwater samples suggests that significant sources of V are mafic and andesitic rock. Anthropogenic activities do not appear to be a significant contributor of V to groundwater in this study. High V concentrations in groundwater samples analyzed in this study were almost always associated with oxic and alkaline groundwater conditions, which is consistent with predictions based on thermodynamic data.

  7. Model predictive controller design of hydrocracker reactors

    OpenAIRE

    GÖKÇE, Dila

    2011-01-01

    This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

  8. Enhanced Voltage Control of VSC-HVDC Connected Offshore Wind Farms Based on Model Predictive Control

    DEFF Research Database (Denmark)

    Guo, Yifei; Gao, Houlei; Wu, Qiuwei

    2018-01-01

    This paper proposes an enhanced voltage control strategy (EVCS) based on model predictive control (MPC) for voltage source converter based high voltage direct current (VSCHVDC) connected offshore wind farms (OWFs). In the proposed MPC based EVCS, all wind turbine generators (WTGs) as well...... as the wind farm side VSC are optimally coordinated to keep voltages within the feasible range and reduce system power losses. Considering the high ratio of the OWF collector system, the effects of active power outputs of WTGs on voltage control are also taken into consideration. The predictive model of VSC...

  9. Nonlinear Predictive Sliding Mode Control for Active Suspension System

    Directory of Open Access Journals (Sweden)

    Dazhuang Wang

    2018-01-01

    Full Text Available An active suspension system is important in meeting the requirements of the ride comfort and handling stability for vehicles. In this work, a nonlinear model of active suspension system and a corresponding nonlinear robust predictive sliding mode control are established for the control problem of active suspension. Firstly, a seven-degree-of-freedom active suspension model is established considering the nonlinear effects of springs and dampers; and secondly, the dynamic model is expanded in the time domain, and the corresponding predictive sliding mode control is established. The uncertainties in the controller are approximated by the fuzzy logic system, and the adaptive controller reduces the approximation error to increase the robustness of the control system. Finally, the simulation results show that the ride comfort and handling stability performance of the active suspension system is better than that of the passive suspension system and the Skyhook active suspension. Thus, the system can obviously improve the shock absorption performance of vehicles.

  10. Generic Model Predictive Control Framework for Advanced Driver Assistance Systems

    NARCIS (Netherlands)

    Wang, M.

    2014-01-01

    This thesis deals with a model predictive control framework for control design of Advanced Driver Assistance Systems, where car-following tasks are under control. The framework is applied to design several autonomous and cooperative controllers and to examine the controller properties at the

  11. Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

    Directory of Open Access Journals (Sweden)

    Jin Xin

    2017-10-01

    Full Text Available This paper develops an offset free tracking model predictive control based on a dynamic partial least square (PLS framework. First, state space model is used as the inner model of PLS to describe the dynamic system, where subspace identification method is used to identify the inner model. Based on the obtained model, multiple independent model predictive control (MPC controllers are designed. Due to the decoupling character of PLS, these controllers are running separately, which is suitable for distributed control framework. In addition, the increment of inner model output is considered in the cost function of MPC, which involves integral action in the controller. Hence, the offset free tracking performance is guaranteed. The results of an industry background simulation demonstrate the effectiveness of proposed method.

  12. An estimator-based distributed voltage-predictive control strategy for ac islanded microgrids

    DEFF Research Database (Denmark)

    Wang, Yanbo; Chen, Zhe; Wang, Xiongfei

    2015-01-01

    This paper presents an estimator-based voltage predictive control strategy for AC islanded microgrids, which is able to perform voltage control without any communication facilities. The proposed control strategy is composed of a network voltage estimator and a voltage predictive controller for each...... and has a good capability to reject uncertain perturbations of islanded microgrids....

  13. The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

    Directory of Open Access Journals (Sweden)

    César Hernández-Hernández

    2017-06-01

    Full Text Available Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.

  14. Predictive control of irrigation canals – robust design and real-time implementation

    NARCIS (Netherlands)

    Aguilar, José V.; Langarita, Pedro; Rodellar, José; Linares, Lorenzo; Horváth, K.

    2016-01-01

    Predictive control is one of the most commonly used control methods in a variety of application areas, including hydraulic processes such as water distribution canals for irrigation. This article presents the design and application of predictive control for the water discharge entering into an

  15. Enhanced pid vs model predictive control applied to bldc motor

    Science.gov (United States)

    Gaya, M. S.; Muhammad, Auwal; Aliyu Abdulkadir, Rabiu; Salim, S. N. S.; Madugu, I. S.; Tijjani, Aminu; Aminu Yusuf, Lukman; Dauda Umar, Ibrahim; Khairi, M. T. M.

    2018-01-01

    BrushLess Direct Current (BLDC) motor is a multivariable and highly complex nonlinear system. Variation of internal parameter values with environment or reference signal increases the difficulty in controlling the BLDC effectively. Advanced control strategies (like model predictive control) often have to be integrated to satisfy the control desires. Enhancing or proper tuning of a conventional algorithm results in achieving the desired performance. This paper presents a performance comparison of Enhanced PID and Model Predictive Control (MPC) applied to brushless direct current motor. The simulation results demonstrated that the PSO-PID is slightly better than the PID and MPC in tracking the trajectory of the reference signal. The proposed scheme could be useful algorithms for the system.

  16. Nonlinear Economic Model Predictive Control Strategy for Active Smart Buildings

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.

    2016-01-01

    Nowadays, the development of advanced and innovative intelligent control techniques for energy management in buildings is a key issue within the smart grid topic. A nonlinear economic model predictive control (EMPC) scheme, based on the branch-and-bound tree search used as optimization algorithm ...... controller is shown very reliable keeping the comfort levels in the two considered seasons and shifting the load away from peak hours in order to achieve the desired flexible electricity consumption.......Nowadays, the development of advanced and innovative intelligent control techniques for energy management in buildings is a key issue within the smart grid topic. A nonlinear economic model predictive control (EMPC) scheme, based on the branch-and-bound tree search used as optimization algorithm...

  17. Robust multi-model predictive control of multi-zone thermal plate system

    Directory of Open Access Journals (Sweden)

    Poom Jatunitanon

    2018-02-01

    Full Text Available A modern controller was designed by using the mathematical model of a multi–zone thermal plate system. An important requirement for this type of controller is that it must be able to keep the temperature set-point of each thermal zone. The mathematical model used in the design was determined through a system identification process. The results showed that when the operating condition is changed, the performance of the controller may be reduced as a result of the system parameter uncertainties. This paper proposes a weighting technique of combining the robust model predictive controller for each operating condition into a single robust multi-model predictive control. Simulation and experimental results showed that the proposed method performed better than the conventional multi-model predictive control in rise time of transient response, when used in a system designed to work over a wide range of operating conditions.

  18. Network control principles predict neuron function in the Caenorhabditis elegans connectome

    Science.gov (United States)

    Yan, Gang; Vértes, Petra E.; Towlson, Emma K.; Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László

    2017-10-01

    Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

  19. Network control principles predict neuron function in the Caenorhabditis elegans connectome.

    Science.gov (United States)

    Yan, Gang; Vértes, Petra E; Towlson, Emma K; Chew, Yee Lian; Walker, Denise S; Schafer, William R; Barabási, Albert-László

    2017-10-26

    Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

  20. 40 CFR 81.38 - Metropolitan Houston-Galveston Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Metropolitan Houston-Galveston... Designation of Air Quality Control Regions § 81.38 Metropolitan Houston-Galveston Intrastate Air Quality Control Region. The Metropolitan Houston-Galveston Intrastate Air Quality Control Region (Texas) has been...

  1. SCADA system with predictive controller applied to irrigation canals

    OpenAIRE

    Figueiredo, João; Botto, Miguel; Rijo, Manuel

    2013-01-01

    This paper applies a model predictive controller (MPC) to an automatic water canal with sensors and actuators controlled by a network (programmable logic controller), and supervised by a SCADA system (supervisory control and a data acquisition). This canal is composed by a set of distributed sub-systems that control the water level in each canal pool, constrained by discharge gates (control variables) and water off-takes (disturbances). All local controllers are available through an industria...

  2. Hourly Wind Speed Interval Prediction in Arid Regions

    Science.gov (United States)

    Chaouch, M.; Ouarda, T.

    2013-12-01

    The long and extended warm and dry summers, the low rate of rain and humidity are the main factors that explain the increase of electricity consumption in hot arid regions. In such regions, the ventilating and air-conditioning installations, that are typically the most energy-intensive among energy consumption activities, are essential for securing healthy, safe and suitable indoor thermal conditions for building occupants and stored materials. The use of renewable energy resources such as solar and wind represents one of the most relevant solutions to overcome the increase of the electricity demand challenge. In the recent years, wind energy is gaining more importance among the researchers worldwide. Wind energy is intermittent in nature and hence the power system scheduling and dynamic control of wind turbine requires an estimate of wind energy. Accurate forecast of wind speed is a challenging task for the wind energy research field. In fact, due to the large variability of wind speed caused by the unpredictable and dynamic nature of the earth's atmosphere, there are many fluctuations in wind power production. This inherent variability of wind speed is the main cause of the uncertainty observed in wind power generation. Furthermore, producing wind power forecasts might be obtained indirectly by modeling the wind speed series and then transforming the forecasts through a power curve. Wind speed forecasting techniques have received substantial attention recently and several models have been developed. Basically two main approaches have been proposed in the literature: (1) physical models such as Numerical Weather Forecast and (2) statistical models such as Autoregressive integrated moving average (ARIMA) models, Neural Networks. While the initial focus in the literature has been on point forecasts, the need to quantify forecast uncertainty and communicate the risk of extreme ramp events has led to an interest in producing probabilistic forecasts. In short term

  3. Control System Design for Cylindrical Tank Process Using Neural Model Predictive Control Technique

    Directory of Open Access Journals (Sweden)

    M. Sridevi

    2010-10-01

    Full Text Available Chemical manufacturing and process industry requires innovative technologies for process identification. This paper deals with model identification and control of cylindrical process. Model identification of the process was done using ARMAX technique. A neural model predictive controller was designed for the identified model. The performance of the controllers was evaluated using MATLAB software. The performance of NMPC controller was compared with Smith Predictor controller and IMC controller based on rise time, settling time, overshoot and ISE and it was found that the NMPC controller is better suited for this process.

  4. Preschool Inhibitory Control Predicts ADHD Group Status and Inhibitory Weakness in School.

    Science.gov (United States)

    Jacobson, Lisa A; Schneider, Heather; Mahone, E Mark

    2017-12-26

    Discriminative utility of performance measures of inhibitory control was examined in preschool children with and without ADHD to determine whether performance measures added to diagnostic prediction and to prediction of informant-rated day-to-day executive function. Children ages 4-5 years (N = 105, 61% boys; 54 ADHD, medication-naïve) were assessed using performance measures (Auditory Continuous Performance Test for Preschoolers-Commission errors, Conflicting Motor Response Test, NEPSY Statue) and caregiver (parent, teacher) ratings of inhibition (Behavior Rating Inventory of Executive Function-Preschool version). Performance measures and parent and teacher reports of inhibitory control significantly and uniquely predicted ADHD group status; however, performance measures did not add to prediction of group status beyond parent reports. Performance measures did significantly predict classroom inhibitory control (teacher ratings), over and above parent reports of inhibitory control. Performance measures of inhibitory control may be adequate predictors of ADHD status and good predictors of young children's classroom inhibitory control, demonstrating utility as components of clinical assessments. © The Author(s) 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  5. Automatic Power Control for Daily Load-following Operation using Model Predictive Control Method

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Keuk Jong; Kim, Han Gon [KH, Daejeon (Korea, Republic of)

    2009-10-15

    Under the circumstances that nuclear power occupies more than 50%, nuclear power plants are required to be operated on load-following operation in order to make the effective management of electric grid system and enhanced responsiveness to rapid changes in power demand. Conventional reactors such as the OPR1000 and APR1400 have a regulating system that controls the average temperature of the reactor core relation to the reference temperature. This conventional method has the advantages of proven technology and ease of implementation. However, this method is unsuitable for controlling the axial power shape, particularly the load following operation. Accordingly, this paper reports on the development of a model predictive control method which is able to control the reactor power and the axial shape index. The purpose of this study is to analyze the behavior of nuclear reactor power and the axial power shape by using a model predictive control method when the power is increased and decreased for a daily load following operation. The study confirms that deviations in the axial shape index (ASI) are within the operating limit.

  6. LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines.

    Science.gov (United States)

    Xu, Jingting; Hu, Hong; Dai, Yang

    The identification of enhancers is a challenging task. Various types of epigenetic information including histone modification have been utilized in the construction of enhancer prediction models based on a diverse panel of machine learning schemes. However, DNA methylation profiles generated from the whole genome bisulfite sequencing (WGBS) have not been fully explored for their potential in enhancer prediction despite the fact that low methylated regions (LMRs) have been implied to be distal active regulatory regions. In this work, we propose a prediction framework, LMethyR-SVM, using LMRs identified from cell-type-specific WGBS DNA methylation profiles and a weighted support vector machine learning framework. In LMethyR-SVM, the set of cell-type-specific LMRs is further divided into three sets: reliable positive, like positive and likely negative, according to their resemblance to a small set of experimentally validated enhancers in the VISTA database based on an estimated non-parametric density distribution. Then, the prediction model is obtained by solving a weighted support vector machine. We demonstrate the performance of LMethyR-SVM by using the WGBS DNA methylation profiles derived from the human embryonic stem cell type (H1) and the fetal lung fibroblast cell type (IMR90). The predicted enhancers are highly conserved with a reasonable validation rate based on a set of commonly used positive markers including transcription factors, p300 binding and DNase-I hypersensitive sites. In addition, we show evidence that the large fraction of the LMethyR-SVM predicted enhancers are not predicted by ChromHMM in H1 cell type and they are more enriched for the FANTOM5 enhancers. Our work suggests that low methylated regions detected from the WGBS data are useful as complementary resources to histone modification marks in developing models for the prediction of cell-type-specific enhancers.

  7. A multicontroller structure for teaching and designing predictive control strategies

    International Nuclear Information System (INIS)

    Hodouin, D.; Desbiens, A.

    1999-01-01

    The paper deals with the unification of the existing linear control algorithms in order to facilitate their transfer to the engineering students and to industry's engineers. The resulting control algorithm is the Global Predictive Control (GlobPC), which is now taught at the graduate and continuing education levels. GlobPC is based on an internal model framework where three independent control criteria are minimized: one for tracking, one for regulation and one for feedforward. This structure allows to obtain desired tracking, regulation and feedforward behaviors in an optimal way while keeping them perfectly separated. It also cleanly separates the deterministic and stochastic predictions of the process model output. (author)

  8. Stochastic Predictive Control of Multi-Microgrid Systems

    DEFF Research Database (Denmark)

    Bazmohammadi, Najmeh; Tahsiri, Ahmadreza; Anvari-Moghaddam, Amjad

    2018-01-01

    This paper presents a stochastic predictive control algorithm for a number of microgrids connected to the same distribution system. Each microgrid includes a variety of distributed resources such as wind turbine, photo voltaic units, energy storage devices and loads. Considering the uncertainty...

  9. 40 CFR 81.63 - Metropolitan Fort Smith Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Metropolitan Fort Smith Interstate Air... Air Quality Control Regions § 81.63 Metropolitan Fort Smith Interstate Air Quality Control Region. The Metropolitan Fort Smith Interstate Air Quality Control Region (Arkansas-Oklahoma) has been revised to consist...

  10. Model Predictive Control for Offset-Free Reference Tracking

    Czech Academy of Sciences Publication Activity Database

    Belda, Květoslav

    2016-01-01

    Roč. 5, č. 1 (2016), s. 8-13 ISSN 1805-3386 Institutional support: RVO:67985556 Keywords : offset-free reference tracking * predictive control * ARX model * state-space model * multi-input multi-output system * robotic system * mechatronic system Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2016/AS/belda-0458355.pdf

  11. Research on frequency control strategy of interconnected region based on fuzzy PID

    Science.gov (United States)

    Zhang, Yan; Li, Chunlan

    2018-05-01

    In order to improve the frequency control performance of the interconnected power grid, overcome the problems of poor robustness and slow adjustment of traditional regulation, the paper puts forward a frequency control method based on fuzzy PID. The method takes the frequency deviation and tie-line deviation of each area as the control objective, takes the regional frequency deviation and its deviation as input, and uses fuzzy mathematics theory, adjusts PID control parameters online. By establishing the regional frequency control model of water-fire complementary power generation in MATLAB, the regional frequency control strategy is given, and three control modes (TBC-FTC, FTC-FTC, FFC-FTC) are simulated and analyzed. The simulation and experimental results show that, this method has better control performance compared with the traditional regional frequency regulation.

  12. Model predictive control of a crude oil distillation column

    Directory of Open Access Journals (Sweden)

    Morten Hovd

    1999-04-01

    Full Text Available The project of designing and implementing model based predictive control on the vacuum distillation column at the Nynäshamn Refinery of Nynäs AB is described in this paper. The paper describes in detail the modeling for the model based control, covers the controller implementation, and documents the benefits gained from the model based controller.

  13. Plant control using embedded predictive models

    International Nuclear Information System (INIS)

    Godbole, S.S.; Gabler, W.E.; Eschbach, S.L.

    1990-01-01

    B and W recently undertook the design of an advanced light water reactor control system. A concept new to nuclear steam system (NSS) control was developed. The concept, which is called the Predictor-Corrector, uses mathematical models of portions of the controlled NSS to calculate, at various levels within the system, demand and control element position signals necessary to satisfy electrical demand. The models give the control system the ability to reduce overcooling and undercooling of the reactor coolant system during transients and upsets. Two types of mathematical models were developed for use in designing and testing the control system. One model was a conventional, comprehensive NSS model that responds to control system outputs and calculates the resultant changes in plant variables that are then used as inputs to the control system. Two other models, embedded in the control system, were less conventional, inverse models. These models accept as inputs plant variables, equipment states, and demand signals and predict plant operating conditions and control element states that will satisfy the demands. This paper reports preliminary results of closed-loop Reactor Coolant (RC) pump trip and normal load reduction testing of the advanced concept. Results of additional transient testing, and of open and closed loop stability analyses will be reported as they are available

  14. Multivariable predictive control considering time delay for load-frequency control in multi-area power systems

    Directory of Open Access Journals (Sweden)

    Daniar Sabah

    2016-12-01

    Full Text Available In this paper, a multivariable model based predictive control (MPC is proposed for the solution of load frequency control (LFC in a multi-area interconnected power system. The proposed controller is designed to consider time delay, generation rate constraint and multivariable nature of the LFC system, simultaneously. A new formulation of the MPC is presented to compensate time delay. The generation rate constraint is considered by employing a constrained MPC and economic allocation of the generation is further guaranteed by an innovative modification in the predictive control objective function. The effectiveness of proposed scheme is verified through time-based simulations on the standard 39-bus test system and the responses are then compared with the proportional-integral controller. The evaluation of the results reveals that the proposed control scheme offers satisfactory performance with fast responses.

  15. Distributed Model Predictive Control via Dual Decomposition

    DEFF Research Database (Denmark)

    Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle

    2014-01-01

    This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...

  16. REALIGNED MODEL PREDICTIVE CONTROL OF A PROPYLENE DISTILLATION COLUMN

    Directory of Open Access Journals (Sweden)

    A. I. Hinojosa

    Full Text Available Abstract In the process industry, advanced controllers usually aim at an economic objective, which usually requires closed-loop stability and constraints satisfaction. In this paper, the application of a MPC in the optimization structure of an industrial Propylene/Propane (PP splitter is tested with a controller based on a state space model, which is suitable for heavily disturbed environments. The simulation platform is based on the integration of the commercial dynamic simulator Dynsim® and the rigorous steady-state optimizer ROMeo® with the real-time facilities of Matlab. The predictive controller is the Infinite Horizon Model Predictive Control (IHMPC, based on a state-space model that that does not require the use of a state observer because the non-minimum state is built with the past inputs and outputs. The controller considers the existence of zone control of the outputs and optimizing targets for the inputs. We verify that the controller is efficient to control the propylene distillation system in a disturbed scenario when compared with a conventional controller based on a state observer. The simulation results show a good performance in terms of stability of the controller and rejection of large disturbances in the composition of the feed of the propylene distillation column.

  17. Predictability of solute transport in diffusion-controlled hydrogeologic regimes

    International Nuclear Information System (INIS)

    Gillham, R.W.; Cherry, J.A.

    1983-01-01

    Hydrogeologic regimes that are favourable for the subsurface management of low-level radioactive wastes must have transport properties that will limit the migration velocity of contaminants to some acceptably low value. Of equal importance, for the purpose of impact assessment and licensing, is the need to be able to predict, with a reasonable degree of certainty and over long time periods, what the migration velocity of the various contaminants of interest will be. This paper presents arguments to show that in addition to having favourable velocity characteristics, transport in saturated, diffusion-controlled hydrogeologic regimes is considerably more predictable than in the most common alternatives. The classical transport models for unsaturated, saturated-advection-controlled and saturated-diffusion-controlled environments are compared, with particular consideration being given to the difficulties associated with the characterization of the respective transport parameters. Results are presented which show that the diffusion of non-reactive solutes and solutes that react according to a constant partitioning ratio (K/sub d/) are highly predictable under laboratory conditions and that the diffusion coefficients for the reactive solutes can be determined with a reasonable degree of accuracy from independent measurements of bulk density, porosity, distribution coefficient and tortuosity. Field evidence is presented which shows that the distribution of environmental isotopes and chloride in thick clayey deposits is consistent with a diffusion-type transport process in these media. These results are particularly important in that they not only demonstrate the occurrence of diffusion-controlled hydrogeologic regimes, but they also demonstrate the predictability of the migration characteristics over very long time periods

  18. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....

