Hybrid state-space self-tuning control of uncertain linear systems
Shieh, L. S.; Wang, Y. J.; Sunkel, J. W.
1993-01-01
The paper presents a hybrid state-space self-tuner using a new dual-rate sampling scheme for digital adaptive control of continuous-time uncertain linear systems. A state-space-based recursive least-squares algorithm, together with a variable forgetting factor, is used for direct estimations of both the equivalent discrete-time uncertain linear system parameters and the associated discrete-time state of a continuous-time uncertain linear system from the sampled input and output data. An analogue optimal regional pole-placement design method is used for designing an optimal observer-based analogue controller. A suboptimal observer-based digital controller is then designed from the designed analogue controller using digital redesign technique. To enhance the robustness of parameter identification and state estimation algorithms, a dynamic bound for a class of uncertain bilinear parameters and a fast-rate digital controller are developed at each fast-sampling period. Also, to accommodate computation loads and computation delay for developing the advanced hybrid self-tuner, the designed analogue controller and observer gains are both updated at each slow-sampling period. This control technique has been successfully applied to benchmark control problems.
Chu-Tong Wang; Tsai, Jason S. H.; Chia-Wei Chen; You Lin; Shu-Mei Guo; Leang-San Shieh
2010-01-01
An active fault-tolerant pulse-width-modulated tracker using the nonlinear autoregressive moving average with exogenous inputs model-based state-space self-tuning control is proposed for continuous-time multivariable nonlinear stochastic systems with unknown system parameters, plant noises, measurement noises, and inaccessible system states. Through observer/Kalman filter identification method, a good initial guess of the unknown parameters of the chosen model is obtained so as to reduce the ...
Directory of Open Access Journals (Sweden)
Chu-Tong Wang
2010-01-01
Full Text Available An active fault-tolerant pulse-width-modulated tracker using the nonlinear autoregressive moving average with exogenous inputs model-based state-space self-tuning control is proposed for continuous-time multivariable nonlinear stochastic systems with unknown system parameters, plant noises, measurement noises, and inaccessible system states. Through observer/Kalman filter identification method, a good initial guess of the unknown parameters of the chosen model is obtained so as to reduce the identification process time and enhance the system performances. Besides, by modifying the conventional self-tuning control, a fault-tolerant control scheme is also developed. For the detection of fault occurrence, a quantitative criterion is exploited by comparing the innovation process errors estimated by the Kalman filter estimation algorithm. In addition, the weighting matrix resetting technique is presented by adjusting and resetting the covariance matrix of parameter estimates to improve the parameter estimation for faulty system recovery. The technique can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection.
Hybrid state-space time integration of rotating beams
DEFF Research Database (Denmark)
Krenk, Steen; Nielsen, Martin Bjerre
2012-01-01
An efficient time integration algorithm for the dynamic equations of flexible beams in a rotating frame of reference is presented. The equations of motion are formulated in a hybrid state-space format in terms of local displacements and local components of the absolute velocity. With inspiration ...
Directory of Open Access Journals (Sweden)
Ru Wang
2017-01-01
Full Text Available In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID controller is proposed to imitate the actual pressure of the hydraulic support. To avoid the premature convergence and to improve the convergence velocity for tuning PID parameters, the PID controller is optimized with a hybrid optimization algorithm integrated with the particle swarm algorithm (PSO and genetic algorithm (GA. A selection probability and an adaptive cross probability are introduced into the PSO to enhance the diversity of particles. The proportional overflow valve is installed to control the pressure of the pillar cylinder. The data of the control voltage of the proportional relief valve amplifier and pillar pressure are collected to acquire the system transfer function. Several simulations with different methods are performed on the hydraulic cylinder pressure system. The results demonstrate that the hybrid algorithm for a PID controller has comparatively better global search ability and faster convergence velocity on the pressure control of the hydraulic cylinder. Finally, an experiment is conducted to verify the validity of the proposed method.
A new hybrid piezo-actuated compliant mechanism with self-tuned flexure arm
Ling, Mingxiang; Cao, Junyi
2017-04-01
Recent interests and demands for developing video-rate atomic force microscopes, high-throughput probe-based nanofabrication and high-frequency vibration generator for assisted-machining are increasingly posing new challenges for designing high-bandwidth and large-range piezo-actuated compliant mechanisms. The previous studies mainly focused on making the trade-off between natural frequency and motion range by designing a proper topology. Differing from the previous works, this paper attempts to break the deadlock by employing both piezo-stacks and piezoelectric patches to actuate compliant mechanisms. In this method, piezo-stacks provide an actuating force similar to the traditional way, while piezoelectric patches are bonded on the surface of the flexure arms in compliant mechanisms. These `active' laminaes are used to further actuate the hosting flexural beam by inducing strains on the interface and then give additional bending moments to the flexural arms, which enlarge the output displacement of the compliant mechanism while without the sacrifice of natural frequency. An analytical formulation is established to illustrate the new driving principle and the compound static behaviour of a specific hybrid piezo-actuated multistage compliant mechanism. Initial prototype is also manufactured and experimentally testing is conducted to verify the feasibility of the method.
Robust Self Tuning Controllers
DEFF Research Database (Denmark)
Poulsen, Niels Kjølstad
1985-01-01
The present thesis concerns robustness properties of adaptive controllers. It is addressed to methods for robustifying self tuning controllers with respect to abrupt changes in the plant parameters. In the thesis an algorithm for estimating abruptly changing parameters is presented. The estimator...... has several operation modes and a detector for controlling the mode. A special self tuning controller has been developed to regulate plant with changing time delay....
Self-Tuning Fuzzy PI-Type Controller in Z-Source Inverter for Hybrid Electric Vehicles
Directory of Open Access Journals (Sweden)
Pham Cong Thanh
2012-10-01
Full Text Available This paper presents new algorithms to control speed induction motor (SIM and the peak dc-link voltage (PDV across the inverter bridge in z-source inverters (ZSI by applying self-tuning fuzzy PI controller (SFP with robust structure and non-linear characteristic. In particular, this so-called SFP based control algorithm (SFPA is applied to a closed loop speed controlsystem of induction motor, which relies on direct torque controlscheme combined with modified space vector modulation (DTCMSVM control strategy with so many exceptional features (e.g. fast torque response, low steady state torque ripple, and high accurate. Additionally, SFPA is used to control SIM and PDV are more adaptive to the sudden change of parameters such as load torque, stator resistance and dc input voltage (DIV, respectively. The transient response of SIM and PDV are thus improved with less over shoot, short rise time, small steady-state error and fast settling time, with low disturbance for output voltage stabilization in the inverter bridge. As a result, we achieve higher accuracy and robustness of SIM control system. Our new SFPA is verified in both simulation and experimental implementation using MATLAB and dSPACE DS1103, respectively.
Hybrid State-Space Time Integration of Rotating Beams
DEFF Research Database (Denmark)
Nielsen, Martin Bjerre; Krenk, Steen
2010-01-01
Modeling and efficien design of wind turbines require efficien and accurate computational methods for dynamic analysis of the different components. In the present paper an efficien hybrid formulation for beams in a rotating frame of reference is presented for analysis of the rotor system. It is d...
Self-tuning regulators. [adaptive control research
Astrom, K. J.
1975-01-01
The results of a research project are discussed for self-tuning regulators for active control. An algorithm for the self-tuning regulator is described as being stochastic, nonlinear, time variable, and not trivial.
Disformally self-tuning gravity
Emond, William T
2015-01-01
We extend a previous self-tuning analysis of the most general scalar-tensor theory of gravity in four dimensions with second order field equations by considering a generalized coupling to the matter sector. Through allowing a disformal coupling to matter we are able to extend the Fab Four model and construct a new class of theories that are able to tune away the cosmological constant on Friedmann-Lemaitre-Robertson-Walker backgrounds.
Disformally self-tuning gravity
Emond, William T.; Saffin, Paul M.
2016-03-01
We extend a previous self-tuning analysis of the most general scalar-tensor theory of gravity in four dimensions with second order field equations by considering a generalized coupling to the matter sector. Through allowing a disformal coupling to matter we are able to extend the Fab Four model and construct a new class of theories that are able to tune away the cosmological constant on Friedmann-Lemaitre-Robertson-Walker backgrounds.
Knox, H. A.; Draelos, T.; Young, C. J.; Lawry, B.; Chael, E. P.; Faust, A.; Peterson, M. G.
2015-12-01
The quality of automatic detections from seismic sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. Yet, achieving superior automatic detection of seismic events is closely related to these parameters. We present an automated sensor tuning (AST) system that learns near-optimal parameter settings for each event type using neuro-dynamic programming (reinforcement learning) trained with historic data. AST learns to test the raw signal against all event-settings and automatically self-tunes to an emerging event in real-time. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Reducing false alarms early in the seismic pipeline processing will have a significant impact on this goal. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections in seismic waveforms from a network of stations, we show that AST increases the probability of detection while decreasing false alarms.