  19. Control and prediction components of movement planning in stuttering vs. nonstuttering adults

    Science.gov (United States)

    Daliri, Ayoub; Prokopenko, Roman A.; Flanagan, J. Randall; Max, Ludo

    2014-01-01

    Purpose Stuttering individuals show speech and nonspeech sensorimotor deficiencies. To perform accurate movements, the sensorimotor system needs to generate appropriate control signals and correctly predict their sensory consequences. Using a reaching task, we examined the integrity of these control and prediction components, separately, for movements unrelated to the speech motor system. Method Nine stuttering and nine nonstuttering adults made fast reaching movements to visual targets while sliding an object under the index finger. To quantify control, we determined initial direction error and end-point error. To quantify prediction, we calculated the correlation between vertical and horizontal forces applied to the object—an index of how well vertical force (preventing slip) anticipated direction-dependent variations in horizontal force (moving the object). Results Directional and end-point error were significantly larger for the stuttering group. Both groups performed similarly in scaling vertical force with horizontal force. Conclusions The stuttering group's reduced reaching accuracy suggests limitations in generating control signals for voluntary movements, even for non-orofacial effectors. Typical scaling of vertical force with horizontal force suggests an intact ability to predict the consequences of planned control signals. Stuttering may be associated with generalized deficiencies in planning control signals rather than predicting the consequences of those signals. PMID:25203459

  20. Force Control for a Pneumatic Cylinder Using Generalized Predictive Controller Approach

    Directory of Open Access Journals (Sweden)

    Ahmad ’Athif Mohd Faudzi

    2014-01-01

    Full Text Available Pneumatic cylinder is a well-known device because of its high power to weight ratio, easy use, and environmental safety. Pneumatic cylinder uses air as its power source and converts it to a possible movement such as linear and rotary movement. In order to control the pneumatic cylinder, controller algorithm is needed to control the on-off solenoid valve with encoder and pressure sensor as the feedback inputs. In this paper, generalized predictive controller (GPC is proposed as the control strategy for the pneumatic cylinder force control. To validate and compare the performance, proportional-integral (PI controller is also presented. Both controllers algorithms GPC and PI are developed using existing linear model of the cylinder from previous research. Results are presented in simulation and experimental approach using MATLAB-Simulink as the platform. The results show that the GPC is capable of fast response with low steady state error and percentage overshoot compared to PI.

  1. 40 CFR 81.76 - State of Hawaii Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false State of Hawaii Air Quality Control... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.76 State of Hawaii Air Quality Control Region. The State of Hawaii Air Quality...

  2. Prediction-Based Control for Nonlinear Systems with Input Delay

    Directory of Open Access Journals (Sweden)

    I. Estrada-Sánchez

    2017-01-01

    Full Text Available This work has two primary objectives. First, it presents a state prediction strategy for a class of nonlinear Lipschitz systems subject to constant time delay in the input signal. As a result of a suitable change of variable, the state predictor asymptotically provides the value of the state τ units of time ahead. Second, it proposes a solution to the stabilization and trajectory tracking problems for the considered class of systems using predicted states. The predictor-controller convergence is proved by considering a complete Lyapunov functional. The proposed predictor-based controller strategy is evaluated using numerical simulations.

  3. Analytic Model Predictive Control of Uncertain Nonlinear Systems: A Fuzzy Adaptive Approach

    Directory of Open Access Journals (Sweden)

    Xiuyan Peng

    2015-01-01

    Full Text Available A fuzzy adaptive analytic model predictive control method is proposed in this paper for a class of uncertain nonlinear systems. Specifically, invoking the standard results from the Moore-Penrose inverse of matrix, the unmatched problem which exists commonly in input and output dimensions of systems is firstly solved. Then, recurring to analytic model predictive control law, combined with fuzzy adaptive approach, the fuzzy adaptive predictive controller synthesis for the underlying systems is developed. To further reduce the impact of fuzzy approximation error on the system and improve the robustness of the system, the robust compensation term is introduced. It is shown that by applying the fuzzy adaptive analytic model predictive controller the rudder roll stabilization system is ultimately uniformly bounded stabilized in the H-infinity sense. Finally, simulation results demonstrate the effectiveness of the proposed method.

  4. Pinning-controlled synchronization of complex networks with bounded or unbounded synchronized regions

    International Nuclear Information System (INIS)

    Yan-Li, Zou; Guan-Rong, Chen

    2009-01-01

    This paper studies pinning-controlled synchronization of complex networks with bounded or unbounded synchronized regions. To study a state-feedback pinning-controlled network with N nodes, it first converts the controlled network to an extended network of N+1 nodes without controls. It is shown that the controlled synchronizability of the given network is determined by the real part of the smallest nonzero eigenvalue of the coupling matrix of its extended network when the synchronized region is unbounded; but it is determined by the ratio of the real parts of the largest and the smallest nonzero eigenvalues of the coupling matrix when the synchronized region is bounded. Both theoretical analysis and numerical simulation show that the portion of controlled nodes has no critical values when the synchronized region is unbounded, but it has a critical value when the synchronized region is bounded. In the former case, therefore, it is possible to control the network to achieve synchronization by pinning only one node. In the latter case, the network can achieve controlled synchronization only when the portion of controlled nodes is larger than the critical value. (general)

  5. Profile control simulations and experiments on TCV : A controller test environment and results using a model-based predictive controller

    NARCIS (Netherlands)

    Maljaars, E.; Felici, F.; Blanken, T.C.; Galperti, C.; Sauter, O.; de Baar, M.R.; Carpanese, F.; Goodman, T.P.; Kim, D.; Kim, S.H.; Kong, M.G.; Mavkov, B.; Merle, A.; Moret, J.M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A.A.; Vu, N.M.T.

    2017-01-01

    The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety

  6. Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

    NARCIS (Netherlands)

    Maljaars, B.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J.; Nouailletas, R.; Scheffer, M.; Teplukhina, A.; Vu, T.

    2017-01-01

    The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety

  7. Low trait self-control predicts self-handicapping.

    Science.gov (United States)

    Uysal, Ahmet; Knee, C Raymond

    2012-02-01

    Past research has shown that self-handicapping stems from uncertainty about one's ability and self-presentational concerns. The present studies suggest that low dispositional self-control is also associated with self-handicapping. In 3 studies (N = 289), the association between self-control and self-handicapping was tested. Self-control was operationalized as trait self-control, whereas self-handicapping was operationalized as trait self-handicapping in Study 1 (N = 160), self-reported self-handicapping in Study 2 (N = 74), and behavioral self-handicapping in Study 3 (N = 55). In all 3 studies, hierarchical regression analyses revealed that low self-control predicts self-handicapping, independent of self-esteem, self-doubt, social desirability, and gender. © 2012 The Authors. Journal of Personality © 2012, Wiley Periodicals, Inc.

  8. Effective and Robust Generalized Predictive Speed Control of Induction Motor

    Directory of Open Access Journals (Sweden)

    Patxi Alkorta

    2013-01-01

    Full Text Available This paper presents and validates a new proposal for effective speed vector control of induction motors based on linear Generalized Predictive Control (GPC law. The presented GPC-PI cascade configuration simplifies the design with regard to GPC-GPC cascade configuration, maintaining the advantages of the predictive control algorithm. The robust stability of the closed loop system is demonstrated by the poles placement method for several typical cases of uncertainties in induction motors. The controller has been tested using several simulations and experiments and has been compared with Proportional Integral Derivative (PID and Sliding Mode (SM control schemes, obtaining outstanding results in speed tracking even in the presence of parameter uncertainties, unknown load disturbance, and measurement noise in the loop signals, suggesting its use in industrial applications.

  9. The ordered network structure and prediction summary for M ≥ 7 earthquakes in Xinjiang region of China

    International Nuclear Information System (INIS)

    Men, Ke-Pei; Zhao, Kai

    2014-01-01

    M ≥ 7 earthquakes have showed an obvious commensurability and orderliness in Xinjiang of China and its adjacent region since 1800. The main orderly values are 30 a x k (k = 1, 2, 3), 11 ∝ 12 a, 41 ∝ 43 a, 18 ∝ 19 a, and 5 ∝ 6 a. In the guidance of the information forecasting theory of Wen-Bo Weng, based on previous research results, combining ordered network structure analysis with complex network technology, we focus on the prediction summary of M ≥ 7 earthquakes by using the ordered network structure, and add new information to further optimize network, hence construct the 2D- and 3D-ordered network structure of M ≥ 7 earthquakes. In this paper, the network structure revealed fully the regularity of seismic activity of M ≥ 7 earthquakes in the study region during the past 210 years. Based on this, the Karakorum M7.1 earthquake in 1996, the M7.9 earthquake on the frontier of Russia, Mongol, and China in 2003, and two Yutian M7.3 earthquakes in 2008 and 2014 were predicted successfully. At the same time, a new prediction opinion is presented that the future two M ≥ 7 earthquakes will probably occur around 2019-2020 and 2025-2026 in this region. The results show that large earthquake occurred in defined region can be predicted. The method of ordered network structure analysis produces satisfactory results for the mid-and-long term prediction of M ≥ 7 earthquakes.

  10. Model Predictive Control for Load Frequency Control with Wind Turbines

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2015-01-01

    Full Text Available Reliable load frequency (LFC control is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control (DMPC based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The scheme incorporates the two critical nonlinear constraints, for example, the generation rate constraint (GRC and the valve limit, into convex optimization problems. Furthermore, the algorithm reduces the impact on the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and that without the participation of the wind turbines is carried out. Good performance is obtained in the presence of power system nonlinearities due to the governors and turbines constraints and load change disturbances.

  11. Prediction based active ramp metering control strategy with mobility and safety assessment

    Science.gov (United States)

    Fang, Jie; Tu, Lili

    2018-04-01

    Ramp metering is one of the most direct and efficient motorway traffic flow management measures so as to improve traffic conditions. However, owing to short of traffic conditions prediction, in earlier studies, the impact on traffic flow dynamics of the applied RM control was not quantitatively evaluated. In this study, a RM control algorithm adopting Model Predictive Control (MPC) framework to predict and assess future traffic conditions, which taking both the current traffic conditions and the RM-controlled future traffic states into consideration, was presented. The designed RM control algorithm targets at optimizing the network mobility and safety performance. The designed algorithm is evaluated in a field-data-based simulation. Through comparing the presented algorithm controlled scenario with the uncontrolled scenario, it was proved that the proposed RM control algorithm can effectively relieve the congestion of traffic network with no significant compromises in safety aspect.

  12. Robust predictive control of a gasoline debutanizer column

    Directory of Open Access Journals (Sweden)

    E. Almeida Neto

    2000-12-01

    Full Text Available This paper studies the application of Model Predictive Control to moderately nonlinear processes. The system used in this work is an industrial gasoline debutanizer column. The paper presents two new formulations of MPC: MMPC (Multi-Model Predictive Controller and RSMPC (Robust Stable MPC. The approach is based on the concepts of Linear Matrix Inequalities (LMI, which have been recently introduced in the MPC field. Model uncertainty is considered by assuming that the true process model belongs to a convex set (polytope of possible plants. The controller has guaranteed stability when a Lyapunov type inequality constraint is included in the MPC problem. In the debutanizer column, several nonlinearities are present in the advanced control level when the manipulated inputs are the reflux flow and the reboiler heat duty. In most cases the controlled outputs are the contents of C5+ (pentane and heavier hydrocarbons in the LPG (Liquefied Petroleum Gas and the gasoline vapor pressure (P VR. In this case the QDMC algorithm which is usually applied to the debutanizer column has a poor performance and stability problems reflected in an oscillatory behavior of the process. The new approach considers several process models representing different operating conditions where linear models are identified. The results presented here show that the multimodel controller is capable of controlling the process in the entire operating window while the conventional MPC has a limited operating range.

  13. Model Predictive Control of a Nonlinear System with Known Scheduling Variable

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2012-01-01

    Model predictive control (MPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Consequently...... the control problem of the nonlinear system is simplied into a quadratic programming. Wind turbine is chosen as the case study and we choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon....

  14. Coordinated Voltage Control of a Wind Farm based on Model Predictive Control

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai

    2016-01-01

    This paper presents an autonomous wind farm voltage controller based on Model Predictive Control (MPC). The reactive power compensation and voltage regulation devices of the wind farm include Static Var Compensators (SVCs), Static Var Generators (SVGs), Wind Turbine Generators (WTGs) and On...... are calculated based on an analytical method to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both voltage violated and normal operation conditions. A wind farm with 20 wind turbines was used to conduct case studies to verify the proposed coordinated...

  15. Predictive access control for distributed computation

    DEFF Research Database (Denmark)

    Yang, Fan; Hankin, Chris; Nielson, Flemming

    2013-01-01

    We show how to use aspect-oriented programming to separate security and trust issues from the logical design of mobile, distributed systems. The main challenge is how to enforce various types of security policies, in particular predictive access control policies — policies based on the future beh...... behavior of a program. A novel feature of our approach is that we can define policies concerning secondary use of data....

  16. Predictive control strategies for an indirect matrix converter operating at fixed switching frequency

    DEFF Research Database (Denmark)

    Rivera, M.; Nasir, U.; Tarisciotti, L.

    2017-01-01

    The classic model predictive control presents a variable switching frequency which could produce high ripple in the controlled waveforms or resonances in the input filter of the matrix converter, affecting the performance of the system. This paper presents two model predictive control strategies...

  17. Comparison of H-infinity control and generalized predictive control for a laser scanner system

    DEFF Research Database (Denmark)

    Ordys, A.W.; Stoustrup, Jakob; Smillie, I.

    2000-01-01

    This paper describes tests performed on a laser scanner system to assess the feasibility of H-infinity control and generalized predictive control design techniques in achieving a required performance in a trajectory folowing problem. The two methods are compared with respect to achieved scan times...

  18. Comparison Analysis of Model Predictive Controller with Classical PID Controller For pH Control Process

    Directory of Open Access Journals (Sweden)

    V. Balaji

    2016-12-01

    Full Text Available pH control plays a important role in any chemical plant and process industries. For the past four decades the classical PID controller has been occupied by the industries. Due to the faster computing   technology in the industry demands a tighter advanced control strategy. To fulfill the needs and requirements Model Predictive Control (MPC is the best among all the advanced control algorithms available in the present scenario. The study and analysis has been done for First Order plus Delay Time (FOPDT model controlled by Proportional Integral Derivative (PID and MPC using the Matlab software. This paper explores the capability of the MPC strategy, analyze and compare the control effects with conventional control strategy in pH control. A comparison results between the PID and MPC is plotted using the software. The results clearly show that MPC provide better performance than the classical controller.

  19. Model Predictive Control for Linear Complementarity and Extended Linear Complementarity Systems

    Directory of Open Access Journals (Sweden)

    Bambang Riyanto

    2005-11-01

    Full Text Available In this paper, we propose model predictive control method for linear complementarity and extended linear complementarity systems by formulating optimization along prediction horizon as mixed integer quadratic program. Such systems contain interaction between continuous dynamics and discrete event systems, and therefore, can be categorized as hybrid systems. As linear complementarity and extended linear complementarity systems finds applications in different research areas, such as impact mechanical systems, traffic control and process control, this work will contribute to the development of control design method for those areas as well, as shown by three given examples.

  20. Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. II. Predicting wind components

    Energy Technology Data Exchange (ETDEWEB)

    Kamp, Derek van der [University of Victoria, Pacific Climate Impacts Consortium, Victoria, BC (Canada); University of Victoria, School of Earth and Ocean Sciences, Victoria, BC (Canada); Curry, Charles L. [Environment Canada University of Victoria, Canadian Centre for Climate Modelling and Analysis, Victoria, BC (Canada); University of Victoria, School of Earth and Ocean Sciences, Victoria, BC (Canada); Monahan, Adam H. [University of Victoria, School of Earth and Ocean Sciences, Victoria, BC (Canada)

    2012-04-15

    A regression-based downscaling technique was applied to monthly mean surface wind observations from stations throughout western Canada as well as from buoys in the Northeast Pacific Ocean over the period 1979-2006. A predictor set was developed from principal component analysis of the three wind components at 500 hPa and mean sea-level pressure taken from the NCEP Reanalysis II. Building on the results of a companion paper, Curry et al. (Clim Dyn 2011), the downscaling was applied to both wind speed and wind components, in an effort to evaluate the utility of each type of predictand. Cross-validated prediction skill varied strongly with season, with autumn and summer displaying the highest and lowest skill, respectively. In most cases wind components were predicted with better skill than wind speeds. The predictive ability of wind components was found to be strongly related to their orientation. Wind components with the best predictions were often oriented along topographically significant features such as constricted valleys, mountain ranges or ocean channels. This influence of directionality on predictive ability is most prominent during autumn and winter at inland sites with complex topography. Stations in regions with relatively flat terrain (where topographic steering is minimal) exhibit inter-station consistencies including region-wide seasonal shifts in the direction of the best predicted wind component. The conclusion that wind components can be skillfully predicted only over a limited range of directions at most stations limits the scope of statistically downscaled wind speed predictions. It seems likely that such limitations apply to other regions of complex terrain as well. (orig.)

  1. Model Predictive Control of Buoy Type Wave Energy Converter

    DEFF Research Database (Denmark)

    Soltani, Mohsen N.; Sichani, Mahdi T.; Mirzaei, Mahmood

    2014-01-01

    by forcing this condition. In the paper the theoretical framework for this principal is shown. The optimal controller requires information of the sea state for infinite horizon which is not applicable. Model Predictive Controllers (MPC) can have finite horizon which crosses out this requirement....... This approach is then taken into account and an MPC controller is designed for a model wave energy converter and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller....

  2. Model Predictive Control of Buoy Type Wave Energy Converter

    DEFF Research Database (Denmark)

    Soltani, Mohsen; Sichani, Mahdi Teimouri; Mirzaei, Mahmood

    2014-01-01

    by forcing this condition. In the paper the theoretical framework for this principal is shown. The optimal controller requires information of the sea state for infinite horizon which is not applicable. Model Predictive Controllers (MPC) can have finite horizon which crosses out this requirement....... This approach is then taken into account and an MPC controller is designed for a model WEC and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller....

  3. Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

    DEFF Research Database (Denmark)

    Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon

    2008-01-01

    This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...

  4. NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES

    Directory of Open Access Journals (Sweden)

    SILVA R. G.

    1999-01-01

    Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.

  5. Electric vehicle charge planning using Economic Model Predictive Control

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Poulsen, Niels K.; Madsen, Henrik

    2012-01-01

    Economic Model Predictive Control (MPC) is very well suited for controlling smart energy systems since electricity price and demand forecasts are easily integrated in the controller. Electric vehicles (EVs) are expected to play a large role in the future Smart Grid. They are expected to provide...... grid services, both for peak reduction and for ancillary services, by absorbing short term variations in the electricity production. In this paper the Economic MPC minimizes the cost of electricity consumption for a single EV. Simulations show savings of 50–60% of the electricity costs compared...... to uncontrolled charging from load shifting based on driving pattern predictions. The future energy system in Denmark will most likely be based on renewable energy sources e.g. wind and solar power. These green energy sources introduce stochastic fluctuations in the electricity production. Therefore, energy...

  6. Tuning SISO offset-free Model Predictive Control based on ARX models

    DEFF Research Database (Denmark)

    Huusom, Jakob Kjøbsted; Poulsen, Niels Kjølstad; Jørgensen, Sten Bay

    2012-01-01

    , the proposed controller is simple to tune as it has only one free tuning parameter. These two features are advantageous in predictive process control as they simplify industrial commissioning of MPC. Disturbance rejection and offset-free control is important in industrial process control. To achieve offset......In this paper, we present a tuning methodology for a simple offset-free SISO Model Predictive Controller (MPC) based on autoregressive models with exogenous inputs (ARX models). ARX models simplify system identification as they can be identified from data using convex optimization. Furthermore......-free control in face of unknown disturbances or model-plant mismatch, integrators must be introduced in either the estimator or the regulator. Traditionally, offset-free control is achieved using Brownian disturbance models in the estimator. In this paper we achieve offset-free control by extending the noise...

  7. Analysis of explicit model predictive control for path-following control.

    Science.gov (United States)

    Lee, Junho; Chang, Hyuk-Jun

    2018-01-01

    In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration.

  8. Analysis of explicit model predictive control for path-following control

    Science.gov (United States)

    2018-01-01

    In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration. PMID:29534080

  9. Predictive control strategies for wind turbine system based on permanent magnet synchronous generator.

    Science.gov (United States)

    Maaoui-Ben Hassine, Ikram; Naouar, Mohamed Wissem; Mrabet-Bellaaj, Najiba

    2016-05-01

    In this paper, Model Predictive Control and Dead-beat predictive control strategies are proposed for the control of a PMSG based wind energy system. The proposed MPC considers the model of the converter-based system to forecast the possible future behavior of the controlled variables. It allows selecting the voltage vector to be applied that leads to a minimum error by minimizing a predefined cost function. The main features of the MPC are low current THD and robustness against parameters variations. The Dead-beat predictive control is based on the system model to compute the optimum voltage vector that ensures zero-steady state error. The optimum voltage vector is then applied through Space Vector Modulation (SVM) technique. The main advantages of the Dead-beat predictive control are low current THD and constant switching frequency. The proposed control techniques are presented and detailed for the control of back-to-back converter in a wind turbine system based on PMSG. Simulation results (under Matlab-Simulink software environment tool) and experimental results (under developed prototyping platform) are presented in order to show the performances of the considered control strategies. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  10. Model predictive control in light naphtha distillation column of gasoline hydrogenation process

    Directory of Open Access Journals (Sweden)

    Kornkrit Chiewchanchairat

    2015-03-01

    Full Text Available The main scope of this research is for designing and implementing of model predictive control (MPC on the light naphtha distillation column of gasoline hydrogenation process. This model is designed by using robust multivariable predictive control technology (RMPCT. The performance of MPC controller is better than PID controllers 32.1 % those are comparing by using as the same of objective function and also in the MPC controller can be used for steam optimization that is shown in this research, stream consumption is reduced 6.6 Kg/ m3 of fresh feed.

  11. Investigation of energy management strategies for photovoltaic systems - A predictive control algorithm

    Science.gov (United States)

    Cull, R. C.; Eltimsahy, A. H.

    1983-01-01

    The present investigation is concerned with the formulation of energy management strategies for stand-alone photovoltaic (PV) systems, taking into account a basic control algorithm for a possible predictive, (and adaptive) controller. The control system controls the flow of energy in the system according to the amount of energy available, and predicts the appropriate control set-points based on the energy (insolation) available by using an appropriate system model. Aspects of adaptation to the conditions of the system are also considered. Attention is given to a statistical analysis technique, the analysis inputs, the analysis procedure, and details regarding the basic control algorithm.

  12. High Quality Model Predictive Control for Single Phase Grid Connected Photovoltaic Inverters

    DEFF Research Database (Denmark)

    Zangeneh Bighash, Esmaeil; Sadeghzadeh, Seyed Mohammad; Ebrahimzadeh, Esmaeil

    2018-01-01

    Single phase grid-connected inverters with LCL filter are widely used to connect the photovoltaic systems to the utility grid. Among the presented control schemes, predictive control methods are faster and more accurate but are more complex to implement. Recently, the model-predictive control...... algorithm for single-phase inverter has been presented, where the algorithm implementation is straightforward. In the proposed approach, all switching states are tested in each switching period to achieve the control objectives. However, since the number of the switching states in single-phase inverter...... is low, the inverter output current has a high total harmonic distortions. In order to reduce the total harmonic distortions of the injected current, this paper presents a high-quality model-predictive control for one of the newest structure of the grid connected photovoltaic inverter, i.e., HERIC...

  13. Change in avian abundance predicted from regional forest inventory data

    Science.gov (United States)

    Twedt, Daniel J.; Tirpak, John M.; Jones-Farrand, D. Todd; Thompson, Frank R.; Uihlein, William B.; Fitzgerald, Jane A.

    2010-01-01

    An inability to predict population response to future habitat projections is a shortcoming in bird conservation planning. We sought to predict avian response to projections of future forest conditions that were developed from nationwide forest surveys within the Forest Inventory and Analysis (FIA) program. To accomplish this, we evaluated the historical relationship between silvicolous bird populations and FIA-derived forest conditions within 25 ecoregions that comprise the southeastern United States. We aggregated forest area by forest ownership, forest type, and tree size-class categories in county-based ecoregions for 5 time periods spanning 1963-2008. We assessed the relationship of forest data with contemporaneous indices of abundance for 24 silvicolous bird species that were obtained from Breeding Bird Surveys. Relationships between bird abundance and forest inventory data for 18 species were deemed sufficient as predictive models. We used these empirically derived relationships between regional forest conditions and bird populations to predict relative changes in abundance of these species within ecoregions that are anticipated to coincide with projected changes in forest variables through 2040. Predicted abundances of these 18 species are expected to remain relatively stable in over a quarter (27%) of the ecoregions. However, change in forest area and redistribution of forest types will likely result in changed abundance of some species within many ecosystems. For example, abundances of 11 species, including pine warbler (Dendroica pinus), brown-headed nuthatch (Sitta pusilla), and chuckwills- widow (Caprimulgus carolinensis), are projected to increase within more ecoregions than ecoregions where they will decrease. For 6 other species, such as blue-winged warbler (Vermivora pinus), Carolina wren (Thryothorus ludovicianus), and indigo bunting (Passerina cyanea), we projected abundances will decrease within more ecoregions than ecoregions where they will

  14. Regional variation in the predictive validity of self-rated health for mortality

    Directory of Open Access Journals (Sweden)

    Edward R. Berchick

    2017-12-01

    Full Text Available Self-rated health (SRH is a commonly used measure for assessing general health in surveys in the United States. However, individuals from different parts of the United States may vary in how they assess their health. Geographic differences in health care access and in the prevalence of illnesses may make it difficult to discern true regional differences in health when using SRH as a health measure. In this article, we use data from the 1986 and 1989–2006 National Health Interview Survey Linked Mortality Files and estimate Cox regression models to examine whether the relationship between SRH and five-year all-cause mortality differs by Census region. Contrary to hypotheses, there is no evidence of regional variation in the predictive validity of SRH for mortality. At all levels of SRH, and for both non-Hispanic white and non-Hispanic black respondents, SRH is equally and strongly associated with five-year mortality across regions. Our results suggest that differences in SRH across regions are not solely due to differences in how respondents assess their health across regions, but reflect true differences in health. Future research can, therefore, employ this common measure to investigate the geographic patterning of health in the United States.

  15. Association of genetic variations in the mitochondrial DNA control region with presbycusis.

    Science.gov (United States)

    Falah, Masoumeh; Farhadi, Mohammad; Kamrava, Seyed Kamran; Mahmoudian, Saeid; Daneshi, Ahmad; Balali, Maryam; Asghari, Alimohamad; Houshmand, Massoud

    2017-01-01

    The prominent role of mitochondria in the generation of reactive oxygen species, cell death, and energy production contributes to the importance of this organelle in the intracellular mechanism underlying the progression of the common sensory disorder of the elderly, presbycusis. Reduced mitochondrial DNA (mtDNA) gene expression and coding region variation have frequently been reported as being associated with the development of presbycusis. The mtDNA control region regulates gene expression and replication of the genome of this organelle. To comprehensively understand of the role of mitochondria in the progression of presbycusis, we compared variations in the mtDNA control region between subjects with presbycusis and controls. A total of 58 presbycusis patients and 220 control subjects were enrolled in the study after examination by the otolaryngologist and audiology tests. Variations in the mtDNA control region were investigated by polymerase chain reaction and Sanger sequencing. A total of 113 sequence variants were observed in mtDNA, and variants were detected in 100% of patients, with 84% located in hypervariable regions. The frequencies of the variants, 16,223 C>T, 16,311 T>C, 16,249 T>C, and 15,954 A>C, were significantly different between presbycusis and control subjects. The statistically significant difference in the frequencies of four nucleotide variants in the mtDNA control region of presbycusis patients and controls is in agreement with previous experimental evidence and supports the role of mitochondria in the intracellular mechanism underlying presbycusis development. Moreover, these variants have potential as diagnostic markers for individuals at a high risk of developing presbycusis. The data also suggest the possible presence of changes in the mtDNA control region in presbycusis, which could alter regulatory factor binding sites and influence mtDNA gene expression and copy number.

  16. Thermal Storage Power Balancing with Model Predictive Control

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik

    2013-01-01

    The method described in this paper balances power production and consumption with a large number of thermal loads. Linear controllers are used for the loads to track a temperature set point, while Model Predictive Control (MPC) and model estimation of the load behavior are used for coordination....... The total power consumption of all loads is controlled indirectly through a real-time price. The MPC incorporates forecasts of the power production and disturbances that influence the loads, e.g. time-varying weather forecasts, in order to react ahead of time. A simulation scenario demonstrates...

  17. Measures for regional security and arms control in the South-East Asian area

    International Nuclear Information System (INIS)

    Uren, R.T.

    1992-01-01

    The subject of regional security and arms control in the South-East Asia raises some new and difficult issues. No approach to ensuring regional security could be complete without military dimension including the following categories: regional arms control; global arms control measure; confidence building measures that are designed to enhance the transparency of defense policies; confidence building measures that encourage cooperation among the military forces in the region

  18. Interpreting Disruption Prediction Models to Improve Plasma Control

    Science.gov (United States)

    Parsons, Matthew

    2017-10-01

    In order for the tokamak to be a feasible design for a fusion reactor, it is necessary to minimize damage to the machine caused by plasma disruptions. Accurately predicting disruptions is a critical capability for triggering any mitigative actions, and a modest amount of attention has been given to efforts that employ machine learning techniques to make these predictions. By monitoring diagnostic signals during a discharge, such predictive models look for signs that the plasma is about to disrupt. Typically these predictive models are interpreted simply to give a `yes' or `no' response as to whether a disruption is approaching. However, it is possible to extract further information from these models to indicate which input signals are more strongly correlated with the plasma approaching a disruption. If highly accurate predictive models can be developed, this information could be used in plasma control schemes to make better decisions about disruption avoidance. This work was supported by a Grant from the 2016-2017 Fulbright U.S. Student Program, administered by the Franco-American Fulbright Commission in France.

  19. A robust model predictive control strategy for improving the control performance of air-conditioning systems

    International Nuclear Information System (INIS)

    Huang Gongsheng; Wang Shengwei; Xu Xinhua

    2009-01-01

    This paper presents a robust model predictive control strategy for improving the supply air temperature control of air-handling units by dealing with the associated uncertainties and constraints directly. This strategy uses a first-order plus time-delay model with uncertain time-delay and system gain to describe air-conditioning process of an air-handling unit usually operating at various weather conditions. The uncertainties of the time-delay and system gain, which imply the nonlinearities and the variable dynamic characteristics, are formulated using an uncertainty polytope. Based on this uncertainty formulation, an offline LMI-based robust model predictive control algorithm is employed to design a robust controller for air-handling units which can guarantee a good robustness subject to uncertainties and constraints. The proposed robust strategy is evaluated in a dynamic simulation environment of a variable air volume air-conditioning system in various operation conditions by comparing with a conventional PI control strategy. The robustness analysis of both strategies under different weather conditions is also presented.

  20. Image-Based Visual Servoing for Manipulation Via Predictive Control – A Survey of Some Results

    Directory of Open Access Journals (Sweden)

    Corneliu Lazăr

    2016-09-01

    Full Text Available In this paper, a review of predictive control algorithms developed by the authors for visual servoing of robots in manipulation applications is presented. Using these algorithms, a control predictive framework was created for image-based visual servoing (IBVS systems. Firstly, considering the point features, in the year 2008 we introduced an internal model predictor based on the interaction matrix. Secondly, distinctly from the set-point trajectory, we introduced in 2011 the reference trajectory using the concept from predictive control. Finally, minimizing a sum of squares of predicted errors, the optimal input trajectory was obtained. The new concept of predictive control for IBVS systems was employed to develop a cascade structure for motion control of robot arms. Simulation results obtained with a simulator for predictive IBVS systems are also presented.

  1. Moment based model predictive control for systems with additive uncertainty

    NARCIS (Netherlands)

    Saltik, M.B.; Ozkan, L.; Weiland, S.; Ludlage, J.H.A.

    2017-01-01

    In this paper, we present a model predictive control (MPC) strategy based on the moments of the state variables and the cost functional. The statistical properties of the state predictions are calculated through the open loop iteration of dynamics and used in the formulation of MPC cost function. We

  2. Predicting the structural development in Danish livestock and how it affects control strategies against FMD

    DEFF Research Database (Denmark)

    Christiansen, Lasse Engbo; Hisham Beshara Halasa, Tariq; Boklund, Anette

    2012-01-01

    farms were classified by production type and size each year. A total of 88 classes were used. For each species group (cattle, swine, and sheep and goat) a transition probability matrix (TPM) was estimated based on the ten year to year transitions. It was hypothesized that there might be regional......The purpose of this study was to assess if the optimal control strategy against foot-and-mouth disease (FMD) spread is invariant to structural development in Danish livestock until 2030. The DTU-DADS model as presented by Halasa et al. uses demographic information of all farms including...... significantly different TPMs. These TPMs were used in a Markov chain to predict the distribution of farms in year 2030. However, the predictions were unrealistic as far too many farms opened – since all closed farms were allowed to reopen. It was decided to make the closed state a terminal state and make...

  3. Recent Advances in Explicit Multiparametric Nonlinear Model Predictive Control

    KAUST Repository

    Domínguez, Luis F.

    2011-01-19

    In this paper we present recent advances in multiparametric nonlinear programming (mp-NLP) algorithms for explicit nonlinear model predictive control (mp-NMPC). Three mp-NLP algorithms for NMPC are discussed, based on which novel mp-NMPC controllers are derived. The performance of the explicit controllers are then tested and compared in a simulation example involving the operation of a continuous stirred-tank reactor (CSTR). © 2010 American Chemical Society.

  4. Lymphatic filariasis control in Tanga Region, Tanzania

    DEFF Research Database (Denmark)

    Simonsen, Paul Erik; Derua, Yahya A.; Magesa, Stephen M.

    2014-01-01

    BackgroundLymphatic filariasis (LF) control started in Tanga Region of Tanzania in 2004, with annual ivermectin/albendazole mass drug administration (MDA). Since then, the current project has monitored the effect in communities and schools in rural areas of Tanga District. In 2013, after 8 rounds...

  5. Model Predictive Control for a Small Scale Unmanned Helicopter

    Directory of Open Access Journals (Sweden)

    Jianfu Du

    2008-11-01

    Full Text Available Kinematical and dynamical equations of a small scale unmanned helicoper are presented in the paper. Based on these equations a model predictive control (MPC method is proposed for controlling the helicopter. This novel method allows the direct accounting for the existing time delays which are used to model the dynamics of actuators and aerodynamics of the main rotor. Also the limits of the actuators are taken into the considerations during the controller design. The proposed control algorithm was verified in real flight experiments where good perfomance was shown in postion control mode.

  6. Predictive Duty Cycle Control of Three-Phase Active-Front-End Rectifiers

    DEFF Research Database (Denmark)

    Song, Zhanfeng; Tian, Yanjun; Chen, Wei

    2016-01-01

    This paper proposed an on-line optimizing duty cycle control approach for three-phase active-front-end rectifiers, aiming to obtain the optimal control actions under different operating conditions. Similar to finite control set model predictive control strategy, a cost function previously...

  7. High performance predictive current control of a three phase VSI: An ...

    Indian Academy of Sciences (India)

    ... current control of a three phase VSI: An experimental assessment ... Voltage source inverter; two level inverter; predictive current control; weighting factor ... Conventionally, for reference current tracking control in a two level VSI, the objective ...

  8. Evaluation of the NMC regional ensemble prediction system during the Beijing 2008 Olympic Games

    Science.gov (United States)

    Li, Xiaoli; Tian, Hua; Deng, Guo

    2011-10-01

    Based on the B08RDP (Beijing 2008 Olympic Games Mesoscale Ensemble Prediction Research and Development Project) that was launched by the World Weather Research Programme (WWRP) in 2004, a regional ensemble prediction system (REPS) at a 15-km horizontal resolution was developed at the National Meteorological Center (NMC) of the China Meteorological Administration (CMA). Supplementing to the forecasters' subjective affirmation on the promising performance of the REPS during the 2008 Beijing Olympic Games (BOG), this paper focuses on the objective verification of the REPS for precipitation forecasts during the BOG period. By use of a set of advanced probabilistic verification scores, the value of the REPS compared to the quasi-operational global ensemble prediction system (GEPS) is assessed for a 36-day period (21 July-24 August 2008). The evaluation here involves different aspects of the REPS and GEPS, including their general forecast skills, specific attributes (reliability and resolution), and related economic values. The results indicate that the REPS generally performs significantly better for the short-range precipitation forecasts than the GEPS, and for light to heavy rainfall events, the REPS provides more skillful forecasts for accumulated 6- and 24-h precipitation. By further identifying the performance of the REPS through the attribute-focused measures, it is found that the advantages of the REPS over the GEPS come from better reliability (smaller biases and better dispersion) and increased resolution. Also, evaluation of a decision-making score reveals that a much larger group of users benefits from using the REPS forecasts than using the single model (the control run) forecasts, especially for the heavy rainfall events.

  9. Robust stability in predictive control with soft constraints

    DEFF Research Database (Denmark)

    Thomsen, Sven Creutz; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2010-01-01

    In this paper we take advantage of the primary and dual Youla parameterizations for setting up a soft constrained model predictive control (MPC) scheme for which stability is guaranteed in face of norm-bounded uncertainties. Under special conditions guarantees are also given for hard input...... constraints. In more detail, we parameterize the MPC predictions in terms of the primary Youla parameter and use this parameter as the online optimization variable. The uncertainty is parameterized in terms of the dual Youla parameter. Stability can then be guaranteed through small gain arguments on the loop...

  10. Nonlinear H∞ Optimal Control Scheme for an Underwater Vehicle with Regional Function Formulation

    Directory of Open Access Journals (Sweden)

    Zool H. Ismail

    2013-01-01

    Full Text Available A conventional region control technique cannot meet the demands for an accurate tracking performance in view of its inability to accommodate highly nonlinear system dynamics, imprecise hydrodynamic coefficients, and external disturbances. In this paper, a robust technique is presented for an Autonomous Underwater Vehicle (AUV with region tracking function. Within this control scheme, nonlinear H∞ and region based control schemes are used. A Lyapunov-like function is presented for stability analysis of the proposed control law. Numerical simulations are presented to demonstrate the performance of the proposed tracking control of the AUV. It is shown that the proposed control law is robust against parameter uncertainties, external disturbances, and nonlinearities and it leads to uniform ultimate boundedness of the region tracking error.

  11. Generalized Predictive Control of Dynamic Systems with Rigid-Body Modes

    Science.gov (United States)

    Kvaternik, Raymond G.

    2013-01-01

    Numerical simulations to assess the effectiveness of Generalized Predictive Control (GPC) for active control of dynamic systems having rigid-body modes are presented. GPC is a linear, time-invariant, multi-input/multi-output predictive control method that uses an ARX model to characterize the system and to design the controller. Although the method can accommodate both embedded (implicit) and explicit feedforward paths for incorporation of disturbance effects, only the case of embedded feedforward in which the disturbances are assumed to be unknown is considered here. Results from numerical simulations using mathematical models of both a free-free three-degree-of-freedom mass-spring-dashpot system and the XV-15 tiltrotor research aircraft are presented. In regulation mode operation, which calls for zero system response in the presence of disturbances, the simulations showed reductions of nearly 100%. In tracking mode operations, where the system is commanded to follow a specified path, the GPC controllers produced the desired responses, even in the presence of disturbances.

  12. Real-Time Optimization for Economic Model Predictive Control

    DEFF Research Database (Denmark)

    Sokoler, Leo Emil; Edlund, Kristian; Frison, Gianluca

    2012-01-01

    In this paper, we develop an efficient homogeneous and self-dual interior-point method for the linear programs arising in economic model predictive control. To exploit structure in the optimization problems, the algorithm employs a highly specialized Riccati iteration procedure. Simulations show...

  13. Study on regional stratagem for coal mine disasters control and prevention in China

    Energy Technology Data Exchange (ETDEWEB)

    Shen, B.; Lei, Y. [China Coal Research Institute, Beijing (China)

    2009-09-15

    A regional strategy study was aimed at coal mine disaster control and prevention, which deepens and enriches the macro-strategy of coal mine disaster control and prevention, and provides an important support for the rapid and healthy development of China's regional coal industry. The country was divided into 4 regions: Northeast, North, South and Xinqing. In view of the regional status of coal mine disasters, the regulation and development trend of regional coal mine disasters was analysed, the outstanding problems and key factors were identified, and general thoughts on regional coal mine disaster control and prevention are put forward. 4 refs., 4 figs., 1 tab.

  14. Predicting the flammable region reach of propane vapor clouds

    OpenAIRE

    Vílchez Sánchez, Juan Antonio; Villafañe, Diana; Casal Fàbrega, Joaquim

    2014-01-01

    Liquified gas fuels are widely used around the world, and the growth of LNG and LPG consumption continues to increase. However, using these fuels can lead to accidents if they are released to the environment. Consequently, the challenge to control and predict such hazards has become an objective in emergency planning and risk analysis. In a previous article the “Dispersion Safety Factor” (DSF) was proposed, defined as the ratio between the distance at which the lower flammability limit concen...

  15. Model predictive control-based efficient energy recovery control strategy for regenerative braking system of hybrid electric bus

    International Nuclear Information System (INIS)

    Li, Liang; Zhang, Yuanbo; Yang, Chao; Yan, Bingjie; Marina Martinez, C.