Institute of Scientific and Technical Information of China (English)
XI Lifeng; DU Shichang
2007-01-01
The final product quality is determined by cumulation, coupling and propagation of product quality variations from all stations in multi-stage manufacturing systems (MMSs). Modeling and control of variation propagation is essential to improve product quality. However, the current stream of variations (SOV) theory can only solve the problem that a single SOV affects the product quality. Due to the existence of multiple variation streams, limited research has been done on the quality control in serial-parallel hybrid multi-stage manufacturing systems (SPH-MMSs). A state space model and its modeling strategies are developed to describe the multiple variation streams stack-up in an SPH-MMS. The SOV theory is extended to SPH-MMS. The dimensions of system model are reduced to the production-reality level, and the effect and feasibility of the model is validated by a machining case.
Self-tuning Integral Force Feedback
Holterman, J.; de Vries, Theodorus J.A.; Samali, Bijan
2000-01-01
A self-tuning procedure is proposed for an active structural element with collocated sensing and actuation (a so-called ‘Smart Disc’). The procedure aims at optimal active damping by means of Integral Force Feedback control. In case the behavior of the structure to be damped may be described by a
Self-Tuning Speed Regulator for CVC Induction Motor Drive
DEFF Research Database (Denmark)
Bidstrup, N.; Rasmussen, Henrik; Knudsen, Torben
1994-01-01
A self-tuning speed regulator for a current vector controlled induction motor drive has been designed.......A self-tuning speed regulator for a current vector controlled induction motor drive has been designed....
Self Tuning Techniques on PLC Background and Control Systems With Self Tuning Methods Design
Directory of Open Access Journals (Sweden)
Jiri Koziorek
2010-01-01
Full Text Available Advanced Process Control techniques have become standard functions of distributed control systems. Self tuning methods belong to Advanced Process Control (APC techniques. APC techniques contain software packages for advanced control based on mathematical methods. APC tools are designed to increase the process capacity, yield and quality of products. Most of nowadays digital industry regulators and PLCs are provided with some kind of the self tuning constant algorithm. Practical part of the paper deals with design of the control systems which contain self tuning regulator. A control system with PID Self Tuner by Siemens and with visualization in WinCC is designed. There is a description of an implementation of the PID regulator as a function block which can be also used for extension control functions. Control systems for relay and moment self tuner with visualizations in WinCC are also designed.
Self-tuning tuned mass damper (TMD)
Griffin, Steven
2017-04-01
Tuned mass dampers (TMD) are heavily damped resonant devices which add damping to lightly damped, vibrational modes of a structure by dynamically coupling into the lightly damped modes. In practice, a TMD is a damped spring/mass resonator that is tuned so that its frequency is close to a lightly damped mode on the host structure. The TMD is attached to the host structure at a location of large amplitude motion for the mode to be dampened, and its motion is coupled into the host structure's motion. If the TMD is tuned correctly, two damped vibrational modes result, which take the place of the original lightly damped mode of the host structure and heavily damped mode of the TMD. Since aerospace structures tend to respond unfavorably at lightly damped modes in the presence of a dynamic disturbance environment, introduction of one or several TMDs can greatly reduce the dynamic response of a structure by damping problematic modes. A self-tuning TMD is described that can perform all the steps necessary to automatically tune itself and minimize the response of a structure with lightly damped modes and a dynamic excitation. The self-tuning TMD concept introduced here uses a voice coil / magnet combination as -an actuator which enables an innovative stiffness adjustment mechanism -a loss mechanism for the tuned mass damper -a means of excitation for identifying lightly damped modes of the host structure Along with an accelerometer and a tethered power supply/computer, the self-tuning TMD can automatically identify and damp lightly damped modes.
Armstrong, Jeffrey B.; Simon, Donald L.
2012-01-01
Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear
More self-tuning solutions with $H_{MNPQ}$
Kim, J E; Kim, Jihn E.; Lee, Hyun Min
2002-01-01
We find more self-tuning solutions by introducing a general form for Lagrangian of a 3-index antisymmetric tensor field $A_{MNP}$ in the RS II model. In particular, for the logarithmic Lagrangian, $\\propto\\log(-H^2)$, we obtained a closed form weak self-tuning solution.
Self Tuning Scalar Fields in Spherically Symmetric Spacetimes
Appleby, Stephen
2015-01-01
We search for self tuning solutions to the Einstein-scalar field equations for the simplest class of `Fab-Four' models with constant potentials. We first review the conditions under which self tuning occurs in a cosmological spacetime, and by introducing a small modification to the original theory - introducing the second and third Galileon terms - show how one can obtain de Sitter states where the expansion rate is independent of the vacuum energy. We then consider whether the same self tuning mechanism can persist in a spherically symmetric inhomogeneous spacetime. We show that there are no asymptotically flat solutions to the field equations in which the vacuum energy is screened, other than the trivial one (Minkowski space). We then consider the possibility of constructing Schwarzschild de Sitter spacetimes for the modified Fab Four plus Galileon theory. We argue that the only model that can successfully screen the vacuum energy in both an FLRW and Schwarzschild de Sitter spacetime is one containing `John...
A bilinear self-tuning controller for multimachine transient stability
Energy Technology Data Exchange (ETDEWEB)
Rajkumar, V.; Zhu, W.; Mohler, R.R.; Spee, R. (Oregon State Univ., Corvallis, OR (United States). Dept. of Electrical and Computer Engineering); Mittelstadt, W.A. (Bonneville Power Administration, Portland, OR (United States)); Maratukulam, D. (Electric Power Research Inst., Palo Alto, CA (United States))
1994-08-01
Bilinear time-series model-based self-tuning control is proposed for a multimachine power system, controlled by a single variable series capacitor. When the faults of concern are large and de-stabilizing, it is proposed that nonlinear model-based controllers can enhance the region of stability of the power system, and return the states to their stable equilibrium. A simple predictive nonlinear self-tuning controller using local measurements of relative rotor angles is examined. It is shown to perform well in stabilizing different faults on a 45-bus, 17-generator low order model with the dynamic characteristics of the Western System Coordinating Council (WSCC) system.
MIMO Self-Tuning Control of Chemical Process Operation
DEFF Research Database (Denmark)
Hallager, L.; Jørgensen, S. B.; Goldschmidt, L.
1984-01-01
The problem of selecting a feasible model structure for a MIMO self-tuning controller (MIMOSC) is addressed. The dependency of the necessary structure complexity in relation to the specific process operating point is investigated. Experimental results from a fixed-bed chemical reactor are used...
DEFF Research Database (Denmark)
Mailund, Thomas
The thesis describes the sweep-line method, a newly developed reduction method for alleviating the state explosion problem inherent in explicit-state state space exploration. The basic idea underlying the sweep-line method is, when calculating the state space, to recognise and delete states...... that are not reachable from the currently unprocessed states. Intuitively we drag a sweep-line through the state space with the invariant that all states behind the sweep-line have been processed and are unreachable from the states in front of the sweep-line. When calculating the state space of a system we iteratively...
DEFF Research Database (Denmark)
Mailund, Thomas
The thesis describes the sweep-line method, a newly developed reduction method for alleviating the state explosion problem inherent in explicit-state state space exploration. The basic idea underlying the sweep-line method is, when calculating the state space, to recognise and delete states...... that are not reachable from the currently unprocessed states. Intuitively we drag a sweep-line through the state space with the invariant that all states behind the sweep-line have been processed and are unreachable from the states in front of the sweep-line. When calculating the state space of a system we iteratively...
Neural Networks for Self-tuning Control Systems
Directory of Open Access Journals (Sweden)
A. Noriega Ponce
2004-01-01
Full Text Available In this paper, we presented a self-tuning control algorithm based on a three layers perceptron type neural network. The proposed algorithm is advantageous in the sense that practically a previous training of the net is not required and some changes in the set-point are generally enough to adjust the learning coefficient. Optionally, it is possible to introduce a self-tuning mechanism of the learning coefficient although by the moment it is not possible to give final conclusions about this possibility. The proposed algorithm has the special feature that the regulation error instead of the net output error is retropropagated for the weighting coefficients modifications.
A self-tuning phase-shifting algorithm for interferometry.
Estrada, Julio C; Servin, Manuel; Quiroga, Juan A
2010-02-01
In Phase Stepping Interferometry (PSI) an interferogram sequence having a known, and constant phase shift between the interferograms is required. Here we take the case where this constant phase shift is unknown and the only assumption is that the interferograms do have a temporal carrier. To recover the modulating phase from the interferograms, we propose a self-tuning phase-shifting algorithm. Our algorithm estimates the temporal frequency first, and then this knowledge is used to estimate the interesting modulating phase. There are several well known iterative schemes published before, but our approach has the unique advantage of being very fast. Our new temporal carrier, and phase estimator is capable of obtaining a very good approximation of their temporal carrier in a single iteration. Numerical experiments are given to show the performance of this simple yet powerful self-tuning phase shifting algorithm.
A model for self-tuning the cosmological constant
Kim, J E; Lee, H M; Kim, Jihn E.; Kyae, Bumseok; Lee, Hyun Min
2001-01-01
The vanishing cosmological constant in the four dimensional space-time is obtained in a 5D Randall-Sundrum model with a brane (B1) located at $y=0$. The matter fields can be located at the brane. For settling any vacuum energy generated at the brane to zero, we need a three index antisymmetric tensor field $A_{MNP}$ with a specific form for the Lagrangian. For the self-tuning mechanism, the bulk cosmological constant should be negative.