    2016-01-01

    Highlights: • A 7-degree-of-freedom model of hybrid electric vehicle with regenerative braking system is built. • A modified nonlinear model predictive control strategy is developed. • The particle swarm optimization algorithm is employed to solve the optimization problem. • The proposed control strategy is verified by simulation and hardware-in-loop tests. • Test results verify the effectiveness of the proposed control strategy. - Abstract: As one of the main working modes, the energy recovered with regenerative braking system provides an effective approach so as to greatly improve fuel economy of hybrid electric bus. However, it is still a challenging issue to ensure braking stability while maximizing braking energy recovery. To solve this problem, an efficient energy recovery control strategy is proposed based on the modified nonlinear model predictive control method. Firstly, combined with the characteristics of the compound braking process of single-shaft parallel hybrid electric bus, a 7 degrees of freedom model of the vehicle longitudinal dynamics is built. Secondly, considering nonlinear characteristic of the vehicle model and the efficiency of regenerative braking system, the particle swarm optimization algorithm within the modified nonlinear model predictive control is adopted to optimize the torque distribution between regenerative braking system and pneumatic braking system at the wheels. So as to reduce the computational time of modified nonlinear model predictive control, a nearest point method is employed during the braking process. Finally, the simulation and hardware-in-loop test are carried out on road conditions with different tire–road adhesion coefficients, and the proposed control strategy is verified by comparing it with the conventional control method employed in the baseline vehicle controller. The simulation and hardware-in-loop test results show that the proposed strategy can ensure vehicle safety during emergency braking

  16. PID and predictive control of electrical drives and power converters using MATLAB/Simulink

    CERN Document Server

    Wang, Liuping; Yoo, Dae; Gan, Lu; Ng, Ki

    2015-01-01

    A timely introduction to current research on PID and predictive control by one of the leading authors on the subject PID and Predictive Control of Electric Drives and Power Supplies using MATLAB/Simulink examines the classical control system strategies, such as PID control, feed-forward control and cascade control, which are widely used in current practice.  The authors share their experiences in actual design and implementation of the control systems on laboratory test-beds, taking the reader from the fundamentals through to more sophisticated design and analysis.    The book contains secti

  17. Regional Scale High Resolution δ18O Prediction in Precipitation Using MODIS EVI

    Science.gov (United States)

    Huang, Cho-Ying; Wang, Chung-Ho; Lin, Shou-De; Lo, Yi-Chen; Huang, Bo-Wen; Hatch, Kent A.; Shiu, Hau-Jie; You, Cheng-Feng; Chang, Yuan-Mou; Shen, Sheng-Feng

    2012-01-01

    The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develop a novel approach of using the MODIS-EVI (the Moderate Resolution Imagining Spectroradiometer-Enhanced Vegetation Index), to predict δ18O in precipitation at the regional scale. Using a structural equation model, we show that the EVI and precipitated δ18O are highly correlated and thus the EVI is a good predictor of precipitated δ18O. We then test the predictability of our EVI-δ18O model and demonstrate that our approach can provide high accuracy with fine spatial (250×250 m) and temporal (16 days) scale δ18O predictions (annual and monthly predictabilities [r] are 0.96 and 0.80, respectively). We conclude the merging of the EVI and δ18O in precipitation can greatly extend the spatial and temporal data availability and thus enhance the applicability for both the EVI and water isoscape. PMID:23029053

  18. CONTROL PREDICTIVO DE UN ROBOT TIPO SCARA PREDICTIVE CONTROL OF A SCARA ROBOT

    Directory of Open Access Journals (Sweden)

    Oscar Andrés Vivas Albán

    2006-08-01

    Full Text Available Este artículo presenta una aplicación eficiente de un control por modelo de referencia sobre un robot de tipo SCARA. El control estudiado es un control predictivo funcional, el que hace uso de un modelo dinámico simplificado del robot. Los ensayos simulados se realizan sobre un robot de cuatro grados de libertad, tipo SCARA. Con el fin de comparar diferentes estrategias de control, se diseña un controlador clásico tipo PID y dos controladores basados en el modelo de referencia. En este último caso el sistema se linealiza y se desacoplada por realimentación, lo que transforma el sistema a controlar en un simple par de integradores. Al sistema lineal y desacoplado resultante se le aplica el control por par calculado y el control predictivo funcional. Los tres controladores estudiados se simulan sobre el robot SCARA con valores numéricos reales. Las pruebas permiten valorar las respuestas de estos controladores en seguimiento de trayectoria, rechazo de perturbaciones y presencia de errores en el modelado con consignas complejas similares a las utilizadas en procesos de fabricación.This paper describes an efficient approach for model based control, applied on a SCARA robot. The studied control is the predictive functional control which uses a simplified dynamical model of the robot. The simulated tests are made on a SCARA type robot, with four DOF. To compare several control strategies, a classical PID control and two model based controllers are designed. In the last case, the model is first linearized and decoupled by feedback, transforming the system into a double set of integrators. Computed torque control and predictive functional control are applied to the linear and decoupled system. The three studied controllers are simulated on the SCARA robot with real numerical values. Tracking performance, disturbance rejection and model robot mismatch are enlightened, using complex machining tasks trajectories and error presence in the modelling

  19. Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation

    Energy Technology Data Exchange (ETDEWEB)

    Abbas, Nikhar [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Tom, Nathan M [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-06-03

    Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalman filter and autoregressive model to evaluate model predictive control performance.

  20. Predictive Feedback and Feedforward Control for Systems with Unknown Disturbances

    Science.gov (United States)

    Juang, Jer-Nan; Eure, Kenneth W.

    1998-01-01

    Predictive feedback control has been successfully used in the regulation of plate vibrations when no reference signal is available for feedforward control. However, if a reference signal is available it may be used to enhance regulation by incorporating a feedforward path in the feedback controller. Such a controller is known as a hybrid controller. This paper presents the theory and implementation of the hybrid controller for general linear systems, in particular for structural vibration induced by acoustic noise. The generalized predictive control is extended to include a feedforward path in the multi-input multi-output case and implemented on a single-input single-output test plant to achieve plate vibration regulation. There are cases in acoustic-induce vibration where the disturbance signal is not available to be used by the hybrid controller, but a disturbance model is available. In this case the disturbance model may be used in the feedback controller to enhance performance. In practice, however, neither the disturbance signal nor the disturbance model is available. This paper presents the theory of identifying and incorporating the noise model into the feedback controller. Implementations are performed on a test plant and regulation improvements over the case where no noise model is used are demonstrated.

  1. Prevalence and predictive factors for regional osteopenia in women with anorexia nervosa.

    Science.gov (United States)

    Grinspoon, S; Thomas, E; Pitts, S; Gross, E; Mickley, D; Miller, K; Herzog, D; Klibanski, A

    2000-11-21

    Anorexia nervosa is highly prevalent among young women. To determine prevalence and predictive factors for regional bone loss. Prospective cohort analysis. University hospital. 130 women with anorexia nervosa. Dual-energy x-ray absorptiometry. The prevalence of osteopenia (-1.0 SD >/= T-score > -2.5 SD) and osteoporosis (T-score anorexia nervosa. Weight, but not estrogen use, is a significant predictor of BMD in this population at all skeletal sites.

  2. Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Hang-cheong Wong

    2012-01-01

    Full Text Available Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM, to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN and decremental least-squares support vector machine (DLSSVM. Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.

  3. Forming controlled inset regions by ion implantation and laser bombardment

    International Nuclear Information System (INIS)

    Gibbons, J.F.

    1981-01-01

    A semiconductor integrated circuit structure in which the inset regions are ion implanted and laser annealed to maintain substantially the dimensions of the implantation and the method of forming inset implanted regions having controlled dimensions

  4. Predictive Models of Procedural Human Supervisory Control Behavior

    Science.gov (United States)

    2011-01-01

    821708, Brest , France. Page 139 of 150 Boussemart, Y. and M. L. Cummings (2010). "Predicting Supervisory Control Behavior with Hidden Markov Models...Strategies for Strike Planning. COGIS 2006 - Cognitive Systems with Interactive Sensors, Paris . Burges, C. (1998). "A Tutorial on Support Vector Machines

  5. Regional calibration models for predicting loblolly pine tracheid properties using near-infrared spectroscopy

    Science.gov (United States)

    Mohamad Nabavi; Joseph Dahlen; Laurence Schimleck; Thomas L. Eberhardt; Cristian Montes

    2018-01-01

    This study developed regional calibration models for the prediction of loblolly pine (Pinus taeda) tracheid properties using near-infrared (NIR) spectroscopy. A total of 1842 pith-to-bark radial strips, aged 19–31 years, were acquired from 268 trees from 109 stands across the southeastern USA. Diffuse reflectance NIR spectra were collected at 10-mm...

  6. Modeling and control design of a stand alone wind energy conversion system based on functional model predictive control

    Energy Technology Data Exchange (ETDEWEB)

    Kassem, Ahmed M. [Beni-Suef University, Electrical Dept., Beni Suef (Egypt)

    2012-09-15

    This paper investigates the application of the model predictive control (MPC) approach to control the voltage and frequency of a stand alone wind generation system. This scheme consists of a wind turbine which drives an induction generator feeding an isolated load. A static VAR compensator is connected at the induction generator terminals to regulate the load voltage. The rotor speed, and thereby the load frequency are controlled via adjusting the mechanical power input using the blade pitch-angle. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed. Digital simulations have been carried out in order to validate the effectiveness of the proposed scheme. The proposed controller has been tested through step changes in the wind speed and the load impedance. Simulation results show that adequate performance of the proposed wind energy scheme has been achieved. Moreover, this scheme is robust against the parameters variation and eliminates the influence of modeling and measurement errors. (orig.)

  7. Water hammer prediction and control: the Green's function method

    Science.gov (United States)

    Xuan, Li-Jun; Mao, Feng; Wu, Jie-Zhi

    2012-04-01

    By Green's function method we show that the water hammer (WH) can be analytically predicted for both laminar and turbulent flows (for the latter, with an eddy viscosity depending solely on the space coordinates), and thus its hazardous effect can be rationally controlled and minimized. To this end, we generalize a laminar water hammer equation of Wang et al. (J. Hydrodynamics, B2, 51, 1995) to include arbitrary initial condition and variable viscosity, and obtain its solution by Green's function method. The predicted characteristic WH behaviors by the solutions are in excellent agreement with both direct numerical simulation of the original governing equations and, by adjusting the eddy viscosity coefficient, experimentally measured turbulent flow data. Optimal WH control principle is thereby constructed and demonstrated.

  8. Computationally efficient model predictive control algorithms a neural network approach

    CERN Document Server

    Ławryńczuk, Maciej

    2014-01-01

    This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: ·         A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. ·         Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. ·         The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). ·         The MPC algorithms with neural approximation with no on-line linearization. ·         The MPC algorithms with guaranteed stability and robustness. ·         Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require d...

  9. Multi-region fuzzy logic controller with local PID controllers for U-tube steam generator in nuclear power plant

    Directory of Open Access Journals (Sweden)

    Puchalski Bartosz

    2015-12-01

    Full Text Available In the paper, analysis of multi-region fuzzy logic controller with local PID controllers for steam generator of pressurized water reactor (PWR working in wide range of thermal power changes is presented. The U-tube steam generator has a nonlinear dynamics depending on thermal power transferred from coolant of the primary loop of the PWR plant. Control of water level in the steam generator conducted by a traditional PID controller which is designed for nominal power level of the nuclear reactor operates insufficiently well in wide range of operational conditions, especially at the low thermal power level. Thus the steam generator is often controlled manually by operators. Incorrect water level in the steam generator may lead to accidental shutdown of the nuclear reactor and consequently financial losses. In the paper a comparison of proposed multi region fuzzy logic controller and traditional PID controllers designed only for nominal condition is presented. The gains of the local PID controllers have been derived by solving appropriate optimization tasks with the cost function in a form of integrated squared error (ISE criterion. In both cases, a model of steam generator which is readily available in literature was used for control algorithms synthesis purposes. The proposed multi-region fuzzy logic controller and traditional PID controller were subjected to broad-based simulation tests in rapid prototyping software - Matlab/Simulink. These tests proved the advantage of multi-region fuzzy logic controller with local PID controllers over its traditional counterpart.

  10. Neural Network-Based Resistance Spot Welding Control and Quality Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D., Jr.; Ivezic, N.D.; Zacharia, T.

    1999-07-10

    This paper describes the development and evaluation of neural network-based systems for industrial resistance spot welding process control and weld quality assessment. The developed systems utilize recurrent neural networks for process control and both recurrent networks and static networks for quality prediction. The first section describes a system capable of both welding process control and real-time weld quality assessment, The second describes the development and evaluation of a static neural network-based weld quality assessment system that relied on experimental design to limit the influence of environmental variability. Relevant data analysis methods are also discussed. The weld classifier resulting from the analysis successfldly balances predictive power and simplicity of interpretation. The results presented for both systems demonstrate clearly that neural networks can be employed to address two significant problems common to the resistance spot welding industry, control of the process itself, and non-destructive determination of resulting weld quality.

  11. Coral bleaching pathways under the control of regional temperature variability

    Science.gov (United States)

    Langlais, C. E.; Lenton, A.; Heron, S. F.; Evenhuis, C.; Sen Gupta, A.; Brown, J. N.; Kuchinke, M.

    2017-11-01

    Increasing sea surface temperatures (SSTs) are predicted to adversely impact coral populations worldwide through increasing thermal bleaching events. Future bleaching is unlikely to be spatially uniform. Therefore, understanding what determines regional differences will be critical for adaptation management. Here, using a cumulative heat stress metric, we show that characteristics of regional SST determine the future bleaching risk patterns. Incorporating observed information on SST variability, in assessing future bleaching risk, provides novel options for management strategies. As a consequence, the known biases in climate model variability and the uncertainties in regional warming rate across climate models are less detrimental than previously thought. We also show that the thresholds used to indicate reef viability can strongly influence a decision on what constitutes a potential refugia. Observing and understanding the drivers of regional variability, and the viability limits of coral reefs, is therefore critical for making meaningful projections of coral bleaching risk.

  12. Model predictive control of PMSG-based wind turbines for frequency regulation in an isolated grid

    DEFF Research Database (Denmark)

    Wang, Haixin; Yang, Junyou; Ma, Yiming

    2017-01-01

    This paper proposes a frequency regulation strategy applied to wind turbine generators (WTGs) in an isolated grid. In order to complement active power shortage caused by sudden load or wind speed change, an improved deloading method is proposed to solve inconsistent regulation capabilities...... in different speed regions and provide WTGs a certain capacity of power reserves. Considering the torque compensation may bring about power oscillation, speed reference of conventional pitch control system should be reset. Moreover, to suppress disturbances of load and wind speed as well as overcome dependence...... on system parameters, a model predictive controller (MPC) of wind farm is designed to generate torque compensation for each deloaded WTG. The key feature of this strategy is that each WTG reacts to grid disturbances in different ways, which depends on generator speeds. Hardware-in-the-loop simulation...

  13. Development of the predictive maintenance system prototype for the rod control system

    International Nuclear Information System (INIS)

    Lim, H. S.; Hong, H. P.; Koo, J. M.; Kim, Y. B.; Han, H. W.

    2003-01-01

    The demand for safety and reliability of Nuclear Power Plants (NPPs) has been constantly increasing and economical operation is also an important issue. Developing and adopting predictive maintenance technology for the major systems or equipment is considered as a way to achieve these goals. This paper describes the development of a predictive maintenance system prototype for the Rod Control System, which adopts an advanced methodology. Bayesian Belief Networks (BBN) has been adopted for the real time fault diagnosis and prediction of the system. Through a simulation test, it was confirmed that the prototype monitors and secures sound operability of rod drive mechanism and its control system, and also provides the predictive maintenance information

  14. Multiple independent origins of mitochondrial control region duplications in the order Psittaciformes

    Science.gov (United States)

    Schirtzinger, Erin E.; Tavares, Erika S.; Gonzales, Lauren A.; Eberhard, Jessica R.; Miyaki, Cristina Y.; Sanchez, Juan J.; Hernandez, Alexis; Müeller, Heinrich; Graves, Gary R.; Fleischer, Robert C.; Wright, Timothy F.

    2012-01-01

    Mitochondrial genomes are generally thought to be under selection for compactness, due to their small size, consistent gene content, and a lack of introns or intergenic spacers. As more animal mitochondrial genomes are fully sequenced, rearrangements and partial duplications are being identified with increasing frequency, particularly in birds (Class Aves). In this study, we investigate the evolutionary history of mitochondrial control region states within the avian order Psittaciformes (parrots and cockatoos). To this aim, we reconstructed a comprehensive multi-locus phylogeny of parrots, used PCR of three diagnostic fragments to classify the mitochondrial control region state as single or duplicated, and mapped these states onto the phylogeny. We further sequenced 44 selected species to validate these inferences of control region state. Ancestral state reconstruction using a range of weighting schemes identified six independent origins of mitochondrial control region duplications within Psittaciformes. Analysis of sequence data showed that varying levels of mitochondrial gene and tRNA homology and degradation were present within a given clade exhibiting duplications. Levels of divergence between control regions within an individual varied from 0–10.9% with the differences occurring mainly between 51 and 225 nucleotides 3′ of the goose hairpin in domain I. Further investigations into the fates of duplicated mitochondrial genes, the potential costs and benefits of having a second control region, and the complex relationship between evolutionary rates, selection, and time since duplication are needed to fully explain these patterns in the mitochondrial genome. PMID:22543055

  15. Neural networks for predictive control of the mechanism of ...

    African Journals Online (AJOL)

    In this paper, we are interested in the study of the control of orientation of a wind turbine like means of optimization of his output/input ratio (efficiency). The approach suggested is based on the neural predictive control which is justified by the randomness of the wind on the one hand, and on the other hand by the capacity of ...

  16. Adaptive model predictive process control using neural networks

    Science.gov (United States)

    Buescher, K.L.; Baum, C.C.; Jones, R.D.

    1997-08-19

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

  17. Explicit Generalized Predictive Control of Speed and Position of PMSM Drives

    Czech Academy of Sciences Publication Activity Database

    Belda, Květoslav; Vošmik, D.

    2016-01-01

    Roč. 63, č. 6 (2016), s. 3889-3896 ISSN 0278-0046 Institutional support: RVO:67985556 Keywords : current limitation * field weakening * motion control * permanent magnet synchronous motors * position control * predictive control * speed control Subject RIV: BC - Control Systems Theory Impact factor: 7.168, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/belda-0457259.pdf

  18. Hybrid Model Predictive Control as a LFC solution in Hydropower Plants

    Directory of Open Access Journals (Sweden)

    Donaisky Emerson

    2015-01-01

    Full Text Available For Electric Power System safety and stable operation, planning and analysis by using simulation environments are necessary. An important point for frequency stability analysis is, on one hand, an adequate representation of Load-Frequency Control (LFC loops and, on the other hand, the design of advanced control strategies to deal with the power system dynamic complexity. Therefore, in this paper we propose to represent the group turbine/penstock, found in hydropower plants, in a Piecewise Affine (PWA modelling structure. Based on such modelling, we also propose the use of a Hybrid Model Predictive algorithm to be use as a control law in LFC loops. Among the advantages of this PWA representation is the use of this model in the controller algorithm, thereby improving the Load-Frequency Control performance. Simulation results, on a 200 MW hydropower plant compares the performance of predictive control strategy presented with the classical PID control strategy in an isolated condition.

  19. A Building Model Framework for a Genetic Algorithm Multi-objective Model Predictive Control

    DEFF Research Database (Denmark)

    Arendt, Krzysztof; Ionesi, Ana; Jradi, Muhyiddine

    2016-01-01

    Model Predictive Control (MPC) of building systems is a promising approach to optimize building energy performance. In contrast to traditional control strategies which are reactive in nature, MPC optimizes the utilization of resources based on the predicted effects. It has been shown that energy ...

  20. Design of a new PID controller using predictive functional control optimization for chamber pressure in a coke furnace.

    Science.gov (United States)

    Zhang, Jianming

    2017-03-01

    An improved proportional-integral-derivative (PID) controller based on predictive functional control (PFC) is proposed and tested on the chamber pressure in an industrial coke furnace. The proposed design is motivated by the fact that PID controllers for industrial processes with time delay may not achieve the desired control performance because of the unavoidable model/plant mismatches, while model predictive control (MPC) is suitable for such situations. In this paper, PID control and PFC algorithm are combined to form a new PID controller that has the basic characteristic of PFC algorithm and at the same time, the simple structure of traditional PID controller. The proposed controller was tested in terms of set-point tracking and disturbance rejection, where the obtained results showed that the proposed controller had the better ensemble performance compared with traditional PID controllers. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  1. Validation of a predictive model for smart control of electrical energy storage

    NARCIS (Netherlands)

    Homan, Bart; van Leeuwen, Richard Pieter; Smit, Gerardus Johannes Maria; Zhu, Lei; de Wit, Jan B.

    2016-01-01

    The purpose of this paper is to investigate the applicability of a relatively simple model which is based on energy conservation for model predictions as part of smart control of thermal and electric storage. The paper reviews commonly used predictive models. Model predictions of charging and

  2. Association of genetic variations in the mitochondrial DNA control region with presbycusis

    Directory of Open Access Journals (Sweden)

    Falah M

    2017-03-01

    Full Text Available Masoumeh Falah,1 Mohammad Farhadi,1 Seyed Kamran Kamrava,1 Saeid Mahmoudian,1 Ahmad Daneshi,1 Maryam Balali,1 Alimohamad Asghari,2 Massoud Houshmand1,3 1ENT and Head & Neck Research Center and Department, Iran University of Medical Sciences, Tehran, Iran; 2Skull Base Research Center, Iran University of Medical Sciences, Tehran, Iran; 3Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran Background: The prominent role of mitochondria in the generation of reactive oxygen species, cell death, and energy production contributes to the importance of this organelle in the intracellular mechanism underlying the progression of the common sensory disorder of the elderly, presbycusis. Reduced mitochondrial DNA (mtDNA gene expression and coding region variation have frequently been reported as being associated with the development of presbycusis. The mtDNA control region regulates gene expression and replication of the genome of this organelle. To comprehensively understand of the role of mitochondria in the progression of presbycusis, we compared variations in the mtDNA control region between subjects with presbycusis and controls.Methods: A total of 58 presbycusis patients and 220 control subjects were enrolled in the study after examination by the otolaryngologist and audiology tests. Variations in the mtDNA control region were investigated by polymerase chain reaction and Sanger sequencing.Results: A total of 113 sequence variants were observed in mtDNA, and variants were detected in 100% of patients, with 84% located in hypervariable regions. The frequencies of the variants, 16,223 C>T, 16,311 T>C, 16,249 T>C, and 15,954 A>C, were significantly different between presbycusis and control subjects.Conclusion: The statistically significant difference in the frequencies of four nucleotide variants in the mtDNA control region of presbycusis patients and controls is in agreement with previous experimental

  3. Convergence Guaranteed Nonlinear Constraint Model Predictive Control via I/O Linearization

    Directory of Open Access Journals (Sweden)

    Xiaobing Kong

    2013-01-01

    Full Text Available Constituting reliable optimal solution is a key issue for the nonlinear constrained model predictive control. Input-output feedback linearization is a popular method in nonlinear control. By using an input-output feedback linearizing controller, the original linear input constraints will change to nonlinear constraints and sometimes the constraints are state dependent. This paper presents an iterative quadratic program (IQP routine on the continuous-time system. To guarantee its convergence, another iterative approach is incorporated. The proposed algorithm can reach a feasible solution over the entire prediction horizon. Simulation results on both a numerical example and the continuous stirred tank reactors (CSTR demonstrate the effectiveness of the proposed method.

  4. Predictive Solar-Integrated Commercial Building Load Control

    Energy Technology Data Exchange (ETDEWEB)

    Glasgow, Nathan [EdgePower Inc., Aspen, CO (United States)

    2017-01-31

    This report is the final technical report for the Department of Energy SunShot award number EE0007180 to EdgePower Inc., for the project entitled “Predictive Solar-Integrated Commercial Building Load Control.” The goal of this project was to successfully prove that the integration of solar forecasting and building load control can reduce demand charge costs for commercial building owners with solar PV. This proof of concept Tier 0 project demonstrated its value through a pilot project at a commercial building. This final report contains a summary of the work completed through he duration of the project. Clean Power Research was a sub-recipient on the award.

  5. Model predictive control of a wind turbine modelled in Simpack

    International Nuclear Information System (INIS)

    Jassmann, U; Matzke, D; Reiter, M; Abel, D; Berroth, J; Schelenz, R; Jacobs, G

    2014-01-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine

  6. Model predictive control of a wind turbine modelled in Simpack

    Science.gov (United States)

    Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.

    2014-06-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to

  7. Model Predictive Control for the acquisition queue and related queueing networks

    NARCIS (Netherlands)

    Leeuwaarden, van J.S.H.; Lefeber, A.A.J.; Nazarathy, J.; Rooda, J.E.

    2010-01-01

    Model Predictive Control (MPC) is a well established method in control theory and engineering practice. It is often the method of choice for systems that need to be controlled in view of constraints. The main idea of MPC is to solve an optimization problem over a given time horizon at each control

  8. Prospective versus predictive control in timing of hitting a falling ball.

    Science.gov (United States)

    Katsumata, Hiromu; Russell, Daniel M

    2012-02-01

    Debate exists as to whether humans use prospective or predictive control to intercept an object falling under gravity (Baurès et al. in Vis Res 47:2982-2991, 2007; Zago et al. in Vis Res 48:1532-1538, 2008). Prospective control involves using continuous information to regulate action. τ, the ratio of the size of the gap to the rate of gap closure, has been proposed as the information used in guiding interceptive actions prospectively (Lee in Ecol Psychol 10:221-250, 1998). This form of control is expected to generate movement modulation, where variability decreases over the course of an action based upon more accurate timing information. In contrast, predictive control assumes that a pre-programmed movement is triggered at an appropriate criterion timing variable. For a falling object it is commonly argued that an internal model of gravitational acceleration is used to predict the motion of the object and determine movement initiation. This form of control predicts fixed duration movements initiated at consistent time-to-contact (TTC), either across conditions (constant criterion operational timing) or within conditions (variable criterion operational timing). The current study sought to test predictive and prospective control hypotheses by disrupting continuous visual information of a falling ball and examining consistency in movement initiation and duration, and evidence for movement modulation. Participants (n = 12) batted a ball dropped from three different heights (1, 1.3 and 1.5 m), under both full-vision and partial occlusion conditions. In the occlusion condition, only the initial ball drop and the final 200 ms of ball flight to the interception point could be observed. The initiation of the swing did not occur at a consistent TTC, τ, or any other timing variable across drop heights, in contrast with previous research. However, movement onset was not impacted by occluding the ball flight for 280-380 ms. This finding indicates that humans did not

  9. Exploring the physical controls of regional patterns of flow duration curves – Part 2: Role of seasonality, the regime curve, and associated process controls

    Directory of Open Access Journals (Sweden)

    M. Sivapalan

    2012-11-01

    Full Text Available The goal of this paper is to explore the process controls underpinning regional patterns of variations of streamflow regime behavior, i.e., the mean seasonal variation of streamflow within the year, across the continental United States. The ultimate motivation is to use the resulting process understanding to generate insights into the physical controls of another signature of streamflow variability, namely the flow duration curve (FDC. The construction of the FDC removes the time dependence of flows. Thus in order to better understand the physical controls in regions that exhibit strong seasonal dependence, the regime curve (RC, which is closely connected to the FDC, is studied in this paper and later linked back to the FDC. To achieve these aims a top-down modeling approach is adopted; we start with a simple two-stage bucket model, which is systematically enhanced through addition of new processes on the basis of model performance assessment in relation to observations, using rainfall-runoff data from 197 United States catchments belonging to the MOPEX dataset. Exploration of dominant processes and the determination of required model complexity are carried out through model-based sensitivity analyses, guided by a performance metric. Results indicated systematic regional trends in dominant processes: snowmelt was a key process control in cold mountainous catchments in the north and north-west, whereas snowmelt and vegetation cover dynamics were key controls in the north-east; seasonal vegetation cover dynamics (phenology and interception were important along the Appalachian mountain range in the east. A simple two-bucket model (with no other additions was found to be adequate in warm humid catchments along the west coast and in the south-east, with both regions exhibiting strong seasonality, whereas much more complex models are needed in the dry south and south-west. Agricultural catchments in the mid-west were found to be difficult to predict

  10. Adaptive Model Predictive Vibration Control of a Cantilever Beam with Real-Time Parameter Estimation

    Directory of Open Access Journals (Sweden)

    Gergely Takács

    2014-01-01

    Full Text Available This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.

  11. Packetized Predictive Control for Rate-Limited Networks via Sparse Representation

    DEFF Research Database (Denmark)

    Nagahara, Masaaki; Quevedo, Daniel; Østergaard, Jan

    2012-01-01

    controller and the plant input. To achieve robustness with respect to dropouts, the controller transmits data packets containing plant input predictions, which minimize a finite horizon cost function. In our formulation, we design sparse packets for rate-limited networks, by adopting an an ℓ0 optimization...

  12. Silicon microgyroscope temperature prediction and control system based on BP neural network and Fuzzy-PID control method

    International Nuclear Information System (INIS)

    Xia, Dunzhu; Kong, Lun; Hu, Yiwei; Ni, Peizhen

    2015-01-01

    We present a novel silicon microgyroscope (SMG) temperature prediction and control system in a narrow space. As the temperature of SMG is closely related to its drive mode frequency and driving voltage, a temperature prediction model can be established based on the BP neural network. The simulation results demonstrate that the established temperature prediction model can estimate the temperature in the range of −40 to 60 °C with an error of less than ±0.05 °C. Then, a temperature control system based on the combination of fuzzy logic controller and the increment PID control method is proposed. The simulation results prove that the Fuzzy-PID controller has a smaller steady state error, less rise time and better robustness than the PID controller. This is validated by experimental results that show the Fuzzy-PID control method can achieve high precision in keeping the SMG temperature stable at 55 °C with an error of less than 0.2 °C. The scale factor can be stabilized at 8.7 mV/°/s with a temperature coefficient of 33 ppm °C −1 . ZRO (zero rate output) instability is decreased from 1.10°/s (9.5 mV) to 0.08°/s (0.7 mV) when the temperature control system is implemented over an ambient temperature range of −40 to 60 °C. (paper)

  13. Disturbance estimator based predictive current control of grid-connected inverters

    OpenAIRE

    Al-Khafaji, Ahmed Samawi Ghthwan

    2013-01-01

    ABSTRACT: The work presented in my thesis considers one of the modern discrete-time control approaches based on digital signal processing methods, that have been developed to improve the performance control of grid-connected three-phase inverters. Disturbance estimator based predictive current control of grid-connected inverters is proposed. For inverter modeling with respect to the design of current controllers, we choose the d-q synchronous reference frame to make it easier to understand an...

  14. Application of prediction of equilibrium to servo-controlled calorimetry measurements

    International Nuclear Information System (INIS)

    Mayer, R.L. II

    1987-01-01

    Research was performed to develop an endpoint prediction algorithm for use with calorimeters operating in the digital servo-controlled mode. The purpose of this work was to reduce calorimetry measurement times while maintaining the high degree of precision and low bias expected from calorimetry measurements. Data from routine operation of two calorimeters were used to test predictive models at each stage of development against time savings, precision, and robustness criteria. The results of the study indicated that calorimetry measurement times can be significantly reduced using this technique. The time savings is, however, dependent on parameters in the digital servo-control algorithm and on packaging characteristics of measured items

  15. DEVELOPMENT OF THE SOCIAL TENSION RISK PREDICTING ALGORITHM IN THE POPULATION OF CERTAIN REGIONS OF RUSSIA

    Directory of Open Access Journals (Sweden)

    A. B. Mulik

    2017-01-01

    Full Text Available Aim. The aim of the study was development of approaches to predict the risk of social tension for population of the Russian Federation regions.Methods. Theoretical studies based on the analysis of cartographic material from the National Atlas of Russia. The use of geo-information technologies has provided modeling of environmental load in the territory of certain regions of Russia. Experimental studies were performed using standard methods of psycho-physiological testing involving 336 persons 18-23 years old of both sexes.Results. As a fundamental biologically significant factor of the environment, differentiating the Russian Federation territory to areas with discrete actual physical effects, total solar radiation was determined. The subsequent allocation of model regions (Republic of Crimea, Rostov and Saratov regions based on the principle of minimizing regional differences associated factors of environmental pressure per person. Experimental studies have revealed persistent systemic relationships of phenotypic characteristics and tendency of person to neuropsychic tension. The risk of social tension for the study area population is predicted on the condition of finding more than two thirds of the representatives of sample within the borders of a high level of general non-specific reactivity of an organism.Main conclusions. The expediency of using the northern latitude as an integral index of differentiation of areas on the specifics of the severity of the physical factors of environmental impact on human activity is justified. The possibility of the application for the level of general nonspecific reactivity of an organism as a phenotypic trait marker of social tension risk is identified. An algorithm for predicting the risk of social tension among the population, compactly living in certain territories of the Russian Federation is designed. 

  16. Orifice design for the control of coupled region flow

    International Nuclear Information System (INIS)

    Atherton, R.; Spadaro, P.R.; Brummerhop, F.G.

    1975-01-01

    A fluid system arrangement for nuclear reactors is described comprising a triplate orifice apparatus which simultaneously controls core flow distribution, flow rate ratio between hydraulically coupled regions of the blanket and radial static pressure gradients entering and leaving the blanket fuel region. The design of the apparatus is based on the parameters of the diameter of the orifice holes, the friction factor, and expansion, contraction and turning pressure loss coefficients of the geometry of each orifice region. These above parameters are properly matched to provide the desired pressure drop, flow split and negligible cross flow at the interface of standard and power-flattened open lattice blanket regions. (U.S.)

  17. An Iterative Approach for Distributed Model Predictive Control of Irrigation Canals

    NARCIS (Netherlands)

    Doan, D.; Keviczky, T.; Negenborn, R.R.; De Schutter, B.

    2009-01-01

    Optimization techniques have played a fundamental role in designing automatic control systems for the most part of the past half century. This dependence is ever more obvious in today’s wide-spread use of online optimization-based control methods, such as Model Predictive Control (MPC) [1]. The

  18. Effects of lateral boundary condition resolution and update frequency on regional climate model predictions

    Science.gov (United States)

    Pankatz, Klaus; Kerkweg, Astrid

    2015-04-01

    The work presented is part of the joint project "DecReg" ("Regional decadal predictability") which is in turn part of the project "MiKlip" ("Decadal predictions"), an effort funded by the German Federal Ministry of Education and Research to improve decadal predictions on a global and regional scale. In MiKlip, one big question is if regional climate modeling shows "added value", i.e. to evaluate, if regional climate models (RCM) produce better results than the driving models. However, the scope of this study is to look more closely at the setup specific details of regional climate modeling. As regional models only simulate a small domain, they have to inherit information about the state of the atmosphere at their lateral boundaries from external data sets. There are many unresolved questions concerning the setup of lateral boundary conditions (LBC). External data sets come from global models or from global reanalysis data-sets. A temporal resolution of six hours is common for this kind of data. This is mainly due to the fact, that storage space is a limiting factor, especially for climate simulations. However, theoretically, the coupling frequency could be as high as the time step of the driving model. Meanwhile, it is unclear if a more frequent update of the LBCs has a significant effect on the climate in the domain of the RCM. The first study examines how the RCM reacts to a higher update frequency. The study is based on a 30 year time slice experiment for three update frequencies of the LBC, namely six hours, one hour and six minutes. The evaluation of means, standard deviations and statistics of the climate in the regional domain shows only small deviations, some statistically significant though, of 2m temperature, sea level pressure and precipitation. The second part of the first study assesses parameters linked to cyclone activity, which is affected by the LBC update frequency. Differences in track density and strength are found when comparing the simulations

  19. Multiple model predictive control for optimal drug administration of mixed immunotherapy and chemotherapy of tumours.

    Science.gov (United States)

    Sharifi, N; Ozgoli, S; Ramezani, A

    2017-06-01

    Mixed immunotherapy and chemotherapy of tumours is one of the most efficient ways to improve cancer treatment strategies. However, it is important to 'design' an effective treatment programme which can optimize the ways of combining immunotherapy and chemotherapy to diminish their imminent side effects. Control engineering techniques could be used for this. The method of multiple model predictive controller (MMPC) is applied to the modified Stepanova model to induce the best combination of drugs scheduling under a better health criteria profile. The proposed MMPC is a feedback scheme that can perform global optimization for both tumour volume and immune competent cell density by performing multiple constraints. Although current studies usually assume that immunotherapy has no side effect, this paper presents a new method of mixed drug administration by employing MMPC, which implements several constraints for chemotherapy and immunotherapy by considering both drug toxicity and autoimmune. With designed controller we need maximum 57% and 28% of full dosage of drugs for chemotherapy and immunotherapy in some instances, respectively. Therefore, through the proposed controller less dosage of drugs are needed, which contribute to suitable results with a perceptible reduction in medicine side effects. It is observed that in the presence of MMPC, the amount of required drugs is minimized, while the tumour volume is reduced. The efficiency of the presented method has been illustrated through simulations, as the system from an initial condition in the malignant region of the state space (macroscopic tumour volume) transfers into the benign region (microscopic tumour volume) in which the immune system can control tumour growth. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Control Mechanism and Security Region for Intentional Islanding Transition

    DEFF Research Database (Denmark)

    Chen, Yu; Xu, Zhao; Østergaard, Jacob

    2009-01-01

    in the grid. The concept of Islanding Security Region (ISR) has been proposed as an organic composition of the developed control mechanism. The purpose of this control mechanism is to maintain the frequency stability and eventually the security of power supply to the customers, by utilizing resources from...... generation and demand sides. The control mechanism can be extended to consider the distributed generations like wind power and other innovative technologies such as the Demand as Frequency controlled Reserve (DFR) technique in the future....

  1. Model Predictive Control of Wind Turbines using Uncertain LIDAR Measurements

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Soltani, Mohsen; Poulsen, Niels Kjølstad

    2013-01-01

    , we simplify state prediction for the MPC. Consequently, the control problem of the nonlinear system is simplified into a quadratic programming. We consider uncertainty in the wind propagation time, which is the traveling time of wind from the LIDAR measurement point to the rotor. An algorithm based......The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined...... on wind speed estimation and measurements from the LIDAR is devised to find an estimate of the delay and compensate for it before it is used in the controller. Comparisons between the MPC with error compensation, the MPC without error compensation and an MPC with re-linearization at each sample point...

  2. Economic model predictive control theory, formulations and chemical process applications

    CERN Document Server

    Ellis, Matthew; Christofides, Panagiotis D

    2017-01-01

    This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes: Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes. In addition to being...

  3. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob

    2010-01-01

    units. The approach is inspired by smart-grid electric power production and consumption systems, where the flexibility of a large number of power producing and/or power consuming units can be exploited in a smart-grid solution. The objective is to accommodate the load variation on the grid, arising......This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... on one hand from varying consumption, on the other hand by natural variations in power production e.g. from wind turbines. The approach presented is based on quadratic optimization and possess the properties of low algorithmic complexity and of scalability. In particular, the proposed design methodology...

  4. Prediction of household and commercial BMW generation according to socio-economic and other factors for the Dublin region.

    Science.gov (United States)

    Purcell, M; Magette, W L

    2009-04-01

    Both planning and design of integrated municipal solid waste management systems require accurate prediction of waste generation. This research predicted the quantity and distribution of biodegradable municipal waste (BMW) generation within a diverse 'landscape' of residential areas, as well as from a variety of commercial establishments (restaurants, hotels, hospitals, etc.) in the Dublin (Ireland) region. Socio-economic variables, housing types, and the sizes and main activities of commercial establishments were hypothesized as the key determinants contributing to the spatial variability of BMW generation. A geographical information system (GIS) 'model' of BMW generation was created using ArcMap, a component of ArcGIS 9. Statistical data including socio-economic status and household size were mapped on an electoral district basis. Historical research and data from scientific literature were used to assign BMW generation rates to residential and commercial establishments. These predictions were combined to give overall BMW estimates for the region, which can aid waste planning and policy decisions. This technique will also aid the design of future waste management strategies, leading to policy and practice alterations as a function of demographic changes and development. The household prediction technique gave a more accurate overall estimate of household waste generation than did the social class technique. Both techniques produced estimates that differed from the reported local authority data; however, given that local authority reported figures for the region are below the national average, with some of the waste generated from apartment complexes being reported as commercial waste, predictions arising from this research are believed to be closer to actual waste generation than a comparison to reported data would suggest. By changing the input data, this estimation tool can be adapted for use in other locations. Although focusing on waste in the Dublin region

  5. Model predictive control of a high speed switched reluctance generator system

    NARCIS (Netherlands)

    Marinkov, Sava; De Jager, Bram; Steinbuch, Maarten

    2013-01-01

    This paper presents a novel voltage control strategy for the high-speed operation of a Switched Reluctance Generator. It uses a linear Model Predictive Control law based on the average system model. The controller computes the DC-link current needed to achieve the tracking of a desired voltage

  6. Nonlinear Model Predictive Control for Cooperative Control and Estimation

    Science.gov (United States)

    Ru, Pengkai

    Recent advances in computational power have made it possible to do expensive online computations for control systems. It is becoming more realistic to perform computationally intensive optimization schemes online on systems that are not intrinsically stable and/or have very small time constants. Being one of the most important optimization based control approaches, model predictive control (MPC) has attracted a lot of interest from the research community due to its natural ability to incorporate constraints into its control formulation. Linear MPC has been well researched and its stability can be guaranteed in the majority of its application scenarios. However, one issue that still remains with linear MPC is that it completely ignores the system's inherent nonlinearities thus giving a sub-optimal solution. On the other hand, if achievable, nonlinear MPC, would naturally yield a globally optimal solution and take into account all the innate nonlinear characteristics. While an exact solution to a nonlinear MPC problem remains extremely computationally intensive, if not impossible, one might wonder if there is a middle ground between the two. We tried to strike a balance in this dissertation by employing a state representation technique, namely, the state dependent coefficient (SDC) representation. This new technique would render an improved performance in terms of optimality compared to linear MPC while still keeping the problem tractable. In fact, the computational power required is bounded only by a constant factor of the completely linearized MPC. The purpose of this research is to provide a theoretical framework for the design of a specific kind of nonlinear MPC controller and its extension into a general cooperative scheme. The controller is designed and implemented on quadcopter systems.

  7. Bridging the gap between the linear and nonlinear predictive control: Adaptations fo refficient building climate control

    Czech Academy of Sciences Publication Activity Database

    Pčolka, M.; Žáčeková, E.; Robinett, R.; Čelikovský, Sergej; Šebek, M.