Fab 5: Noncanonical Kinetic Gravity, Self Tuning, and Cosmic Acceleration
Appleby, Stephen A; Linder, Eric V
2012-01-01
We investigate circumstances under which one can generalize Horndeski's most general scalar-tensor theory of gravity. Specifically we demonstrate that a nonlinear combination of purely kinetic gravity terms can give rise to an accelerating universe without the addition of extra propagating degrees of freedom on cosmological backgrounds, and exhibit self tuning to bring a large cosmological constant under control. This nonlinear approach leads to new properties that may be instructive for exploring the behaviors of gravity.
Self-Tuning Blind Identification and Equalization of IIR Channels
Directory of Open Access Journals (Sweden)
Bose Tamal
2003-01-01
Full Text Available This paper considers self-tuning blind identification and equalization of fractionally spaced IIR channels. One recursive estimator is used to generate parameter estimates of the numerators of IIR systems, while the other estimates denominator of IIR channel. Equalizer parameters are calculated by solving Bezout type equation. It is shown that the numerator parameter estimates converge (a.s. toward a scalar multiple of the true coefficients, while the second algorithm provides consistent denominator estimates. It is proved that the equalizer output converges (a.s. to a scalar version of the actual symbol sequence.
General self-tuning solutions and no-go theorem
Energy Technology Data Exchange (ETDEWEB)
Förste, Stefan [Bethe Center for Theoretical Physics and Physikalisches Institut der Universität Bonn, Nussallee 12, 53115 Bonn (Germany); Kim, Jihn E. [Department of Physics, Kyung Hee University, Seoul 130-701 (Korea, Republic of); Lee, Hyun Min, E-mail: forste@th.physik.uni-bonn.de, E-mail: jihnekim@gmail.com, E-mail: hyun.min.lee@kias.re.kr [School of Physics, KIAS, Seoul 130-722 (Korea, Republic of)
2013-03-01
We consider brane world models with one extra dimension. In the bulk there is in addition to gravity a three form gauge potential or equivalently a scalar (by generalisation of electric magnetic duality). We find classical solutions for which the 4d effective cosmological constant is adjusted by choice of integration constants. No go theorems for such self-tuning mechanism are circumvented by unorthodox Lagrangians for the three form respectively the scalar. It is argued that the corresponding effective 4d theory always includes tachyonic Kaluza-Klein excitations or ghosts. Known no go theorems are extended to a general class of models with unorthodox Lagrangians.
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Jin Baoquan
2012-11-01
Full Text Available Aiming at the problem of tracking performance degradation of hydraulic EPC system caused by time-varying inertia parameters, nonlinear and external disturbances, the proportion sliding mode control of fuzzy self-tuning gain was proposed. The EPC system state space model on deviation parameters was established and the main feedback sliding mode switching algorithm was designed. The fuzzy method was used to dynamically adjust the proportion sliding mode switching gain by product of the switching function and its derivative state and to adaptive compensate for the uncertainty of the system. At the same time to ensure the effectiveness of the design strategy, the controller model and physical model worked together to simulate the actual conditions. The fixed switching gain switch was, respectively greater and smaller and compared with the fuzzy self-tuning gain, in which the latter achieves a fast and coordinated control of chattering. The results show that after comprehensive consideration all interference the system is stable, fast response, high accuracy and to solve chattering problem caused by the traditional large switching gain of proportion sliding mode.
Reciprocity and self-tuning relations without wrapping
Fioravanti, Davide; Rossi, Marco
2015-01-01
We consider scalar Wilson operators of ${\\cal N}=4$ SYM at high spin, $s$, and generic twist in the multi-color limit. We show that the corresponding (non)linear integral equations (originating from the asymptotic Bethe Ansatz equations) respect certain 'reciprocity' and functional 'self-tuning' relations up to all terms $\\frac{1}{s(\\ln s)^n}$ (inclusive) at any fixed 't Hooft coupling $\\lambda$. Of course, this relation entails straightforwardly the well-known (homonymous) relations for the anomalous dimension at the same order in $s$. On this basis we give some evidence that wrapping corrections should enter the non-linear integral equation and anomalous dimension expansions at the next order $\\frac{(\\ln s)^{2}}{s^2}$, at fixed 't Hooft coupling, in such a way to re-establish the aforementioned relation (which fails otherwise).
Passively Self-Tuning Piezoelectric Energy Harvesting System
Gregg, C. G.; Pillatsch, P.; Wright, P. K.
2014-11-01
Real world systems that are candidates for vibrational energy harvesting rarely vibrate at a single frequency, nor are these frequencies constant over time. This necessitates that vibration harvesters operate over a wide bandwidth or tune their resonance. Most tunable devices require additional energy or active control to achieve resonance over various frequencies. This work presents a passively self-tuning energy harvester that autonomously adapts its resonant frequency to the input without requiring additional energy. The system consists of a clamped- clamped beam, a movable proof mass, and a piezoelectric patch bonded to the underside of the beam. It demonstrated an open-circuit voltage output of 668 mVrms at 160Hz, 0.65g input excitation. Discrepancies between displacement and voltage magnification factors upon tuning at higher frequencies are discussed, as well as instabilities of the system and sensitivity to proof mass characteristics.
A Learning Framework for Self-Tuning Histograms
Viswanathan, Raajay; Laxman, Srivatsan; Arasu, Arvind
2011-01-01
We propose a general learning theoretic formulation for estimating self-tuning histograms. Our formulation uses query feedback from a workload as training data to estimate a histogram that minimizes the expected error on future queries. Our formulation is flexible in the sense that it allows the design and comparison of different methods (possibly specialized for different settings). We first study the simple class of equi-width histograms in our learning framework and present a learning algorithm (EquiHist) that is competitive in many settings and that has formal error guarantees. We then go beyond equi-width histograms and present a novel learning algorithm (SpHist) for estimating general histograms. Here we use Haar wavelets to reduce the problem of learning histograms to a sparse vectory recovery problem. Both algorithms have multiple advantages over existing methods: 1) simple and scalable extensions to multi-dimensional data, 2) scale with number of histogram buckets and size of query feedback, 3) natur...
Self-tuning and the derivation of the Fab Four
Charmousis, Christos; Padilla, Antonio; Saffin, Paul M
2011-01-01
We have recently proposed a special class of scalar tensor theories known as the Fab Four. These arose from attempts to analyse the cosmological constant problem within the context of Horndeski's most general scalar tensor theory. The Fab Four together give rise to a model of self-tuning, with the relevant solutions evading Weinberg's no-go theorem by relaxing the condition of Poincare invariance in the scalar sector. The Fab Four are made up of four geometric terms in the action with each term containing a free potential function of the scalar field. In this paper we rigorously derive this model from the general model of Horndeski, proving that the Fab Four represents the only classical scalar tensor theory of this type that has any hope of tackling the cosmological constant problem. We present the full equations of motion for this theory, and give an heuristic argument to suggest that one might be able to keep radiative corrections under control. We also give the Fab Four in terms of the potentials presente...
A Self-tuning Fuzzy Queue Management Algorithm for Congestion Control
Institute of Scientific and Technical Information of China (English)
Zhang Jingyuan(张敬辕); Xie Jianying
2004-01-01
This letter presents an effective self-tuning fuzzy queue management algorithm for congestion control. With the application of the algorithm, routers in IP network regulate its packet drop probability by a self-tuning fuzzy controller. The main advantage of the algorithm is that, with the parameter self-tuning mechanism, queue length can keep stable in a variety of network environments without the difficulty of parameter configuration. Simulations show that the algorithm is efficient, stable and outperforms the popular RED queue management algorithm significantly.
Institute of Scientific and Technical Information of China (English)
周封; 金丽斯; 刘健; 张再利
2012-01-01
A wind power forecasting method generally provides estimation of future wind power as a single point forecast,while most of the decision-making processes in the electric power systems management require more information than a single value.A new wind power forecasting method is proposed on the basis of discrete time Markov chain models.Aiming at the randomness of power data,a 4-state space is divided on the equal length,and a one-order and two-step hybrid model is built in each state space.The coefficient weights of the hybrid model are obtained by using accelerating genetic algorithm.Since the model analyzes power data directly,it efficiently avoids amplifying errors in converting wind speed forecasts into power forecasts.The hybrid models of four types and the new prediction error formula are presented.Analysis and numerical examples show that the prediction accuracy of hybrid models（N=102） is better than that of persistence method（PM） model,and the corresponding point prediction and probability distribution estimation are also presented.%现有风电功率预测方法只提供功率的单点预测值,但在电力市场的决策过程中却需要更多的信息。文中提出一种基于离散时间Markov链理论的新功率预测模型。针对功率数据的无规律性,采用等分法划分了4种状态空间,并对每种状态空间都建立1阶和2步混合Markov模型,模型权重系数采用加速遗传算法求解。该模型直接对风电功率数据进行数值分析,有效避免通过风速预测再转换为功率时带来的误差累积。给出4种混合模型和最新的评价误差公式。分析和算例表明,N为102时混合模型预测精度高于持续法模型,并给出了单点预测值和概率分布值。
Directory of Open Access Journals (Sweden)
Thomas Doan
2011-05-01
Full Text Available This paper uses several examples to show how the econometrics program RATS can be used to analyze state space models. It demonstrates Kalman filtering and smoothing, estimation of hyperparameters, unconditional and conditional simulation. It also provides a more complicated example where a dynamic simultaneous equations model is transformed into a proper state space representation and its unknown parameters are estimated.
Model-Based Self-Tuning Multiscale Method for Combustion Control
Le, Dzu, K.; DeLaat, John C.; Chang, Clarence T.; Vrnak, Daniel R.
2006-01-01
A multi-scale representation of the combustor dynamics was used to create a self-tuning, scalable controller to suppress multiple instability modes in a liquid-fueled aero engine-derived combustor operating at engine-like conditions. Its self-tuning features designed to handle the uncertainties in the combustor dynamics and time-delays are essential for control performance and robustness. The controller was implemented to modulate a high-frequency fuel valve with feedback from dynamic pressure sensors. This scalable algorithm suppressed pressure oscillations of different instability modes by as much as 90 percent without the peak-splitting effect. The self-tuning logic guided the adjustment of controller parameters and converged quickly toward phase-lock for optimal suppression of the instabilities. The forced-response characteristics of the control model compare well with those of the test rig on both the frequency-domain and the time-domain.
Design and Simulation of PID parameters self-tuning based on DC speed regulating system
Directory of Open Access Journals (Sweden)
Feng Wei Jie
2016-01-01
Full Text Available The DC speed regulating system has many difficult issues such as system parameters and PID control parameters are difficult to determine. On the basis of model for a single closed-loop DC speed regulating system, this paper puts forward a method of PID parameters self-tuning based on the step response detection and reduced order equivalent. First, detect system step response and get response parameters. Then equal it to a second order system model, and achieve optimal PID control parameters based on optimal second order system to realize of PID parameters self-tuning. The PID parameters self-tuning process of DC speed regulating system is simulated with the help of MATLAB/Simulink. The simulation results show that the method is simple and effective. The system can obtain good dynamic and static performance when the PID parameters are applied to DC speed regulating system.
Control of a Quadrotor Using a Smart Self-Tuning Fuzzy PID Controller
Directory of Open Access Journals (Sweden)
Deepak Gautam
2013-11-01
Full Text Available This paper deals with the modelling, simulation-based controller design and path planning of a four rotor helicopter known as a quadrotor. All the drags, aerodynamic, coriolis and gyroscopic effect are neglected. A Newton-Euler formulation is used to derive the mathematical model. A smart self-tuning fuzzy PID controller based on an EKF algorithm is proposed for the attitude and position control of the quadrotor. The PID gains are tuned using a self-tuning fuzzy algorithm. The self-tuning of fuzzy parameters is achieved based on an EKF algorithm. A smart selection technique and exclusive tuning of active fuzzy parameters is proposed to reduce the computational time. Dijkstra’s algorithm is used for path planning in a closed and known environment filled with obstacles and/or boundaries. The Dijkstra algorithm helps avoid obstacle and find the shortest route from a given initial position to the final position.
Football Shaped Extra Dimensions and the Absence of Self-Tuning
Garriga, J; Garriga, Jaume; Porrati, Massimo
2004-01-01
There have been some recent claims that brane-worlds of co-dimension two in a 6D bulk with compact extra dimensions may lead to self-tuning of the effective 4D cosmological constant. Here we show that if a phase transition occurs on a flat brane, so as to change its tension, then the brane will not remain flat. In other words, there is really no self-tuning in such models, which can in fact be understood in four-dimensional terms and are therefore subject to Weinberg's no-go theorem.
Modeling and Non-Linear Self-Tuning Robust Trajectory Control of an Autonomous Underwater Vehicle
Directory of Open Access Journals (Sweden)
Thor Inge Fossen
1988-10-01
Full Text Available A non-linear self-tuning algorithm is demonstrated for an autonomous underwater vehicle. Tighter control is achieved by a non-linear parameter identification algorithm which reduces the parameter uncertainty bounds. Expensive hydrodynamic tests for parameter determination can thus be avoided. Excellent tracking performance and robustness to parameter uncertainty are guaranteed through a robust control strategy based on the estimated parameters. The nonlinear control law is highly robust for imprecise models and the neglected dynamics. The non-linear self-tuning control strategy is simulated for the horizontal positioning of an underwater vehicle.
A comparison of three self-tuning control algorithms developed for the Bristol-Babcock controller
Energy Technology Data Exchange (ETDEWEB)
Tapp, P.A.
1992-04-01
A brief overview of adaptive control methods relating to the design of self-tuning proportional-integral-derivative (PID) controllers is given. The methods discussed include gain scheduling, self-tuning, auto-tuning, and model-reference adaptive control systems. Several process identification and parameter adjustment methods are discussed. Characteristics of the two most common types of self-tuning controllers implemented by industry (i.e., pattern recognition and process identification) are summarized. The substance of the work is a comparison of three self-tuning proportional-plus-integral (STPI) control algorithms developed to work in conjunction with the Bristol-Babcock PID control module. The STPI control algorithms are based on closed-loop cycling theory, pattern recognition theory, and model-based theory. A brief theory of operation of these three STPI control algorithms is given. Details of the process simulations developed to test the STPI algorithms are given, including an integrating process, a first-order system, a second-order system, a system with initial inverse response, and a system with variable time constant and delay. The STPI algorithms` performance with regard to both setpoint changes and load disturbances is evaluated, and their robustness is compared. The dynamic effects of process deadtime and noise are also considered. Finally, the limitations of each of the STPI algorithms is discussed, some conclusions are drawn from the performance comparisons, and a few recommendations are made. 6 refs.
A comparison of three self-tuning control algorithms developed for the Bristol-Babcock controller
Energy Technology Data Exchange (ETDEWEB)
Tapp, P.A.
1992-04-01
A brief overview of adaptive control methods relating to the design of self-tuning proportional-integral-derivative (PID) controllers is given. The methods discussed include gain scheduling, self-tuning, auto-tuning, and model-reference adaptive control systems. Several process identification and parameter adjustment methods are discussed. Characteristics of the two most common types of self-tuning controllers implemented by industry (i.e., pattern recognition and process identification) are summarized. The substance of the work is a comparison of three self-tuning proportional-plus-integral (STPI) control algorithms developed to work in conjunction with the Bristol-Babcock PID control module. The STPI control algorithms are based on closed-loop cycling theory, pattern recognition theory, and model-based theory. A brief theory of operation of these three STPI control algorithms is given. Details of the process simulations developed to test the STPI algorithms are given, including an integrating process, a first-order system, a second-order system, a system with initial inverse response, and a system with variable time constant and delay. The STPI algorithms' performance with regard to both setpoint changes and load disturbances is evaluated, and their robustness is compared. The dynamic effects of process deadtime and noise are also considered. Finally, the limitations of each of the STPI algorithms is discussed, some conclusions are drawn from the performance comparisons, and a few recommendations are made. 6 refs.
Self-tuning Generalized Predictive Control applied to terrain following flight
Hess, R. A.; Jung, Y. C.
1989-01-01
Generalized Predictive Control (GPC) describes an algorithm for the control of dynamic systems in which a control input is generated which minimizes a quadratic cost function consisting of a weighted sum of errors between desired and predicted future system output and future predicted control increments. The output predictions are obtained from an internal model of the plant dynamics. Self-tuning GPC refers to an implementation of the GPC algorithm in which the parameters of the internal model(s) are estimated on-line and the predictive control law tuned to the parameters so identified. The self-tuning GPC algorithm is applied to a problem of rotorcraft longitudinal/vertical terrain-following flight. The ability of the algorithm to tune to the initial vehicle parameters and to successfully adapt to a stability augmentation failure is demonstrated. Flight path performance is compared to a conventional, classically designed flight path control system.
Study on self-tuning pole assignment speed control of an ultrasonic motor.
Shi, Jingzhuo; Bo, Liu; Yu, Zhang
2011-10-01
Ultrasonic motors have a heavy nonlinearity, which varies with driving conditions. The nonlinearity is a problem as an accurate motion actuator for industrial applications and it is important to eliminate the nonlinearity in order to improve the control performance. In general, complicated control strategies are used to deal with the nonlinearity of ultrasonic motors. This paper proposes a new speed control scheme for ultrasonic motors to overcome the nonlinearity employing a simplified self-tuning control. The speed control model which can reflect the main nonlinear characteristics is obtained using a system identification method based on the step response. Then, a pole assignment speed controller is designed. To avoid the influence of the motor's nonlinearity on the speed control performance, a control parameters' on-line self-tuning strategy utilizing the gain of the model is designed. The proposed control strategy is realized using a DSP circuit, and experiments prove the validity of the proposed speed control scheme.
Comparative study of a learning fuzzy PID controller and a self-tuning controller.