    2016-01-01

    Roč. 53, č. 1 (2016), s. 124-138 ISSN 0967-0661 R&D Projects: GA ČR GA13-20433S Institutional support: RVO:67985556 Keywords : Model predictive control * Identification for control * Building climatecontrol Subject RIV: BC - Control Systems Theory Impact factor: 2.602, year: 2016 http://library.utia.cas.cz/separaty/2016/TR/celikovsky-0460306.pdf

  8. Power maximization of a point absorber wave energy converter using improved model predictive control

    Science.gov (United States)

    Milani, Farideh; Moghaddam, Reihaneh Kardehi

    2017-08-01

    This paper considers controlling and maximizing the absorbed power of wave energy converters for irregular waves. With respect to physical constraints of the system, a model predictive control is applied. Irregular waves' behavior is predicted by Kalman filter method. Owing to the great influence of controller parameters on the absorbed power, these parameters are optimized by imperialist competitive algorithm. The results illustrate the method's efficiency in maximizing the extracted power in the presence of unknown excitation force which should be predicted by Kalman filter.

  9. Supervisory Model Predictive Control of the Heat Integrated Distillation Column

    DEFF Research Database (Denmark)

    Meyer, Kristian; Bisgaard, Thomas; Huusom, Jakob Kjøbsted

    2017-01-01

    This paper benchmarks a centralized control system based on model predictive control for the operation of the heat integrated distillation column (HIDiC) against a fully decentralized control system using the most complete column model currently available in the literature. The centralized control...... system outperforms the decentralized system, because it handles the interactions in the HIDiC process better. The integral absolute error (IAE) is reduced by a factor of 2 and a factor of 4 for control of the top and bottoms compositions, respectively....

  10. Applied Distributed Model Predictive Control for Energy Efficient Buildings and Ramp Metering

    Science.gov (United States)

    Koehler, Sarah Muraoka

    Industrial large-scale control problems present an interesting algorithmic design challenge. A number of controllers must cooperate in real-time on a network of embedded hardware with limited computing power in order to maximize system efficiency while respecting constraints and despite communication delays. Model predictive control (MPC) can automatically synthesize a centralized controller which optimizes an objective function subject to a system model, constraints, and predictions of disturbance. Unfortunately, the computations required by model predictive controllers for large-scale systems often limit its industrial implementation only to medium-scale slow processes. Distributed model predictive control (DMPC) enters the picture as a way to decentralize a large-scale model predictive control problem. The main idea of DMPC is to split the computations required by the MPC problem amongst distributed processors that can compute in parallel and communicate iteratively to find a solution. Some popularly proposed solutions are distributed optimization algorithms such as dual decomposition and the alternating direction method of multipliers (ADMM). However, these algorithms ignore two practical challenges: substantial communication delays present in control systems and also problem non-convexity. This thesis presents two novel and practically effective DMPC algorithms. The first DMPC algorithm is based on a primal-dual active-set method which achieves fast convergence, making it suitable for large-scale control applications which have a large communication delay across its communication network. In particular, this algorithm is suited for MPC problems with a quadratic cost, linear dynamics, forecasted demand, and box constraints. We measure the performance of this algorithm and show that it significantly outperforms both dual decomposition and ADMM in the presence of communication delay. The second DMPC algorithm is based on an inexact interior point method which is

  11. Predictive control techniques for energy and indoor environmental quality management in buildings

    Energy Technology Data Exchange (ETDEWEB)

    Kolokotsa, D. [Technological Educational Institute of Crete, Department of Natural Resources and Environment, 3, Romanou str., 73133, Hania, Crete (Greece); Pouliezos, A. [Technical University of Crete, Department of Production Engineering and Management, University Campus, Kounoupidiana, 73100 Hania (Greece); Stavrakakis, G.; Lazos, C. [Technical University of Crete, Department of Electronics and Computer Engineering, University Campus, Kounoupidiana, 73100 Hania (Greece)

    2009-09-15

    The aim of the present paper is to present a model-based predictive controller, combined with a Building Energy Management System (BEMS). The overall system predicts the indoor environmental conditions of a specific building and selects the most appropriate actions so as to reach the set points and contribute to the indoor environmental quality by minimizing energy costs. The controller is tested using a BEMS installation in Hania, Crete, Greece. (author)

  12. Steering Angle Control of Car for Dubins Path-tracking Using Model Predictive Control

    Science.gov (United States)

    Kusuma Rahma Putri, Dian; Subchan; Asfihani, Tahiyatul

    2018-03-01

    Car as one of transportation is inseparable from technological developments. About ten years, there are a lot of research and development on lane keeping system(LKS) which is a system that automaticaly controls the steering to keep the vehicle especially car always on track. This system can be developed for unmanned cars. Unmanned system car requires navigation, guidance and control which is able to direct the vehicle to move toward the desired path. The guidance system is represented by using Dubins-Path that will be controlled by using Model Predictive Control. The control objective is to keep the car’s movement that represented by dinamic lateral motion model so car can move according to the path appropriately. The simulation control on the four types of trajectories that generate the value for steering angle and steering angle changes are at the specified interval.

  13. A Traffic Prediction Algorithm for Street Lighting Control Efficiency

    Directory of Open Access Journals (Sweden)

    POPA Valentin

    2013-01-01

    Full Text Available This paper presents the development of a traffic prediction algorithm that can be integrated in a street lighting monitoring and control system. The prediction algorithm must enable the reduction of energy costs and improve energy efficiency by decreasing the light intensity depending on the traffic level. The algorithm analyses and processes the information received at the command center based on the traffic level at different moments. The data is collected by means of the Doppler vehicle detection sensors integrated within the system. Thus, two methods are used for the implementation of the algorithm: a neural network and a k-NN (k-Nearest Neighbor prediction algorithm. For 500 training cycles, the mean square error of the neural network is 9.766 and for 500.000 training cycles the error amounts to 0.877. In case of the k-NN algorithm the error increases from 8.24 for k=5 to 12.27 for a number of 50 neighbors. In terms of a root means square error parameter, the use of a neural network ensures the highest performance level and can be integrated in a street lighting control system.

  14. Model predictions and control of conditions in a CA-reefer container

    NARCIS (Netherlands)

    Sman, van der R.G.M.; Verdijck, G.J.C.

    2003-01-01

    In this paper a concept for energy saving for refrigerated container transport is presented. The concept is based on model-predictive control of the set points of the cooling unit. These models predict energy consumption of the cooling unit, climatic conditions inside the cargo space, and the change

  15. A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data.

    Science.gov (United States)

    Lu, Qiongshi; Hu, Yiming; Sun, Jiehuan; Cheng, Yuwei; Cheung, Kei-Hoi; Zhao, Hongyu

    2015-05-27

    Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu.

  16. Prediction of multipactor in the iris region of rf deflecting mode cavities

    Directory of Open Access Journals (Sweden)

    G. Burt

    2011-12-01

    Full Text Available Multipactor is a major cause of field limitation in many superconducting rf cavities. Multipacting is a particular issue for deflecting mode cavities as the typical behavior is not well studied, understood, or parametrized. In this paper an approximate analytical model for the prediction of multipactor in the iris region of deflecting mode cavities is developed. This new but simple model yields a clear explanation on the broad range of rf field levels over which the multipactor can occur. The principle multipactors under investigation here are two-point multipactors associated with cyclotron motion in the cavity’s rf magnetic field. The predictions from the model are compared to numerical simulations and good agreement is obtained. The results are also compared to experimental results previously reported by KEK and are also found in good agreement.

  17. Validation of engineering methods for predicting hypersonic vehicle controls forces and moments

    Science.gov (United States)

    Maughmer, M.; Straussfogel, D.; Long, L.; Ozoroski, L.

    1991-01-01

    This work examines the ability of the aerodynamic analysis methods contained in an industry standard conceptual design code, the Aerodynamic Preliminary Analysis System (APAS II), to estimate the forces and moments generated through control surface deflections from low subsonic to high hypersonic speeds. Predicted control forces and moments generated by various control effectors are compared with previously published wind-tunnel and flight-test data for three vehicles: the North American X-15, a hypersonic research airplane concept, and the Space Shuttle Orbiter. Qualitative summaries of the results are given for each force and moment coefficient and each control derivative in the various speed ranges. Results show that all predictions of longitudinal stability and control derivatives are acceptable for use at the conceptual design stage.

  18. Model predictive control approach for a CPAP-device

    Directory of Open Access Journals (Sweden)

    Scheel Mathias

    2017-09-01

    Full Text Available The obstructive sleep apnoea syndrome (OSAS is characterized by a collapse of the upper respiratory tract, resulting in a reduction of the blood oxygen- and an increase of the carbon dioxide (CO2 - concentration, which causes repeated sleep disruptions. The gold standard to treat the OSAS is the continuous positive airway pressure (CPAP therapy. The continuous pressure keeps the upper airway open and prevents the collapse of the upper respiratory tract and the pharynx. Most of the available CPAP-devices cannot maintain the pressure reference [1]. In this work a model predictive control approach is provided. This control approach has the possibility to include the patient’s breathing effort into the calculation of the control variable. Therefore a patient-individualized control strategy can be developed.

  19. A nonlinear model predictive control formulation for obstacle avoidance in high-speed autonomous ground vehicles in unstructured environments

    Science.gov (United States)

    Liu, Jiechao; Jayakumar, Paramsothy; Stein, Jeffrey L.; Ersal, Tulga

    2018-06-01

    This paper presents a nonlinear model predictive control (MPC) formulation for obstacle avoidance in high-speed, large-size autono-mous ground vehicles (AGVs) with high centre of gravity (CoG) that operate in unstructured environments, such as military vehicles. The term 'unstructured' in this context denotes that there are no lanes or traffic rules to follow. Existing MPC formulations for passenger vehicles in structured environments do not readily apply to this context. Thus, a new nonlinear MPC formulation is developed to navigate an AGV from its initial position to a target position at high-speed safely. First, a new cost function formulation is used that aims to find the shortest path to the target position, since no reference trajectory exists in unstructured environments. Second, a region partitioning approach is used in conjunction with a multi-phase optimal control formulation to accommodate the complicated forms the obstacle-free region can assume due to the presence of multiple obstacles in the prediction horizon in an unstructured environment. Third, the no-wheel-lift-off condition, which is the major dynamical safety concern for high-speed, high-CoG AGVs, is ensured by limiting the steering angle within a range obtained offline using a 14 degrees-of-freedom vehicle dynamics model. Thus, a safe, high-speed navigation is enabled in an unstructured environment. Simulations of an AGV approaching multiple obstacles are provided to demonstrate the effectiveness of the algorithm.

  20. Development and Evaluation of a Control System for Regional Traffic Management

    Directory of Open Access Journals (Sweden)

    John L. McLin

    2011-01-01

    Full Text Available Traffic congestion is a worsening problem in metropolitan areas which will require integrated regional traffic control systems to improve traffic conditions. This paper presents a regional traffic control system which can detect incident conditions and provide integrated traffic management during nonrecurrent congestion events. The system combines advanced artificial intelligence techniques with a traffic performance model based on HCM equations. Preliminary evaluation of the control system using traffic microsimulation demonstrates that it has the potential to improve system conditions during traffic incidents. In addition, several enhancements were identified which will make the system more robust in a real traffic control setting. An assessment of the control system elements indicates that there are no substantial technical barriers in implementing this system in a large traffic network.

  1. Predictive control of hollow-fiber bioreactors for the production of monoclonal antibodies.

    Science.gov (United States)

    Dowd, J E; Weber, I; Rodriguez, B; Piret, J M; Kwok, K E

    1999-05-20

    The selection of medium feed rates for perfusion bioreactors represents a challenge for process optimization, particularly in bioreactors that are sampled infrequently. When the present and immediate future of a bioprocess can be adequately described, predictive control can minimize deviations from set points in a manner that can maximize process consistency. Predictive control of perfusion hollow-fiber bioreactors was investigated in a series of hybridoma cell cultures that compared operator control to computer estimation of feed rates. Adaptive software routines were developed to estimate the current and predict the future glucose uptake and lactate production of the bioprocess at each sampling interval. The current and future glucose uptake rates were used to select the perfusion feed rate in a designed response to deviations from the set point values. The routines presented a graphical user interface through which the operator was able to view the up-to-date culture performance and assess the model description of the immediate future culture performance. In addition, fewer samples were taken in the computer-estimated cultures, reducing labor and analytical expense. The use of these predictive controller routines and the graphical user interface decreased the glucose and lactate concentration variances up to sevenfold, and antibody yields increased by 10% to 43%. Copyright 1999 John Wiley & Sons, Inc.

  2. Economic Model Predictive Control for Spray Drying Plants

    DEFF Research Database (Denmark)

    Petersen, Lars Norbert

    and a complexity reduced control model is used for state estimation and prediction in the controllers. These models facilitate development and comparison of control strategies. We develop two MPC strategies; a linear tracking MPC with a Real-Time Optimization layer (MPC with RTO) and an Economic Nonlinear MPC (E...... horizon, out of which only the first input is applied to the dryer. This procedure is repeated at each sample instant and is solved numerically in real-time. The MPC with RTO tracks a target that optimizes the cost of operation at steady-state. The E-MPC optimizes the cost of operation directly by having...... this objective directly in the controller. The need for the RTO layer is then eliminated. We demonstrate the application of the proposed MPC with RTO to control an industrial GEA MSDTM-1250 spray dryer, which produces approximately 7500 kg/hr of enriched milk powder. Compared to the conventional PI controller...

  3. Application of prediction of equilibrium to servo-controlled calorimetry measurements

    International Nuclear Information System (INIS)

    Mayer, R.L. II.

    1987-01-01

    Research was performed to develop an endpoint prediction algorithm for use with calorimeters operating in the digital servo-controlled mode. The purpose of this work was to reduce calorimetry measurement times while maintaining the high degree of precision and low bias expected from calorimetry measurements. Data from routine operation of two calorimeters were used to test predictive models at each stage of development against time savings, precision, and robustness criteria. The results of the study indicated that calorimetry measurement times can be significantly reduced using this technique. The time savings is, however, dependent on parameters in the digital servo-control algorithm and on packaging characteristics of measured items. 7 refs., 4 figs., 1 tab

  4. On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid

    Science.gov (United States)

    Velarde, P.; Valverde, L.; Maestre, J. M.; Ocampo-Martinez, C.; Bordons, C.

    2017-03-01

    In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.

  5. Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Abbas, Nikhar; Tom, Nathan

    2017-09-01

    Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalman filter and autoregressive model to evaluate model predictive control performance.

  6. LANDIS PRO: a landscape model that predicts forest composition and structure changes at regional scales

    Science.gov (United States)

    Wen J. Wang; Hong S. He; Jacob S. Fraser; Frank R. Thompson; Stephen R. Shifley; Martin A. Spetich

    2014-01-01

    LANDIS PRO predicts forest composition and structure changes incorporating species-, stand-, and landscape-scales processes at regional scales. Species-scale processes include tree growth, establishment, and mortality. Stand-scale processes contain density- and size-related resource competition that regulates self-thinning and seedling establishment. Landscapescale...

  7. High abundance of Serine/Threonine-rich regions predicted to be hyper-O-glycosylated in the secretory proteins coded by eight fungal genomes

    Directory of Open Access Journals (Sweden)

    González Mario

    2012-09-01

    Full Text Available Abstract Background O-glycosylation of secretory proteins has been found to be an important factor in fungal biology and virulence. It consists in the addition of short glycosidic chains to Ser or Thr residues in the protein backbone via O-glycosidic bonds. Secretory proteins in fungi frequently display Ser/Thr rich regions that could be sites of extensive O-glycosylation. We have analyzed in silico the complete sets of putatively secretory proteins coded by eight fungal genomes (Botrytis cinerea, Magnaporthe grisea, Sclerotinia sclerotiorum, Ustilago maydis, Aspergillus nidulans, Neurospora crassa, Trichoderma reesei, and Saccharomyces cerevisiae in search of Ser/Thr-rich regions as well as regions predicted to be highly O-glycosylated by NetOGlyc (http://www.cbs.dtu.dk. Results By comparison with experimental data, NetOGlyc was found to overestimate the number of O-glycosylation sites in fungi by a factor of 1.5, but to be quite reliable in the prediction of highly O-glycosylated regions. About half of secretory proteins have at least one Ser/Thr-rich region, with a Ser/Thr content of at least 40% over an average length of 40 amino acids. Most secretory proteins in filamentous fungi were predicted to be O-glycosylated, sometimes in dozens or even hundreds of sites. Residues predicted to be O-glycosylated have a tendency to be grouped together forming hyper-O-glycosylated regions of varying length. Conclusions About one fourth of secretory fungal proteins were predicted to have at least one hyper-O-glycosylated region, which consists of 45 amino acids on average and displays at least one O-glycosylated Ser or Thr every four residues. These putative highly O-glycosylated regions can be found anywhere along the proteins but have a slight tendency to be at either one of the two ends.

  8. Health-aware Model Predictive Control of Pasteurization Plant

    Science.gov (United States)

    Karimi Pour, Fatemeh; Puig, Vicenç; Ocampo-Martinez, Carlos

    2017-01-01

    In order to optimize the trade-off between components life and energy consumption, the integration of a system health management and control modules is required. This paper proposes the integration of model predictive control (MPC) with a fatigue estimation approach that minimizes the damage of the components of a pasteurization plant. The fatigue estimation is assessed with the rainflow counting algorithm. Using data from this algorithm, a simplified model that characterizes the health of the system is developed and integrated with MPC. The MPC controller objective is modified by adding an extra criterion that takes into account the accumulated damage. But, a steady-state offset is created by adding this extra criterion. Finally, by including an integral action in the MPC controller, the steady-state error for regulation purpose is eliminated. The proposed control scheme is validated in simulation using a simulator of a utility-scale pasteurization plant.

  9. Stability analysis of embedded nonlinear predictor neural generalized predictive controller

    Directory of Open Access Journals (Sweden)

    Hesham F. Abdel Ghaffar

    2014-03-01

    Full Text Available Nonlinear Predictor-Neural Generalized Predictive Controller (NGPC is one of the most advanced control techniques that are used with severe nonlinear processes. In this paper, a hybrid solution from NGPC and Internal Model Principle (IMP is implemented to stabilize nonlinear, non-minimum phase, variable dead time processes under high disturbance values over wide range of operation. Also, the superiority of NGPC over linear predictive controllers, like GPC, is proved for severe nonlinear processes over wide range of operation. The necessary conditions required to stabilize NGPC is derived using Lyapunov stability analysis for nonlinear processes. The NGPC stability conditions and improvement in disturbance suppression are verified by both simulation using Duffing’s nonlinear equation and real-time using continuous stirred tank reactor. Up to our knowledge, the paper offers the first hardware embedded Neural GPC which has been utilized to verify NGPC–IMP improvement in realtime.

  10. Thermal regime of the lithosphere and prediction of seismic hazard in the Caspian region

    International Nuclear Information System (INIS)

    Levin, L.E.; Solodilov, L.N.; Kondorskaya, N.V.; Gasanov, A.G; Panahi, B.M.

    2002-01-01

    Full text : Prediction of seicmicity is one of elements of ecology hazard warning. In this collective research, it is elaborated in three directions : quantitative estimate of regional faults by level of seismic activity; ascertainment of space position of earthquake risk zones, determination of high seismic potential sites for the period of the next 3-5 years. During elaboration of prediction, it takes into account that peculiar feature all over the is determined by relationship of about 90 percent of earthquake hypocenters and released energy of seismic waves with elactic-brittle ayer of the lithosphere. Concetration of earthquakes epicenters is established predominantly in zones of complex structure of elastic-brittle layer where gradient of it thickness is 20-30 km. Directions of hypocenters migration in the plastic-viscous layer reveal a space position of seismic dangerous zones. All this provides a necessity for generalization of data on location of earthquakes epicenters; determination of their magnitudes, space position of regional faults and heat flow with calculation of thermal regime being made for clarification of the lithosphere and elastic-brittle thickness variations separately. General analysis includes a calculation of released seismic wave energy and determination of peculiar features of its distribution in the entire region and also studies of hypocenters migration in the plastic-viscous layer of the litosphere in time.

  11. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    Science.gov (United States)

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  12. Predictive control, with restrictions for the climate of a greenhouse

    International Nuclear Information System (INIS)

    Pinon, Sandra; Pena, Miguel; Kuchen, Benjamin

    2002-01-01

    A proposal for controlling nonlinear systems under constraints is presented. a combination of model predictive control and feedback linearization is used. An alternative that uses extended kalman filter as non-measured variable estimator is applied for performing the constrained optimization. Finally, an observability analysis is done in closed loop in order to demonstrate observer convergence

  13. Combined Active and Reactive Power Control of Wind Farms based on Model Predictive Control

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Wang, Jianhui

    2017-01-01

    This paper proposes a combined wind farm controller based on Model Predictive Control (MPC). Compared with the conventional decoupled active and reactive power control, the proposed control scheme considers the significant impact of active power on voltage variations due to the low X=R ratio...... of wind farm collector systems. The voltage control is improved. Besides, by coordination of active and reactive power, the Var capacity is optimized to prevent potential failures due to Var shortage, especially when the wind farm operates close to its full load. An analytical method is used to calculate...... the sensitivity coefficients to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both normal and emergency conditions. A wind farm with 20 wind turbines was used to verify the proposed combined control scheme....