Kazemian, H B
2001-01-01
The self-organising fuzzy controller is an extension of the rule-based fuzzy controller with an additional learning capability. The self-organising fuzzy (SOF) is used as a master controller to readjust conventional PID gains at the actuator level during the system operation, copying the experience of a human operator. The application of the self-organising fuzzy PID (SOF-PID) controller to a 2-link non-linear revolute-joint robot-arm is studied using path tracking trajectories at the setpoint. For the purpose of comparison, the same experiments are repeated by using the self-tuning controller subject to the same data supplied at the setpoint. For the path tracking experiments, the output trajectories of the SOF-PID controller followed the specified path closer and smoother than the self-tuning controller.
General second order scalar-tensor theory, self tuning, and the Fab Four
Charmousis, Christos; Padilla, Antonio; Saffin, Paul M
2011-01-01
Starting from the most general scalar-tensor theory with second order field equations in four dimensions, we establish the unique action that will allow for the existence of a consistent self-tuning mechanism on FLRW backgrounds, and show how it can be understood as a combination of just four base Lagrangians with an intriguing geometric structure dependent on the Ricci scalar, the Einstein tensor, the double dual of the Riemann tensor and the Gauss-Bonnet combination. Spacetime curvature can be screened from the net cosmological constant at any given moment because we allow the scalar field to break Poincar\\'e invariance on the self-tuning vacua, thereby evading the Weinberg no-go theorem. We show how the four arbitrary functions of the scalar field combine in an elegant way opening up the possibility of obtaining non-trivial cosmological solutions.
Self-Tuning Vibration Control of a Rotational Flexible Timoshenko Arm Using Neural Networks
Directory of Open Access Journals (Sweden)
Minoru Sasaki
2012-01-01
Full Text Available A self-tuning vibration control of a rotational flexible arm using neural networks is presented. To the self-tuning control system, the control scheme consists of gain tuning neural networks and a variable-gain feedback controller. The neural networks are trained so as to make the root moment zero. In the process, the neural networks learn the optimal gain of the feedback controller. The feedback controller is designed based on Lyapunov's direct method. The feedback control of the vibration of the flexible system is derived by considering the time rate of change of the total energy of the system. This approach has the advantage over the conventional methods in the respect that it allows one to deal directly with the system's partial differential equations without resorting to approximations. Numerical and experimental results for the vibration control of a rotational flexible arm are discussed. It verifies that the proposed control system is effective at controlling flexible dynamical systems.
Self-tuning Solution of Cosmological Constant in RS-II Model and Goldstone Boson
Kim, J E
2001-01-01
I give a review on the self-tuning solution of the cosmological constant in a 5D RS-II model using a three index antisymmetric tensor field $A_{MNP}$. The three index antisymmetric tensor field can be the fundamental one appearing in 11D supergravity. Also, the dual of its field strength $H_{MNPQ}$, being a massless scalar, may be interpreted as a Goldstone boson of some spontaneously broken global symmetry.
The Redundant Arm Self-motion Control Based on Self-tuning Fuzzy PID Controller
Institute of Scientific and Technical Information of China (English)
Liu Yu(刘宇); Sun Lining; Du Zhijiang
2004-01-01
A fuzzy control algorithm based on self-tuning PID proportional factor is presented. To a certain degree, it overcomes robot motion control's nonlinearity and uncertainty caused by joints coupled and friction, and decreases overshoot of end manipulator's tracking desired curves. The controller's structure is very simple but effective. With this control method, a 7-DOF redundant arm's self-motion developed by the authors is investigated. Research results show that the said controller restrains track overshoot and possesses preferable merits.
APPLICATION OF FUZZY CONTROL METHOD WITH SELF-TUNING FACTOR IN JIGGERS DISCHARGING
Institute of Scientific and Technical Information of China (English)
杨洁明; 魏晋宏; 刘素芬
2000-01-01
Adopting the strategy of fuzzy control with self-tuning factor within whole universe of discourse, a kind of fuzzy control method for jigger discharging is put forward. This method has many advantages over the conventional PID controller in terms of response speed, stability and robustness. It is effective to restrain the jig bed from over-thick or empty, and the stability of the bed is markedly improved. The good results are obtained in factory tests.
A self-tuning digital-driver for open-loop control of stepping-motors
Energy Technology Data Exchange (ETDEWEB)
Okada, T.; Hori, N. [Tsukuba Univ., Tsukuba, Ibarki (Japan). Intelligent Interaction Technologies, Graduate School of Systems and Information Engineering
2010-08-13
Stepping motors are commonly used as actuators in industrial control applications because of the their high torque-to-weight ratio, precise and quick positioning, and self-hold capability. They can also be controlled in an open-loop fashion using a proper driver. This paper described the design of an experimental digital driver, which contained both fixed and adjustable gains. It also discussed a current regulation problem for a stepping-motor which underwent rapid and large changes in its gains. An open-loop nature of a stepping motor could be preserved using only signals that are readily available in the driver and do not require neither the angular position of the shaft nor its rate. Specifically, the paper discussed the stepping motor and driver, parameter variations, dead-zone compensation, nonlinear digital filter, and anti-aliasing filter. The self-tuning algorithm was also presented with particular reference to background controller design and self tuning pre-compensator. The experiments and parameters used in the experiments were also described. It was concluded that stable and safe operations can be achieved using a combination of fixed controller blocks and precompensator blocks with self-tuning parameters, which change as the speed of rotation varies. For this method to work, it is important to include a dead-zone compensator and a nonlinear digital filter and an anti-aliasing filter. 7 refs., 1 tab., 15 figs.
Design of a self-tuning regulator for temperature control of a polymerization reactor.
Vasanthi, D; Pranavamoorthy, B; Pappa, N
2012-01-01
The temperature control of a polymerization reactor described by Chylla and Haase, a control engineering benchmark problem, is used to illustrate the potential of adaptive control design by employing a self-tuning regulator concept. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. The conventional cascade control provides a robust operation, but often lacks in control performance concerning the required strict temperature tolerances. The self-tuning control concept presented in this contribution solves the problem. This design calculates a trajectory for the cooling jacket temperature in order to follow a predefined trajectory of the reactor temperature. The reaction heat and the heat transfer coefficient in the energy balance are estimated online by using an unscented Kalman filter (UKF). Two simple physically motivated relations are employed, which allow the non-delayed estimation of both quantities. Simulation results under model uncertainties show the effectiveness of the self-tuning control concept.
Semiactive Self-Tuning Fuzzy Logic Control of Full Vehicle Model with MR Damper
Directory of Open Access Journals (Sweden)
Mahmut Paksoy
2014-09-01
Full Text Available Intelligent controllers are studied for vibration reduction of a vehicle consisting in a semiactive suspension system with a magnetorheological(MR damper. The vehicle is modeled with seven degrees of freedom as a full vehicle model. The semiactive suspension system consists of a linear spring and an MR damper. MR damper is modeled using Bouc-Wen hysteresis phenomenon and applied to a full vehicle model. Fuzzy Logic based controllers are designed to determine the MR damper voltage. Fuzzy Logic and Self-Tuning Fuzzy Logic controllers are applied to the semiactive suspension system. Results of the system are investigated by simulation studies in MATLAB-Simulink environment. The performance of the semiactive suspension system is analyzed with and without control. Simulation results showed that both Fuzzy Logic and Self-Tuning Fuzzy Logic controllers perform better compared to uncontrolled case. Furthermore, Self-Tuning Fuzzy Logic controller displayed a greater improvement in vibration reduction performance compared to Fuzzy Logic controller.
Direct Drive Electro-hydraulic Servo Control System Design with Self-Tuning Fuzzy PID Controller
Directory of Open Access Journals (Sweden)
Wang Yeqin
2013-06-01
Full Text Available According to the nonlinear and time-varying uncertainty characteristics of direct drive electro-hydraulic servo control system, a self-tuning fuzzy PID control method with speed change integral and differential ahead optimizing operator is put forward by combining fuzzy inference and traditional PID control in this paper.The rule of fuzzy logic is designed, the membership function of the fuzzy subsets is determined and lookup table method is used to correcte the PID parameters in real-time. Finally the simulation is conducted with the typical input signal, such as tracking step, sine etc. The simulation results show that，the self-tuning fuzzy PID control system can effectively improve the dynamic characteristic when the system is out of the range of the operating point compared with the traditional PID control system, there is obvious improvement in the indexes of rapidity, stability and accuracy, and fuzzy self-tuning PID Control is more robust, and more suitable for direct drive electro-hydraulic servo system.
Neuro-Self Tuning Adaptive Controller for Non-Linear Dynamical Systems
Directory of Open Access Journals (Sweden)
Ahmed Sabah Abdul Ameer Al-Araji
2005-01-01
Full Text Available In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual output of the system & to find the jacobain of the system. Which appears to be of critical importance parameter as it is used for the feedback controller and the second stage is learned on-line to modify the weights of the model in order to control the variable parameters that will occur to the system. A back propagation neural network is applied to learn the control structure for self-tuning PID type neuro-controller. Where the neural network is used to minimize the error function by adjusting the PID gains. Simulation results show that the self-tuning PID scheme can deal with a large unknown nonlinearity.
Braun, David J.; Sutas, Andrius; Vijayakumar, Sethu
2017-01-01
Theory predicts that parametrically excited oscillators, tuned to operate under resonant condition, are capable of large-amplitude oscillation useful in diverse applications, such as signal amplification, communication, and analog computation. However, due to amplitude saturation caused by nonlinearity, lack of robustness to model uncertainty, and limited sensitivity to parameter modulation, these oscillators require fine-tuning and strong modulation to generate robust large-amplitude oscillation. Here we present a principle of self-tuning parametric feedback excitation that alleviates the above-mentioned limitations. This is achieved using a minimalistic control implementation that performs (i) self-tuning (slow parameter adaptation) and (ii) feedback pumping (fast parameter modulation), without sophisticated signal processing past observations. The proposed approach provides near-optimal amplitude maximization without requiring model-based control computation, previously perceived inevitable to implement optimal control principles in practical application. Experimental implementation of the theory shows that the oscillator self-tunes itself near to the onset of dynamic bifurcation to achieve extreme sensitivity to small resonant parametric perturbations. As a result, it achieves large-amplitude oscillations by capitalizing on the effect of nonlinearity, despite substantial model uncertainties and strong unforeseen external perturbations. We envision the present finding to provide an effective and robust approach to parametric excitation when it comes to real-world application.
Cosmological self-tuning and local solutions in generalized Horndeski theories
Babichev, Eugeny; Esposito-Farèse, Gilles
2017-01-01
We study both the cosmological self-tuning and the local predictions (inside the Solar System) of the most general shift-symmetric beyond Horndeski theory. We first show that the cosmological self-tuning is generic in this class of theories: By adjusting a mass parameter entering the action, a large bare cosmological constant can effectively be reduced to a small observed one. Requiring then that the metric should be close enough to the Schwarzschild solution in the Solar System, to pass the experimental tests of general relativity, and taking into account the renormalization of Newton's constant, we select a subclass of models which presents all desired properties: It is able to screen a big vacuum energy density, while predicting an exact Schwarzschild-de Sitter solution around a static and spherically symmetric source. As a by-product of our study, we identify a general subclass of beyond Horndeski theory for which regular self-tuning black hole solutions exist, in the presence of a time-dependent scalar field. We discuss possible future development of the present work.
Cosmological self-tuning and local solutions in generalized Horndeski theories
Babichev, Eugeny
2016-01-01
We study both the cosmological self-tuning and the local predictions (inside the Solar system) of the most general shift-symmetric beyond Horndeski theory. We first show that the cosmological self-tuning is generic in this class of theories: By adjusting a mass parameter entering the action, a large bare cosmological constant can be effectively reduced to a small observed one. Requiring then that the metric should be close enough to the Schwarzschild solution in the Solar system, to pass the experimental tests of general relativity, and taking into account the renormalization of Newton's constant, we select a subclass of models which presents all desired properties: It is able to screen a big vacuum energy density, while predicting an exact Schwarzschild-de Sitter solution around a static and spherically symmetric source. As a by-product of our study, we identify a general subclass of beyond Horndeski theory for which regular self-tuning black hole solutions exist, in presence of a time-dependent scalar field...
My Life with State Space Models
DEFF Research Database (Denmark)
Lundbye-Christensen, Søren
2007-01-01
. The conceptual idea behind the state space model is that the evolution over time in the object we are observing and the measurement process itself are modelled separately. My very first serious analysis of a data set was done using a state space model, and since then I seem to have been "haunted" by state space...
Chaotic queue-based genetic algorithm for design of a self-tuning fuzzy logic controller
Saini, Sanju; Saini, J. S.
2012-11-01
This paper employs a chaotic queue-based method using logistic equation in a non-canonical genetic algorithm for optimizing the performance of a self-tuning Fuzzy Logic Controller, used for controlling a nonlinear double-coupled system. A comparison has been made with a standard canonical genetic algorithm implemented on the same plant. It has been shown that chaotic queue-method brings an improvement in the performance of the FLC for wide range of set point changes by a more profound initial population spread in the search space.
On line diagnostics and self-tuning method for the fluidized bed temperature controller
Directory of Open Access Journals (Sweden)
Porzuczek Jan
2016-03-01
Full Text Available The paper presents the method of on-line diagnostics of the bed temperature controller for the fluidized bed boiler. Proposed solution is based on the methods of statistical process control. Detected decrease of the bed temperature control quality is used to activate the controller self-tuning procedure. The algorithm that provides optimal tuning of the bed temperature controller is also proposed. The results of experimental verification of the presented method is attached. Experimental studies were carried out using the 2 MW bubbling fluidized bed boiler.
Self-tuning decoupled fusion Kalman filter based on the Riccati equation
Institute of Scientific and Technical Information of China (English)
Xiaojun SUN; Peng ZHANG; Zili DENG
2008-01-01
An online noise variance estimator for multi-sensor systems with unknown noise variances is proposed by using the correlation method. Based on the Riccati equa-tion and optimal fusion rule "weighted by scalars for state components, a self-tuning component decoupled informa-tion fusion Kalman filter is presented. It is proved that the filter converges to the optimal fusion Kalman filter in a realization by dynamic error system analysis method, so that it has asymptotic optimality. Its effectiveness is demon-strated by simulation for a tracking system with 3 sensors.
Design and simulation about a self-Tuning fuzzy-PID controller
Institute of Scientific and Technical Information of China (English)
ZHANG Yi; FU Wen-yong; LI Yan-hua; DENG Hao-wen; LIU Hong-chang
2009-01-01
Fuzzy logic has attracted the attention of structural control engineers during the last few years, because fuzzy logic can handle nonlinearities, uncertainties, and heuristic knowledge effectively and easily. In this paper, a self-Tuning fuzzy-PID control method which used the technology of the fuzzy control and PID control unified is presented. These techniques can visualize the results and processes for structure stress. These techniques will also provide convenience for engineers and users, and have high practical values. The MATLAB simulation result shows that the system precision and the efficiency are very high and the static error is small, and robustness was also validated.
Stochastic Modelling and Self Tuning Control of a Continuous Cement Raw Material Mixing System
Directory of Open Access Journals (Sweden)
Hannu T. Toivonen
1980-01-01
Full Text Available The control of a continuously operating system for cement raw material mixing is studied. The purpose of the mixing system is to maintain a constant composition of the cement raw meal for the kiln despite variations of the raw material compositions. Experimental knowledge of the process dynamics and the characteristics of the various disturbances is used for deriving a stochastic model of the system. The optimal control strategy is then obtained as a minimum variance strategy. The control problem is finally solved using a self-tuning minimum variance regulator, and results from a successful implementation of the regulator are given.
Self-Tuning Random Early Detection Algorithm to Improve Performance of Network Transmission
Directory of Open Access Journals (Sweden)
Jianyong Chen
2011-01-01
Full Text Available We use a discrete-time dynamical feedback system model of TCP/RED to study the performance of Random Early Detection (RED for different values of control parameters. Our analysis shows that the queue length is able to keep stable at a given target if the maximum probability pmax and exponential averaging weight w satisfy some conditions. From the mathematical analysis, a new self-tuning RED is proposed to improve the performance of TCP-RED network. The appropriate pmax is dynamically obtained according to history information of both pmax and the average queue size in a period of time. And w is properly chosen according to a linear stability condition of the average queue length. From simulations with ns-2, it is found that the self-tuning RED is more robust to stabilize queue length in terms of less deviation from the target and smaller fluctuation amplitude, compared to adaptive RED, Random Early Marking (REM, and Proportional-Integral (PI controller.
Fuzzy Self-Tuning PID Control of Hydrogen-Driven Pneumatic Artificial Muscle Actuator
Institute of Scientific and Technical Information of China (English)
Thanana Nuchkrua; Thananchai Leephakpreeda
2013-01-01
In this paper,a fuzzy self-tuning Proportional-Integral-Derivative (PID) control of hydrogen-driven Pneumatic Artificial Muscle (PAM) actuator is presented.With a conventional PID control,non-linear thermodynamics of the hydrogen-driven PAM actuator still highly affects the mechanical actuations itself,causing deyiation of desired tasks.The fuzzy self-tuning PID controller is systematically developed so as to achieve dynamic performance targets of the hydrogen-driven PAM actuator.The fuzzy rules based on desired characteristics of closed-loop control are designed to finely tune the PID gains of the controller under different operating conditions.The empirical models and properties of the hydrogen-driven PAM actuator are used as a genuine representation of mechanical actuations.A mass-spring-damper system is applied to the hydrogen-driven PAM actuator as a typical mechanical load during actuations.The results of the implementation show that the viability of the proposed method in actuating the hydrogen-driven PAM under mechanical loads is close to desired performance.
Heading Control for a Robotic Dolphin Based on a Self-Tuning Fuzzy Strategy
Directory of Open Access Journals (Sweden)
Zhiqiang Cao
2016-02-01
Full Text Available In this paper, a heading controller based on a self-tuning fuzzy strategy for a robotic dolphin is proposed to improve control accuracy and stability. The structure of the robotic dolphin is introduced and the turning motion is analysed. The analytic model indicates that the turning joint angle can be employed for the heading control. This non-linear model prevents the successful application of traditional model-based controllers. A fuzzy controller is proposed to realize the heading control in our work. It should be mentioned that the traditional fuzzy controller suffers from a distinguished steady-state error, due to the fact that the heading range is relatively large and the fuzzy controller's universe of discourse is fixed. To resolve this problem, a self-tuning mechanism is employed to adjust the input and output scaling factors according to the active working region in pursuit of favourable performance. Experimental results demonstrate the performance of the proposed controller in terms of steady-state error and robustness to interferences.