  14. Design and implementation of predictive current control of three-phase PWM rectifier using space-vector modulation (SVM)

    International Nuclear Information System (INIS)

    Bouafia, Abdelouahab; Gaubert, Jean-Paul; Krim, Fateh

    2010-01-01

    This paper is concerned with the design and implementation of current control of three-phase PWM rectifier based on predictive control strategy. The proposed predictive current control technique operates with constant switching frequency, using space-vector modulation (SVM). The main goal of the designed current control scheme is to maintain the dc-bus voltage at the required level and to achieve the unity power factor (UPF) operation of the converter. For this purpose, two predictive current control algorithms, in the sense of deadbeat control, are developed for direct controlling input current vector of the converter in the stationary α-β and rotating d-q reference frame, respectively. For both predictive current control algorithms, at the beginning of each switching period, the required rectifier average voltage vector allowing the cancellation of both tracking errors of current vector components at the end of the switching period, is computed and applied during a predefined switching period by means of SVM. The main advantages of the proposed predictive current control are that no need to use hysteresis comparators or PI controllers in current control loops, and constant switching frequency. Finally, the developed predictive current control algorithms were tested both in simulations and experimentally, and illustrative results are presented here. Results have proven excellent performance in steady and transient states, and verify the validity of the proposed predictive current control which is compared to other control strategies.

  15. An outlook on robust model predictive control algorithms : Reflections on performance and computational aspects

    NARCIS (Netherlands)

    Saltik, M.B.; Özkan, L.; Ludlage, J.H.A.; Weiland, S.; Van den Hof, P.M.J.

    2018-01-01

    In this paper, we discuss the model predictive control algorithms that are tailored for uncertain systems. Robustness notions with respect to both deterministic (or set based) and stochastic uncertainties are discussed and contributions are reviewed in the model predictive control literature. We

  16. A comparison of the performance of the 3-D super-ensemble and an ensemble Kalman filter for short-range regional ocean prediction

    Directory of Open Access Journals (Sweden)

    Baptiste Mourre

    2014-01-01

    Full Text Available This study compares the ability of two approaches integrating models and data to forecast the Ligurian Sea regional oceanographic conditions in the short-term range (0–72 hours when constrained by a common observation dataset. The post-processing 3-D super-ensemble (3DSE algorithm, which uses observations to optimally combine multi-model forecasts into a single prediction of the oceanic variable, is first considered. The 3DSE predictive skills are compared to those of the Regional Ocean Modeling System model in which observations are assimilated through a more conventional ensemble Kalman filter (EnKF approach. Assimilated measurements include sea surface temperature maps, and temperature and salinity subsurface observations from a fleet of five underwater gliders. Retrospective analyses are carried out to produce daily predictions during the 11-d period of the REP10 sea trial experiment. The forecast skill evaluation based on a distributed multi-sensor validation dataset indicates an overall superior performance of the EnKF, both at the surface and at depth. While the 3DSE and EnKF perform comparably well in the area spanned by the incorporated measurements, the 3DSE accuracy is found to rapidly decrease outside this area. In particular, the univariate formulation of the method combined with the absence of regular surface salinity measurements produces large errors in the 3DSE salinity forecast. On the contrary, the EnKF leads to more homogeneous forecast errors over the modelling domain for both temperature and salinity. The EnKF is found to consistently improve the predictions with respect to the control solution without assimilation and to be positively skilled when compared to the climatological estimate. For typical regional oceanographic applications with scarce subsurface observations, the lack of physical spatial and multivariate error covariances applicable to the individual model weights in the 3DSE formulation constitutes a major

  17. Adaptive Control of a Utility-Scale Wind Turbine Operating in Region 3

    Science.gov (United States)

    Frost, Susan A.; Balas, Mark J.; Wright, Alan D.

    2009-01-01

    Adaptive control techniques are well suited to nonlinear applications, such as wind turbines, which are difficult to accurately model and which have effects from poorly known operating environments. The turbulent and unpredictable conditions in which wind turbines operate create many challenges for their operation. In this paper, we design an adaptive collective pitch controller for a high-fidelity simulation of a utility scale, variable-speed horizontal axis wind turbine. The objective of the adaptive pitch controller in Region 3 is to regulate generator speed and reject step disturbances. The control objective is accomplished by collectively pitching the turbine blades. We use an extension of the Direct Model Reference Adaptive Control (DMRAC) approach to track a reference point and to reject persistent disturbances. The turbine simulation models the Controls Advanced Research Turbine (CART) of the National Renewable Energy Laboratory in Golden, Colorado. The CART is a utility-scale wind turbine which has a well-developed and extensively verified simulator. The adaptive collective pitch controller for Region 3 was compared in simulations with a bas celliansesical Proportional Integrator (PI) collective pitch controller. In the simulations, the adaptive pitch controller showed improved speed regulation in Region 3 when compared with the baseline PI pitch controller and it demonstrated robustness to modeling errors.

  18. Model Predictive Control of the Hybrid Ventilation for Livestock

    DEFF Research Database (Denmark)

    Wu, Zhuang; Stoustrup, Jakob; Trangbæk, Klaus

    2006-01-01

    In this paper, design and simulation results of Model Predictive Control (MPC) strategy for livestock hybrid ventilation systems and associated indoor climate through variable valve openings and exhaust fans are presented. The design is based on thermal comfort parameters for poultry in barns...

  19. Model predictive control of hybrid systems : stability and robustness

    NARCIS (Netherlands)

    Lazar, M.

    2006-01-01

    This thesis considers the stabilization and the robust stabilization of certain classes of hybrid systems using model predictive control. Hybrid systems represent a broad class of dynamical systems in which discrete behavior (usually described by a finite state machine) and continuous behavior

  20. A simple method to predict regional fish abundance: an example in the McKenzie River Basin, Oregon

    Science.gov (United States)

    D.J. McGarvey; J.M. Johnston

    2011-01-01

    Regional assessments of fisheries resources are increasingly called for, but tools with which to perform them are limited. We present a simple method that can be used to estimate regional carrying capacity and apply it to the McKenzie River Basin, Oregon. First, we use a macroecological model to predict trout densities within small, medium, and large streams in the...

  1. Toward a Smart Car: Hybrid Nonlinear Predictive Controller With Adaptive Horizon

    Czech Academy of Sciences Publication Activity Database

    Pčolka, M.; Žáčeková, E.; Čelikovský, Sergej; Šebek, M.

    (2018), č. článku 08059760. ISSN 1063-6536 R&D Projects: GA ČR(CZ) GA17-04682S Institutional support: RVO:67985556 Keywords : Autonomous vehicles * hybrid systems * nonlinear model predictive control (MPC) * optimization * vehicle control Subject RIV: BC - Control Systems Theory Impact factor: 3.882, year: 2016 http://ieeexplore.ieee.org/document/8059760/

  2. Improved model predictive control for high voltage quality in microgrid applications

    DEFF Research Database (Denmark)

    Dragicevic, T.; Al hasheem, Mohamed; Lu, M.

    2017-01-01

    This paper proposes an improvement of the finite control set model predictive control (FCS-MPC) strategy for enhancing the voltage regulation performance of a voltage source converter (VSC) used for standalone microgrid and uninterrupted power supply (UPS) applications. The modification is based...

  3. Involving regional expertise in nationwide modeling for adequate prediction of climate change effects on different demands for fresh water

    Science.gov (United States)

    de Lange, W. J.

    2014-05-01

    Wim J. de Lange, Geert F. Prinsen, Jacco H. Hoogewoud, Ab A Veldhuizen, Joachim Hunink, Erik F.W. Ruijgh, Timo Kroon Nationwide modeling aims to produce a balanced distribution of climate change effects (e.g. harm on crops) and possible compensation (e.g. volume fresh water) based on consistent calculation. The present work is based on the Netherlands Hydrological Instrument (NHI, www.nhi.nu), which is a national, integrated, hydrological model that simulates distribution, flow and storage of all water in the surface water and groundwater systems. The instrument is developed to assess the impact on water use on land-surface (sprinkling crops, drinking water) and in surface water (navigation, cooling). The regional expertise involved in the development of NHI come from all parties involved in the use, production and management of water, such as waterboards, drinking water supply companies, provinces, ngo's, and so on. Adequate prediction implies that the model computes changes in the order of magnitude that is relevant to the effects. In scenarios related to drought, adequate prediction applies to the water demand and the hydrological effects during average, dry, very dry and extremely dry periods. The NHI acts as a part of the so-called Deltamodel (www.deltamodel.nl), which aims to predict effects and compensating measures of climate change both on safety against flooding and on water shortage during drought. To assess the effects, a limited number of well-defined scenarios is used within the Deltamodel. The effects on demand of fresh water consist of an increase of the demand e.g. for surface water level control to prevent dike burst, for flushing salt in ditches, for sprinkling of crops, for preserving wet nature and so on. Many of the effects are dealt with by regional and local parties. Therefore, these parties have large interest in the outcome of the scenario analyses. They are participating in the assessment of the NHI previous to the start of the analyses

  4. Controlled test for predictive power of Lyapunov exponents: their inability to predict epileptic seizures.

    Science.gov (United States)

    Lai, Ying-Cheng; Harrison, Mary Ann F; Frei, Mark G; Osorio, Ivan

    2004-09-01

    Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy. Copyright 2004 American Institute of Physics

  5. Model predictive control of room temperature with disturbance compensation

    Science.gov (United States)

    Kurilla, Jozef; Hubinský, Peter

    2017-08-01

    This paper deals with temperature control of multivariable system of office building. The system is simplified to several single input-single output systems by decoupling their mutual linkages, which are separately controlled by regulator based on generalized model predictive control. Main part of this paper focuses on the accuracy of the office temperature with respect to occupancy profile and effect of disturbance. Shifting of desired temperature and changing of weighting coefficients are used to achieve the desired accuracy of regulation. The final structure of regulation joins advantages of distributed computing power and possibility to use network communication between individual controllers to consider the constraints. The advantage of using decoupled MPC controllers compared to conventional PID regulators is demonstrated in a simulation study.

  6. Model predictive and reallocation problem for CubeSat fault recovery and attitude control

    Science.gov (United States)

    Franchi, Loris; Feruglio, Lorenzo; Mozzillo, Raffaele; Corpino, Sabrina

    2018-01-01

    In recent years, thanks to the increase of the know-how on machine-learning techniques and the advance of the computational capabilities of on-board processing, expensive computing algorithms, such as Model Predictive Control, have begun to spread in space applications even on small on-board processor. The paper presents an algorithm for an optimal fault recovery of a 3U CubeSat, developed in MathWorks Matlab & Simulink environment. This algorithm involves optimization techniques aiming at obtaining the optimal recovery solution, and involves a Model Predictive Control approach for the attitude control. The simulated system is a CubeSat in Low Earth Orbit: the attitude control is performed with three magnetic torquers and a single reaction wheel. The simulation neglects the errors in the attitude determination of the satellite, and focuses on the recovery approach and control method. The optimal recovery approach takes advantage of the properties of magnetic actuation, which gives the possibility of the redistribution of the control action when a fault occurs on a single magnetic torquer, even in absence of redundant actuators. In addition, the paper presents the results of the implementation of Model Predictive approach to control the attitude of the satellite.

  7. Towards Robust Predictive Fault–Tolerant Control for a Battery Assembly System

    Directory of Open Access Journals (Sweden)

    Seybold Lothar

    2015-12-01

    Full Text Available The paper deals with the modeling and fault-tolerant control of a real battery assembly system which is under implementation at the RAFI GmbH company (one of the leading electronic manufacturing service providers in Germany. To model and control the battery assembly system, a unified max-plus algebra and model predictive control framework is introduced. Subsequently, the control strategy is enhanced with fault-tolerance features that increase the overall performance of the production system being considered. In particular, it enables tolerating (up to some degree mobile robot, processing and transportation faults. The paper discusses also robustness issues, which are inevitable in real production systems. As a result, a novel robust predictive fault-tolerant strategy is developed that is applied to the battery assembly system. The last part of the paper shows illustrative examples, which clearly exhibit the performance of the proposed approach.

  8. Economic Optimization of Spray Dryer Operation using Nonlinear Model Predictive Control

    DEFF Research Database (Denmark)

    Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2014-01-01

    In this paper we investigate an economically optimizing Nonlinear Model Predictive Control (E-NMPC) for a spray drying process. By simulation we evaluate the economic potential of this E-NMPC compared to a conventional PID based control strategy. Spray drying is the preferred process to reduce...... the water content for many liquid foodstuffs and produces a free flowing powder. The main challenge in controlling the spray drying process is to meet the residual moisture specifications and avoid that the powder sticks to the chamber walls of the spray dryer. We present a model for a spray dryer that has...... been validated on experimental data from a pilot plant. We use this model for simulation as well as for prediction in the E-NMPC. The E-NMPC is designed with hard input constraints and soft output constraints. The open-loop optimal control problem in the E-NMPC is solved using the single...

  9. Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

    Science.gov (United States)

    Quinn Thomas, R.; Brooks, Evan B.; Jersild, Annika L.; Ward, Eric J.; Wynne, Randolph H.; Albaugh, Timothy J.; Dinon-Aldridge, Heather; Burkhart, Harold E.; Domec, Jean-Christophe; Fox, Thomas R.; Gonzalez-Benecke, Carlos A.; Martin, Timothy A.; Noormets, Asko; Sampson, David A.; Teskey, Robert O.

    2017-07-01

    Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model-data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 105 km2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO2 study were allowed to have different mortality parameters than the other field plots in the region. We present

  10. Financial analysis of brucellosis control for small-scale goat farming in the Bajío region, Mexico.

    Science.gov (United States)

    Oseguera Montiel, David; Bruce, Mieghan; Frankena, Klaas; Udo, Henk; van der Zijpp, Akke; Rushton, Jonathan

    2015-03-01

    Brucellosis is an endemic disease in small-scale goat husbandry systems in Mexico. It is a zoonosis and the economic consequences can be large, although estimates are not available for the Mexican goat sector. Our objective was to conduct a financial analysis of brucellosis control in a prominent dairy goat production area of the Bajío region, Mexico. We used three models: (1) a brucellosis transmission model at village flock level (n=1000 head), (2) a flock growth model at smallholder flock level (n=23 head) using output of model 1 and (3) cost-benefit analysis of several brucellosis control scenarios based on output of model 2. Scenarios consisted of test-and-slaughter or vaccination or a combination of both compared to the base situation (no control). The average net present values (NPV) of using vaccination over a 5-year period was 3.8 US$ (90% CI: 1.3-6.6) and 20 US$ (90% CI: 11.3-28.6) over a 10-year period per goat. The average benefit-cost ratios over a 5-year period and 10-year period were 4.3 US$ (90% CI: 2.2-6.9) and 12.3 US$ (90% CI: 7.5-17.3) per goat, respectively. For the total dairy goat population (38,462 head) of the study area (the Bajío of Jalisco and Michoacán) the NPV's over a 5-year and 10-year period were 0.15 million US$ and 0.8 million US$. However, brucellosis prevalence was predicted to remain relatively high at about 12%. Control scenarios with test-and-slaughter predicted to reduce brucellosis prevalence to less than 3%, but this produced a negative NPV over a 5-year period ranging from -31.6 to -11.1 US$ and from -31.1 to 7.5 US$ over a 10-year period. A brucellosis control campaign based on vaccination with full coverage is economically profitable for the goat dairy sector of the region although smallholders would need financial support in case test-and-slaughter is applied to reduce the prevalence more quickly. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass Boiler

    Directory of Open Access Journals (Sweden)

    Palash Sarkar

    2017-02-01

    Full Text Available This paper presents a novel, effective method to handle critical sensor faults affecting a control system devised to operate a biomass boiler. In particular, the proposed method consists of integrating a data reconciliation algorithm in a model predictive control loop, so as to annihilate the effects of faults occurring in the sensor of the flue gas oxygen concentration, by feeding the controller with the reconciled measurements. Indeed, the oxygen content in flue gas is a key variable in control of biomass boilers due its close connections with both combustion efficiency and polluting emissions. The main benefit of including the data reconciliation algorithm in the loop, as a fault tolerant component, with respect to applying standard fault tolerant methods, is that controller reconfiguration is not required anymore, since the original controller operates on the restored, reliable data. The integrated data reconciliation–model predictive control (MPC strategy has been validated by running simulations on a specific type of biomass boiler—the KPA Unicon BioGrate boiler.

  12. Modeling a multivariable reactor and on-line model predictive control.

    Science.gov (United States)

    Yu, D W; Yu, D L

    2005-10-01

    A nonlinear first principle model is developed for a laboratory-scaled multivariable chemical reactor rig in this paper and the on-line model predictive control (MPC) is implemented to the rig. The reactor has three variables-temperature, pH, and dissolved oxygen with nonlinear dynamics-and is therefore used as a pilot system for the biochemical industry. A nonlinear discrete-time model is derived for each of the three output variables and their model parameters are estimated from the real data using an adaptive optimization method. The developed model is used in a nonlinear MPC scheme. An accurate multistep-ahead prediction is obtained for MPC, where the extended Kalman filter is used to estimate system unknown states. The on-line control is implemented and a satisfactory tracking performance is achieved. The MPC is compared with three decentralized PID controllers and the advantage of the nonlinear MPC over the PID is clearly shown.

  13. A Combined Cooperative Braking Model with a Predictive Control Strategy in an Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Hongqiang Guo

    2013-12-01

    Full Text Available Cooperative braking with regenerative braking and mechanical braking plays an important role in electric vehicles for energy-saving control. Based on the parallel and the series cooperative braking models, a combined model with a predictive control strategy to get a better cooperative braking performance is presented. The balance problem between the maximum regenerative energy recovery efficiency and the optimum braking stability is solved through an off-line process optimization stream with the collaborative optimization algorithm (CO. To carry out the process optimization stream, the optimal Latin hypercube design (Opt LHD is presented to discrete the continuous design space. To solve the poor real-time problem of the optimization, a high-precision predictive model based on the off-line optimization data of the combined model is built, and a predictive control strategy is proposed and verified through simulation. The simulation results demonstrate that the predictive control strategy and the combined model are reasonable and effective.

  14. Predicting Wetland Distribution Changes under Climate Change and Human Activities in a Mid- and High-Latitude Region

    Directory of Open Access Journals (Sweden)

    Dandan Zhao

    2018-03-01

    Full Text Available Wetlands in the mid- and high-latitudes are particularly vulnerable to environmental changes and have declined dramatically in recent decades. Climate change and human activities are arguably the most important factors driving wetland distribution changes which will have important implications for wetland ecological functions and services. We analyzed the importance of driving variables for wetland distribution and investigated the relative importance of climatic factors and human activity factors in driving historical wetland distribution changes. We predicted wetland distribution changes under climate change and human activities over the 21st century using the Random Forest model in a mid- and high-latitude region of Northeast China. Climate change scenarios included three Representative Concentration Pathways (RCPs based on five general circulation models (GCMs downloaded from the Coupled Model Intercomparison Project, Phase 5 (CMIP5. The three scenarios (RCP 2.6, RCP 4.5, and RCP 8.5 predicted radiative forcing to peak at 2.6, 4.5, and 8.5 W/m2 by the 2100s, respectively. Our results showed that the variables with high importance scores were agricultural population proportion, warmness index, distance to water body, coldness index, and annual mean precipitation; climatic variables were given higher importance scores than human activity variables on average. Average predicted wetland area among three emission scenarios were 340,000 ha, 123,000 ha, and 113,000 ha for the 2040s, 2070s, and 2100s, respectively. Average change percent in predicted wetland area among three periods was greatest under the RCP 8.5 emission scenario followed by RCP 4.5 and RCP 2.6 emission scenarios, which were 78%, 64%, and 55%, respectively. Losses in predicted wetland distribution were generally around agricultural lands and expanded continually from the north to the whole region over time, while the gains were mostly associated with grasslands and water in the

  15. Hyperspectral-based predictive modelling of grapevine water status in the Portuguese Douro wine region

    Science.gov (United States)

    Pôças, Isabel; Gonçalves, João; Costa, Patrícia Malva; Gonçalves, Igor; Pereira, Luís S.; Cunha, Mario

    2017-06-01

    In this study, hyperspectral reflectance (HySR) data derived from a handheld spectroradiometer were used to assess the water status of three grapevine cultivars in two sub-regions of Douro wine region during two consecutive years. A large set of potential predictors derived from the HySR data were considered for modelling/predicting the predawn leaf water potential (Ψpd) through different statistical and machine learning techniques. Three HySR vegetation indices were selected as final predictors for the computation of the models and the in-season time trend was removed from data by using a time predictor. The vegetation indices selected were the Normalized Reflectance Index for the wavelengths 554 nm and 561 nm (NRI554;561), the water index (WI) for the wavelengths 900 nm and 970 nm, and the D1 index which is associated with the rate of reflectance increase in the wavelengths of 706 nm and 730 nm. These vegetation indices covered the green, red edge and the near infrared domains of the electromagnetic spectrum. A large set of state-of-the-art analysis and statistical and machine-learning modelling techniques were tested. Predictive modelling techniques based on generalized boosted model (GBM), bagged multivariate adaptive regression splines (B-MARS), generalized additive model (GAM), and Bayesian regularized neural networks (BRNN) showed the best performance for predicting Ψpd, with an average determination coefficient (R2) ranging between 0.78 and 0.80 and RMSE varying between 0.11 and 0.12 MPa. When cultivar Touriga Nacional was used for training the models and the cultivars Touriga Franca and Tinta Barroca for testing (independent validation), the models performance was good, particularly for GBM (R2 = 0.85; RMSE = 0.09 MPa). Additionally, the comparison of Ψpd observed and predicted showed an equitable dispersion of data from the various cultivars. The results achieved show a good potential of these predictive models based on vegetation indices to support

  16. Active Power Optimal Control of Wind Turbines with Doubly Fed Inductive Generators Based on Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Guo Jiuwang

    2015-01-01

    Full Text Available Because of the randomness and fluctuation of wind energy, as well as the impact of strongly nonlinear characteristic of variable speed constant frequency (VSCF wind power generation system with doubly fed induction generators (DFIG, traditional active power control strategies are difficult to achieve high precision control and the output power of wind turbines is more fluctuated. In order to improve the quality of output electric energy of doubly fed wind turbines, on the basis of analyzing the operating principles and dynamic characteristics of doubly fed wind turbines, this paper proposes a new active power optimal control method of doubly fed wind turbines based on predictive control theory. This method uses state space model of wind turbines, based on the prediction of the future state of wind turbines, moves horizon optimization, and meanwhile, gets the control signals of pitch angle and generator torque. Simulation results show that the proposed control strategies can guarantee the utilization efficiency for wind energy. Simultaneously, they can improve operation stability of wind turbines and the quality of electric energy.

  17. Health-aware Model Predictive Control of Wind Turbines using Fatigue Prognosis

    DEFF Research Database (Denmark)

    Sardi, Hector Eloy Sanchez; Escobet, Teressa; Puig, Vicenc

    2015-01-01

    management module with the control provides a mechanism for the wind turbine to operate safely and optimize the trade-off between components life and energy production. The research presented in this paper explores the integration of model predictive control (MPC) with fatigue-based prognosis approach...

  18. Ozone phytotoxicity evaluation and prediction of crops production in tropical regions

    Science.gov (United States)

    Mohammed, Nurul Izma; Ramli, Nor Azam; Yahya, Ahmad Shukri

    2013-04-01

    Increasing ozone concentration in the atmosphere can threaten food security due to its effects on crop production. Since the 1980s, ozone has been believed to be the most damaging air pollutant to crops. In Malaysia, there is no index to indicate the reduction of crops due to the exposure of ozone. Therefore, this study aimed to identify the accumulated exposure over a threshold of X ppb (AOTX) indexes in assessing crop reduction in Malaysia. In European countries, crop response to ozone exposure is mostly expressed as AOT40. This study was designed to evaluate and predict crop reduction in tropical regions and in particular, the Malaysian climate, by adopting the AOT40 index method and modifying it based on Malaysian air quality and crop data. Nine AOTX indexes (AOT0, AOT5, AOT10, AOT15, AOT20, AOT25, AOT30, AOT40, and AOT50) were analyzed, crop responses tested and reduction in crops predicted. The results showed that the AOT50 resulted in the highest reduction in crops and the highest R2 value between the AOT50 and the crops reduction from the linear regression analysis. Hence, this study suggests that the AOT50 index is the most suitable index to estimate the potential ozone impact on crops in tropical regions. The result showed that the critical level for AOT50 index if the estimated crop reduction is 5% was 1336 ppb h. Additionally, the results indicated that the AOT40 index in Malaysia gave a minimum percentage of 6% crop reduction; as contrasted with the European guideline of 5% (due to differences in the climate e.g., average amount of sunshine).

  19. What is the Best Model Specification and Earth Observation Product for Predicting Regional Grain Yields in Food Insecure Countries?

    Science.gov (United States)

    Davenport, F., IV; Harrison, L.; Shukla, S.; Husak, G. J.; Funk, C. C.

    2017-12-01

    We evaluate the predictive accuracy of an ensemble of empirical model specifications that use earth observation data to predict sub-national grain yields in Mexico and East Africa. Products that are actively used for seasonal drought monitoring are tested as yield predictors. Our research is driven by the fact that East Africa is a region where decisions regarding agricultural production are critical to preventing the loss of economic livelihoods and human life. Regional grain yield forecasts can be used to anticipate availability and prices of key staples, which can turn can inform decisions about targeting humanitarian response such as food aid. Our objective is to identify-for a given region, grain, and time year- what type of model and/or earth observation can most accurately predict end of season yields. We fit a set of models to county level panel data from Mexico, Kenya, Sudan, South Sudan, and Somalia. We then examine out of sample predicative accuracy using various linear and non-linear models that incorporate spatial and time varying coefficients. We compare accuracy within and across models that use predictor variables from remotely sensed measures of precipitation, temperature, soil moisture, and other land surface processes. We also examine at what point in the season a given model or product is most useful for determining predictive accuracy. Finally we compare predictive accuracy across a variety of agricultural regimes including high intensity irrigated commercial agricultural and rain fed subsistence level farms.

  20. Short-range ensemble predictions based on convection perturbations in the Eta Model for the Serra do Mar region in Brazil

    Science.gov (United States)

    Bustamante, J. F. F.; Chou, S. C.; Gomes, J. L.

    2009-04-01

    The Southeast Brazil, in the coastal and mountain region called Serra do Mar, between Sao Paulo and Rio de Janeiro, is subject to frequent events of landslides and floods. The Eta Model has been producing good quality forecasts over South America at about 40-km horizontal resolution. For that type of hazards, however, more detailed and probabilistic information on the risks should be provided with the forecasts. Thus, a short-range ensemble prediction system (SREPS) based on the Eta Model is being constructed. Ensemble members derived from perturbed initial and lateral boundary conditions did not provide enough spread for the forecasts. Members with model physics perturbation are being included and tested. The objective of this work is to construct more members for the Eta SREPS by adding physics perturbed members. The Eta Model is configured at 10-km resolution and 38 layers in the vertical. The domain covered is most of Southeast Brazil, centered over the Serra do Mar region. The constructed members comprise variations of the cumulus parameterization Betts-Miller-Janjic (BMJ) and Kain-Fritsch (KF) schemes. Three members were constructed from the BMJ scheme by varying the deficit of saturation pressure profile over land and sea, and 2 members of the KF scheme were included using the standard KF and a momentum flux added to KF scheme version. One of the runs with BMJ scheme is the control run as it was used for the initial condition perturbation SREPS. The forecasts were tested for 6 cases of South America Convergence Zone (SACZ) events. The SACZ is a common summer season feature of Southern Hemisphere that causes persistent rain for a few days over the Southeast Brazil and it frequently organizes over Serra do Mar region. These events are particularly interesting because of the persistent rains that can accumulate large amounts and cause generalized landslides and death. With respect to precipitation, the KF scheme versions have shown to be able to reach the

  1. Regionally differentiated air pollution control regulations in the installation-related emission control law of the Federal Republic of Germany

    International Nuclear Information System (INIS)

    Buettner, T.W.

    1992-01-01

    The volume treats an issue from the boundary zone between environmental law and environmental economics, namely the regionalization of air pollution control standards in installation-related emission control law. In order to examine the question of whether this proposal, which originates in the field of environmental economics, can be adopted and is purposeful, the author initially performs a complete inventorization of applicable norms, this covering emission control law, the law of regional planning, and the provisions of international law. This status quo is then reviewed using conformity and optimization criteria developed by the political sciences. The assessment comes to the conclusion that the introduction of regionally differentiated air pollution control standards is not desirable. The author further submits proposals for the streamlining of the law of installation-related air pollution control in the Federal Republic of Germany. (orig.) [de

  2. A Comparison of the Crustal Deformation Predicted by Glacial Isostatic Adjustment to Seismicity in the Baffin Region of Northern Canada

    Science.gov (United States)

    James, T. S.; Schamehorn, T.; Bent, A. L.; Allen, T. I.; Mulder, T.; Simon, K.

    2016-12-01

    The horizontal crustal strain-rates induced by glacial isostatic adjustment (GIA) in northern Canada and western Greenland region are compared to the spatial pattern of seismicity. For the comparison, an updated seismicity catalogue was created from the 2010 version of the NRCan Seismic Hazard Earthquake Epicentre File (SHEEF2010) catalogue and the Greenland Ice Sheet Monitoring Network (GLISN) catalogue of the Geological Survey of Denmark and Greenland (GEUS). Crustal motion rates were computed with the Innu/Laur16 ice-sheet history and the VM5a viscosity profile (Simon et al., 2015; 2016). This GIA model optimizes the fit to relative sea-level and vertical crustal motion measurements around Hudson Bay and in the Canadian Arctic Archipelago (CAA). A region in Baffin Bay with historically high seismicity, including the 1933 M 7.4 and the 1934 and 1945 M 6.5 earthquakes, features high predicted GIA strain-rates. Elsewhere, agreement is not strong, with zones of seismicity occurring where predicted horizontal crustal strain-rates are small and large crustal strain-rates predicted where earthquake occurrence is muted. For example, large compressional crustal strain-rates are predicted beneath seismically quiescent portions of the Greenland ice sheet. Similarly, large predicted extensional strain-rates occur around southern Hudson Bay and the Foxe Basin, which are also regions of relative seismic quiescence. Additional factors to be considered include the orientation of the background stress field, relative to the predicted stress changes, and potential pre-existing zones of lithospheric weakness.

  3. Fault-Tolerant Region-Based Control of an Underwater Vehicle with Kinematically Redundant Thrusters

    Directory of Open Access Journals (Sweden)

    Zool H. Ismail

    2014-01-01

    Full Text Available This paper presents a new control approach for an underwater vehicle with a kinematically redundant thruster system. This control scheme is derived based on a fault-tolerant decomposition for thruster force allocation and a region control scheme for the tracking objective. Given a redundant thruster system, that is, six or more pairs of thrusters are used, the proposed redundancy resolution and region control scheme determine the number of thruster faults, as well as providing the reference thruster forces in order to keep the underwater vehicle within the desired region. The stability of the presented control law is proven in the sense of a Lyapunov function. Numerical simulations are performed with an omnidirectional underwater vehicle and the results of the proposed scheme illustrate the effectiveness in terms of optimizing the thruster forces.

  4. Mathematical Modelling and Predictive Control of Permanent Magnet Synchronous Motor Drives

    Czech Academy of Sciences Publication Activity Database

    Belda, Květoslav

    2013-01-01

    Roč. 2, č. 4 (2013), s. 114-120 ISSN 1805-3386 R&D Projects: GA ČR(CZ) GAP102/11/0437 Institutional support: RVO:67985556 Keywords : Permanent magnet synchronous motor * mathematical modelling * discrete predictive control * multistep explicit control law * square-root optimization Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2014/AS/belda-0422285.pdf

  5. Machine learning and hurdle models for improving regional predictions of stream water acid neutralizing capacity

    Science.gov (United States)

    Nicholas A. Povak; Paul F. Hessburg; Keith M. Reynolds; Timothy J. Sullivan; Todd C. McDonnell; R. Brion Salter

    2013-01-01

    In many industrialized regions of the world, atmospherically deposited sulfur derived from industrial, nonpoint air pollution sources reduces stream water quality and results in acidic conditions that threaten aquatic resources. Accurate maps of predicted stream water acidity are an essential aid to managers who must identify acid-sensitive streams, potentially...

  6. 40 CFR 81.55 - Northeast Pennsylvania-Upper Delaware Valley Interstate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Northeast Pennsylvania-Upper Delaware... Designation of Air Quality Control Regions § 81.55 Northeast Pennsylvania-Upper Delaware Valley Interstate Air Quality Control Region. The Northeast Pennsylvania-Upper Delaware Valley Interstate Air Quality Control...

  7. Model Predictive Control of Hybrid Thermal Energy Systems in Transport Refrigeration

    DEFF Research Database (Denmark)

    Shafiei, Seyed Ehsan; Alleyne, Andrew

    2015-01-01

    A predictive control scheme is designed to control a transport refrigeration system, such as a delivery truck, that includes a vapor compression cycle configured in parallel with a thermal energy storage (TES) unit. A novel approach to TES utilization is introduced and is based on the current...

  8. A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

    Science.gov (United States)

    Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

    2017-11-01

    As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.

  9. Higher energy efficiency and better water quality by using model predictive flow control at water supply systems

    NARCIS (Netherlands)

    Bakker, M.; Verberk, J.Q.J.C.; Palmen, L.J.; Sperber, V.; Bakker, G.

    2011-01-01

    Half of all water supply systems in the Netherlands are controlled by model predictive flow control; the other half are controlled by conventional level based control. The differences between conventional level based control and model predictive control were investigated in experiments at five full

  10. Integration of Predictive Display and Aircraft Flight Control System

    Directory of Open Access Journals (Sweden)

    Efremov A.V.

    2017-01-01

    Full Text Available The synthesis of predictive display information and direct lift control system are considered for the path control tracking tasks (in particular landing task. The both solutions are based on pilot-vehicle system analysis and requirements to provide the highest accuracy and lowest pilot workload. The investigation was carried out for cases with and without time delay in aircraft dynamics. The efficiency of the both ways for the flying qualities improvement and their integration is tested by ground based simulation.

  11. Well-control co-ops for regional responses

    International Nuclear Information System (INIS)

    Abel, L.W.; Winchester, J.

    1996-01-01

    Oilfield exploration and production companies worldwide can benefit economically and in many other ways by participating in well-control cooperatives rather than operating alone and reacting to well control (WC) events by creating one-off equipment to respond to the event. The authors have presented details of a proposed co-op in this paper. The cost to maintain a full complement of WC equipment and retain trained personnel in a given region is prohibitive for a single company; however, cost becomes reasonable when resources are shared among an operator base of co-operating companies. Each individual operator receives the full benefit of having strong WC event response capability while paying only a portion of the cost

  12. An adaptive predictive controller and its applications in power stations

    Energy Technology Data Exchange (ETDEWEB)

    Yang Zhiyuan; Lu Huiming; Zhang Xinggao [North China Electric Power University, Beijing (China); Song Chunping [Tsinghua University, Beijing (China). Dept. of Thermal Energy Engineering

    1999-07-01

    Based on the objective function in the form of integration of generalized model error, a globally convergent model reference adaptive predictive control algorithm (MRAPC) containing inertia-time compensators is presented in this paper. MRAPC has been successfully applied to control important thermal process of more than 20 units in many Chinese power stations. In this paper three representative examples are described. Continual operation results for years demonstrate that MRAPC is a successful attempt for the practical applications of adaptive control techniques. (author)

  13. Linear Model-Based Predictive Control of the LHC 1.8 K Cryogenic Loop

    CERN Document Server

    Blanco-Viñuela, E; De Prada-Moraga, C

    1999-01-01

    The LHC accelerator will employ 1800 superconducting magnets (for guidance and focusing of the particle beams) in a pressurized superfluid helium bath at 1.9 K. This temperature is a severely constrained control parameter in order to avoid the transition from the superconducting to the normal state. Cryogenic processes are difficult to regulate due to their highly non-linear physical parameters (heat capacity, thermal conductance, etc.) and undesirable peculiarities like non self-regulating process, inverse response and variable dead time. To reduce the requirements on either temperature sensor or cryogenic system performance, various control strategies have been investigated on a reduced-scale LHC prototype built at CERN (String Test). Model Based Predictive Control (MBPC) is a regulation algorithm based on the explicit use of a process model to forecast the plant output over a certain prediction horizon. This predicted controlled variable is used in an on-line optimization procedure that minimizes an approp...

  14. Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network

    International Nuclear Information System (INIS)

    Xu, Bo; Dan, Han-Cheng; Li, Liang

    2017-01-01

    Highlights: • Pavement temperature prediction model is presented with improved BP neural network. • Dynamic and static methods are presented to predict pavement temperature. • Pavement temperature can be excellently predicted in next 3 h. - Abstract: Ice cover on pavement threatens traffic safety, and pavement temperature is the main factor used to determine whether the wet pavement is icy or not. In this paper, a temperature prediction model of the pavement in winter is established by introducing an improved Back Propagation (BP) neural network model. Before the application of the BP neural network model, many efforts were made to eliminate chaos and determine the regularity of temperature on the pavement surface (e.g., analyze the regularity of diurnal and monthly variations of pavement temperature). New dynamic and static prediction methods are presented by improving the algorithms to intelligently overcome the prediction inaccuracy at the change point of daily temperature. Furthermore, some scenarios have been compared for different dates and road sections to verify the reliability of the prediction model. According to the analysis results, the daily pavement temperatures can be accurately predicted for the next 3 h from the time of prediction by combining the dynamic and static prediction methods. The presented method in this paper can provide technical references for temperature prediction of the pavement and the development of an early-warning system for icy pavements in cold regions.

  15. Prediction models and control algorithms for predictive applications of setback temperature in cooling systems

    International Nuclear Information System (INIS)

    Moon, Jin Woo; Yoon, Younju; Jeon, Young-Hoon; Kim, Sooyoung

    2017-01-01

    Highlights: • Initial ANN model was developed for predicting the time to the setback temperature. • Initial model was optimized for producing accurate output. • Optimized model proved its prediction accuracy. • ANN-based algorithms were developed and tested their performance. • ANN-based algorithms presented superior thermal comfort or energy efficiency. - Abstract: In this study, a temperature control algorithm was developed to apply a setback temperature predictively for the cooling system of a residential building during occupied periods by residents. An artificial neural network (ANN) model was developed to determine the required time for increasing the current indoor temperature to the setback temperature. This study involved three phases: development of the initial ANN-based prediction model, optimization and testing of the initial model, and development and testing of three control algorithms. The development and performance testing of the model and algorithm were conducted using TRNSYS and MATLAB. Through the development and optimization process, the final ANN model employed indoor temperature and the temperature difference between the current and target setback temperature as two input neurons. The optimal number of hidden layers, number of neurons, learning rate, and moment were determined to be 4, 9, 0.6, and 0.9, respectively. The tangent–sigmoid and pure-linear transfer function was used in the hidden and output neurons, respectively. The ANN model used 100 training data sets with sliding-window method for data management. Levenberg-Marquart training method was employed for model training. The optimized model had a prediction accuracy of 0.9097 root mean square errors when compared with the simulated results. Employing the ANN model, ANN-based algorithms maintained indoor temperatures better within target ranges. Compared to the conventional algorithm, the ANN-based algorithms reduced the duration of time, in which the indoor temperature

  16. Model predictive control of wind energy conversion systems

    CERN Document Server

    Yaramasu, Venkata Narasimha R

    2017-01-01

    The authors provide a comprehensive analysis on the model predictive control of power converters employed in a wide variety of variable-speed wind energy conversion systems (WECS). The contents of this book includes an overview of wind energy system configurations, power converters for variable-speed WECS, digital control techniques, MPC, modeling of power converters and wind generators for MPC design. Other topics include the mapping of continuous-time models to discrete-time models by various exact, approximate, and quasi-exact discretization methods, modeling and control of wind turbine grid-side two-level and multilevel voltage source converters. The authors also focus on the MPC of several power converter configurations for full variable-speed permanent magnet synchronous generator based WECS, squirrel-cage induction generator based WECS, and semi-variable-speed doubly fed induction generator based WECS.

  17. Input-constrained model predictive control via the alternating direction method of multipliers

    DEFF Research Database (Denmark)

    Sokoler, Leo Emil; Frison, Gianluca; Andersen, Martin S.

    2014-01-01

    This paper presents an algorithm, based on the alternating direction method of multipliers, for the convex optimal control problem arising in input-constrained model predictive control. We develop an efficient implementation of the algorithm for the extended linear quadratic control problem (LQCP......) with input and input-rate limits. The algorithm alternates between solving an extended LQCP and a highly structured quadratic program. These quadratic programs are solved using a Riccati iteration procedure, and a structure-exploiting interior-point method, respectively. The computational cost per iteration...... is quadratic in the dimensions of the controlled system, and linear in the length of the prediction horizon. Simulations show that the approach proposed in this paper is more than an order of magnitude faster than several state-of-the-art quadratic programming algorithms, and that the difference in computation...

  18. Model Predictive Control Based on Kalman Filter for Constrained Hammerstein-Wiener Systems

    Directory of Open Access Journals (Sweden)

    Man Hong

    2013-01-01

    Full Text Available To precisely track the reactor temperature in the entire working condition, the constrained Hammerstein-Wiener model describing nonlinear chemical processes such as in the continuous stirred tank reactor (CSTR is proposed. A predictive control algorithm based on the Kalman filter for constrained Hammerstein-Wiener systems is designed. An output feedback control law regarding the linear subsystem is derived by state observation. The size of reaction heat produced and its influence on the output are evaluated by the Kalman filter. The observation and evaluation results are calculated by the multistep predictive approach. Actual control variables are computed while considering the constraints of the optimal control problem in a finite horizon through the receding horizon. The simulation example of the CSTR tester shows the effectiveness and feasibility of the proposed algorithm.

  19. Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler

    Directory of Open Access Journals (Sweden)

    Zhenhao Tang

    2017-01-01

    Full Text Available To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process.

  20. A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking

    Directory of Open Access Journals (Sweden)

    Hongxiao Yu

    2015-05-01

    Full Text Available Trajectory tracking and state estimation are significant in the motion planning and intelligent vehicle control. This article focuses on the model predictive control approach for the trajectory tracking of the intelligent vehicles and state estimation of the nonlinear vehicle system. The constraints of the system states are considered when applying the model predictive control method to the practical problem, while 4-degree-of-freedom vehicle model and unscented Kalman filter are proposed to estimate the vehicle states. The estimated states of the vehicle are used to provide model predictive control with real-time control and judge vehicle stability. Furthermore, in order to decrease the cost of solving the nonlinear optimization, the linear time-varying model predictive control is used at each time step. The effectiveness of the proposed vehicle state estimation and model predictive control method is tested by driving simulator. The results of simulations and experiments show that great and robust performance is achieved for trajectory tracking and state estimation in different scenarios.