State Space Methods for Timed Petri Nets
DEFF Research Database (Denmark)
Christensen, Søren; Jensen, Kurt; Mailund, Thomas
2001-01-01
We present two recently developed state space methods for timed Petri nets. The two methods reconciles state space methods and time concepts based on the introduction of a global clock and associating time stamps to tokens. The first method is based on an equivalence relation on states which makes...... it possible to condense the usually infinite state space of a timed Petri net into a finite condensed state space without loosing analysis power. The second method supports on-the-fly verification of certain safety properties of timed systems. We discuss the application of the two methods in a number...
Institute of Scientific and Technical Information of China (English)
Zhiyun Zou; Dandan Zhao; Xinghong Liu; Yuqing Guo; Chen Guan; Wenqiang Feng; Ning Guo
2015-01-01
By taking advantage of the separation characteristics of nonlinear gain and dynamic sector inside a Hammerstein model, a novel pole placement self tuning control scheme for nonlinear Hammerstein system was put forward based on the linear system pole placement self tuning control algorithm. And the nonlinear Hammerstein system pole placement self tuning control (NL-PP-STC) algorithm was presented in detail. The identification ability of its parameter estimation algorithm of NL-PP-STC was analyzed, which was always identifiable in closed loop. Two particular problems including the selection of poles and the on-line estimation of model parameters, which may be met in applications of NL-PP-STC to real process control, were discussed. The control simulation of a strong nonlinear pH neutralization process was carried out and good control performance was achieved.
State-Space Formulation for Circuit Analysis
Martinez-Marin, T.
2010-01-01
This paper presents a new state-space approach for temporal analysis of electrical circuits. The method systematically obtains the state-space formulation of nondegenerate linear networks without using concepts of topology. It employs nodal/mesh systematic analysis to reduce the number of undesired variables. This approach helps students to…
Mean Shift Detection for State Space Models
Kuhn, J.; Mandjes, M.; Taimre, T.; Weber, T.; McPhee, M.J.; Anderssen, R.S.
2015-01-01
In this paper we develop and validate a procedure for testing against a shift in mean in the observations and hidden state sequence of state space models with Gaussian noise. State space models are popular for modelling stochastic networks as they allow to take into account that observations of the
Continuous expected utility for arbitrary state spaces
Wakker, P.P.
1985-01-01
Subjective expected utility maximization with continuous utility is characterized, extending the result of Wakker (1984, Journal of Mathematical Psychology) to infinite state spaces. In Savage (1954, The Foundations of Statistics) the main restriction, P6, requires structure for the state space, e.g
Pruning state spaces with extended beam search
Dashti, M.T.; Wijs, A.J.
2007-01-01
This paper focuses on using beam search, a heuristic search algorithm, for pruning state spaces while generating. The original beam search is adapted to the state space generation setting and two new search variants are devised. The resulting framework encompasses some known algorithms, such as $A^*
Self-tuning fuzzy logic control of a switched reluctance generator for wind energy applications
DEFF Research Database (Denmark)
Park, Kiwoo; Chen, Zhe
2012-01-01
This paper presents a new self-tuning fuzzy logic control (FLC) based speed controller of a switched reluctance generator (SRG) for wind power applications. Due to its doubly salient structure and magnetic saturation, the SRG possesses an inherent characteristic of strong nonlinearity. In addition...... has better adaptability than a traditional controller so that it provides better performance over a wide range of operating conditions. The current controller is basically a hysteresis controller which controls the phase current in accordance with the turn-on and turn-off angles. Simulation results......, its flux linkage, inductance, and torque are highly coupled with the rotor position and phase current. All these features make the application of traditional controllers to the SRG difficult and unsatisfactory. The proposed controller consists of three main parts: turn-on and turn-off angle...
Development of seam tracking system with ultrasonic sensor using self-tuning fuzzy control
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
One kind of the SAW seam tracking system with contactless ultrasonic sensor is presented in this paper. The new contactless ultrasonic sensor for seam tracking and the working principle of the seam tracking with the sensor are introduced. Based on the experiments, the optimal values of the fuzzy control parameters α and k3 are defined by means of the off-line adjusting method. Because the self-tuning fuzzy control is adopted in the seam tracking system, the overshoot of the system is restrained, the steady-state error is reduced, and the system’s response speed is improved effectively. The results of the SAW seam tracking experiments show that this system’s tracking accuracy is up to ±0.5mm and the system can satisfy the requirements of the engineering application.
Self-Tuning Sliding Mode Controller—Program Control for Process and Mechanical System—
Sakamoto, Noriaki; Komiyama, Daigo; Kubota, Masakazu
Sliding mode control is a well-known technique to guarantee robustness in the presence of uncertainties of modeling, parameter variations, and external disturbances. The sliding mode control law is based on the knowledge of controlled system and the norm (or maximum value, etc.) of uncertainties. However, the modeling work is difficult, and the cost of it is expensive. So, this paper proposes the self-tuning sliding mode controller that calculates the control input (manipulated variable) only by using the desired value and the state variable without requiring the system parameter, the input parameter and the size of the disturbance. Various experiments, which are the temperature control of aluminum and wood-ceramics, the level control of the water tank, and the position control of the shape memory alloy in the program control (time-scheduled control), show the validity and utility of the proposed controller.
Speed Control System on Marine Diesel Engine Based on a Self-Tuning Fuzzy PID Controller
Directory of Open Access Journals (Sweden)
Naeim Farouk
2012-03-01
Full Text Available The degree of speed control of ship machinery effects on the economics and optimization of the machinery configuration and operation. All marine vessel ranging need some sort of speed control system to control and govern the speed of the marine diesel engines. This study presents a self-tuning fuzzy PID control system for speed control system of marine diesel engine. The system under consideration is a fourth-order plant with highly dynamic and uncertain environments. The current speed controllers for marine/traction diesel engines based on PID Controller cannot fully handle the uncertainties associated with such dynamic environments. A fuzzy logic control algorithm is used to estimate the PID coefficients in order to handle such uncertainties to produce a better control performance. Simulation tests were established using Simulink of MATLAB. The obtained results have demonstrated the feasibility and effectiveness of the proposed approach. Simulation results are represented in this study.
Reduced rule base self-tuning fuzzy PI controller for TCSC
Energy Technology Data Exchange (ETDEWEB)
Hameed, Salman; Das, Biswarup; Pant, Vinay [Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Roorkee - 247 667, Uttarakhand (India)
2010-11-15
In this paper, a reduced rule base self-tuning fuzzy PI controller (STFPIC) for thyristor controlled series capacitor (TCSC) is proposed. Essentially, a STFPIC consists of two fuzzy logic controllers (FLC). In this work, for each FLC, 49 rules have been used and as a result, the overall complexity of the STFPIC increases substantially. To reduce this complexity, application of singular value decomposition (SVD) based rule reduction technique is also proposed in this paper. By applying this methodology, the number of rules in each FLC has been reduced from 49 to 9. Therefore, the proposed rule base reduction technique reduces the total number of rules in the STFPIC by almost 80% (from 49 x 2 = 98 to 9 x 2 = 18), thereby reducing the complexity of the STFPIC significantly. The feasibility of the proposed algorithm has been tested on 2-area 4-machine power system and 10-machine 39-bus system through detailed digital simulation using MATLAB/SIMULINK. (author)
Adaptive synchronized switch damping on an inductor: a self-tuning switching law
Kelley, Christopher R.; Kauffman, Jeffrey L.
2017-03-01
Synchronized switch damping (SSD) techniques exploit low-power switching between passive circuits connected to piezoelectric material to reduce structural vibration. In the classical implementation of SSD, the piezoelectric material remains in an open circuit for the majority of the vibration cycle and switches briefly to a shunt circuit at every displacement extremum. Recent research indicates that this switch timing is only optimal for excitation exactly at resonance and points to more general optimal switch criteria based on the phase of the displacement and the system parameters. This work proposes a self-tuning approach that implements the more general optimal switch timing for synchronized switch damping on an inductor (SSDI) without needing any knowledge of the system parameters. The law involves a gradient-based search optimization that is robust to noise and uncertainties in the system. Testing of a physical implementation confirms this law successfully adapts to the frequency and parameters of the system. Overall, the adaptive SSDI controller provides better off-resonance steady-state vibration reduction than classical SSDI while matching performance at resonance.
Self-tuning GMV control of glucose concentration in fed-batch baker's yeast production.
Hitit, Zeynep Yilmazer; Boyacioglu, Havva; Ozyurt, Baran; Ertunc, Suna; Hapoglu, Hale; Akay, Bulent
2014-04-01
A detailed system identification procedure and self-tuning generalized minimum variance (STGMV) control of glucose concentration during the aerobic fed-batch yeast growth were realized. In order to determine the best values of the forgetting factor (λ), initial value of the covariance matrix (α), and order of the Auto-Regressive Moving Average with eXogenous (ARMAX) model (n a, n b), transient response data obtained from the real process wereutilized. Glucose flow rate was adjusted according to the STGMV control algorithm coded in Visual Basic in an online computer connected to the system. Conventional PID algorithm was also implemented for the control of the glucose concentration in aerobic fed-batch yeast cultivation. Controller performances were examined by evaluating the integrals of squared errors (ISEs) at constant and random set point profiles. Also, batch cultivation was performed, and microorganism concentration at the end of the batch run was compared with the fed-batch cultivation case. From the system identification step, the best parameter estimation was accomplished with the values λ = 0.9, α = 1,000 and n a = 3, n b = 2. Theoretical control studies show that the STGMV control system was successful at both constant and random glucose concentration set profiles. In addition, random effects given to the set point, STGMV control algorithm were performed successfully in experimental study.
Research of Self-Tuning PID for PMSM Vector Control based on Improved KMTOA
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Lingzhi Yi
2017-03-01
Full Text Available The Permanent Magnet Synchronous Motor has been applying widely due to it’s high efficiency, high reliability, relatively low cost and low moment of inertia. However, the PMSM drives are easily affected by the uncertain factors such as the variation of motor parameters and load disturbance. In order to realize the control of the PMSM accurately, a novel adaptive chaotic kinetic molecular theory optimization algorithm was implemented for seeking the best parameters of PID controller. In the PMSM vector control system, the speed loop will be adjusted by a CKMTOA PID controller. In modified kinetic molecular theory optimization algorithm, the adaptive inertia weight factors are used to accelerate the convergence speed, and chaotic searching is conducted within the neighbor set of the solutions to avoid the local minima. The model of PMSM and its` space vector control system are set up in the software of MATLAB/Simulink. The effectiveness of the self-tuning CKMTOA PID controller is verified by comparing with the conventional PID and particle swarm optimization algorithm. The extensive simulations and analysis also show the effectiveness of the proposed approach
Experiments on Adaptive Self-Tuning of Seismic Signal Detector Parameters
Knox, H. A.; Draelos, T.; Young, C. J.; Chael, E. P.; Peterson, M. G.; Lawry, B.; Phillips-Alonge, K. E.; Balch, R. S.; Ziegler, A.
2016-12-01
Scientific applications, including underground nuclear test monitoring and microseismic monitoring can benefit enormously from data-driven dynamic algorithms for tuning seismic and infrasound signal detection parameters since continuous streams are producing waveform archives on the order of 1TB per month. Tuning is a challenge because there are a large number of data processing parameters that interact in complex ways, and because the underlying populating of true signal detections is generally unknown. The largely manual process of identifying effective parameters, often performed only over a subset of stations over a short time period, is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. We present improvements to an Adaptive Self-Tuning algorithm for continuously adjusting detection parameters based on consistency with neighboring sensors. Results are shown for 1) data from a very dense network ( 120 stations, 10 km radius) deployed during 2008 on Erebus Volcano, Antarctica, and 2) data from a continuous downhole seismic array in the Farnsworth Field, an oil field in Northern Texas that hosts an ongoing carbon capture, utilization, and storage project. Performance is assessed in terms of missed detections and false detections relative to human analyst detections, simulated waveforms where ground-truth detections exist and visual inspection.
The STAMP Software for State Space Models
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Roy Mendelssohn
2011-05-01
Full Text Available This paper reviews the use of STAMP (Structural Time Series Analyser, Modeler and Predictor for modeling time series data using state-space methods with unobserved components. STAMP is a commercial, GUI-based program that runs on Windows, Linux and Macintosh computers as part of the larger OxMetrics System. STAMP can estimate a wide-variety of both univariate and multivariate state-space models, provides a wide array of diagnostics, and has a batch mode capability. The use of STAMP is illustrated for the Nile river data which is analyzed throughout this issue, as well as by modeling a variety of oceanographic and climate related data sets. The analyses of the oceanographic and climate data illustrate the breadth of models available in STAMP, and that state-space methods produce results that provide new insights into important scientific problems.
Self-tuning control with a filter and a neural compensator for a class of nonlinear systems.
Fu, Yue; Chai, Tianyou
2013-05-01
Considering the mismatching of model-process order, in this brief, a self-tuning proportional-integral-derivative (PID)-like controller is proposed by combining a pole assignment self-tuning PID controller with a filter and a neural compensator. To design the PID controller, a reduced order model is introduced, whose linear parameters are identified by a normalized projection algorithm with a deadzone. The higher order nonlinearity is estimated by a high order neural network. The gains of the PID controller are obtained by pole assignment, which together with other parameters are tuned on-line. The bounded-input bounded-output stability condition and convergence condition of the closed-loop system are presented. Simulations are conducted on the continuous stirred tank reactors system. The results show the effectiveness of the proposed method.
Robust design of a 2-DOF GMV controller: a direct self-tuning and fuzzy scheduling approach.
Silveira, Antonio S; Rodríguez, Jaime E N; Coelho, Antonio A R
2012-01-01
This paper presents a study on self-tuning control strategies with generalized minimum variance control in a fixed two degree of freedom structure-or simply GMV2DOF-within two adaptive perspectives. One, from the process model point of view, using a recursive least squares estimator algorithm for direct self-tuning design, and another, using a Mamdani fuzzy GMV2DOF parameters scheduling technique based on analytical and physical interpretations from robustness analysis of the system. Both strategies are assessed by simulation and real plants experimentation environments composed of a damped pendulum and an under development wind tunnel from the Department of Automation and Systems of the Federal University of Santa Catarina.
Speed control of SR motor by self-tuning fuzzy PI controller with artiﬁcial neural network
Indian Academy of Sciences (India)
Ercument Karakas; Soner Vardarbasi
2007-10-01
In this work, the dynamic model, ﬂux-current-rotor position and torque-current-rotor position values of the switched reluctance motor (SRM) are obtained in MATLAB/Simulink. Motor control speed is achieved by self-tuning fuzzy PI (Proportional Integral) controller with artiﬁcial neural network tuning (NSTFPI). Performance of NSTFPI controller is compared with performance of fuzzy logic (FL) and fuzzy logic PI (FLPI) controllers in respect of rise time, settling time, overshoot and steady state error
Zorić, Nemanja D.; Simonović, Aleksandar M.; Mitrović, Zoran S.; Stupar, Slobodan N.; Obradović, Aleksandar M.; Lukić, Nebojša S.
2014-10-01
This paper deals with active free vibrations control of smart composite beams using particle-swarm optimized self-tuning fuzzy logic controller. In order to improve the performance and robustness of the fuzzy logic controller, this paper proposes integration of self-tuning method, where scaling factors of the input variables in the fuzzy logic controller are adjusted via peak observer, with optimization of membership functions using the particle swarm optimization algorithm. The Mamdani and zero-order Takagi-Sugeno-Kang fuzzy inference methods are employed. In order to overcome stability problem, at the same time keeping advantages of the proposed self-tuning fuzzy logic controller, this controller is combined with the LQR making composite controller. Several numerical studies are provided for the cantilever composite beam for both single mode and multimodal cases. In the multimodal case, a large-scale system is decomposed into smaller subsystems in a parallel structure. In order to represent the efficiency of the proposed controller, obtained results are compared with the corresponding results in the cases of the optimized fuzzy logic controllers with constant scaling factors and linear quadratic regulator.
Directory of Open Access Journals (Sweden)
Gilberto Herrera-Ruíz
2013-03-01
Full Text Available A New Adaptive Self-Tuning Fourier Coefficients Algorithm for Periodic Torque Ripple Minimization in Permanent Magnet Synchronous Motors (PMSM Torque ripple occurs in Permanent Magnet Synchronous Motors (PMSMs due to the non-sinusoidal flux density distribution around the air-gap and variable magnetic reluctance of the air-gap due to the stator slots distribution. These torque ripples change periodically with rotor position and are apparent as speed variations, which degrade the PMSM drive performance, particularly at low speeds, because of low inertial filtering. In this paper, a new self-tuning algorithm is developed for determining the Fourier Series Controller coefficients with the aim of reducing the torque ripple in a PMSM, thus allowing for a smoother operation. This algorithm adjusts the controller parameters based on the component’s harmonic distortion in time domain of the compensation signal. Experimental evaluation is performed on a DSP-controlled PMSM evaluation platform. Test results obtained validate the effectiveness of the proposed self-tuning algorithm, with the Fourier series expansion scheme, in reducing the torque ripple.
Directory of Open Access Journals (Sweden)
Varghese Mathew Vaidyan
2015-09-01
Full Text Available Present self-tuning regulator architectures based on recursive least-square estimation are computationally expensive and require large amount of resources and time in generating the first control signal due to computational bottlenecks imposed by the calculations involved in estimation stage, different stages of matrix multiplications and the number of intermediate variables at each iteration and precludes its use in applications that have fast required response times and those which run on embedded computing platforms with low-power or low-cost requirements with constraints on resource usage. A salient feature of this study is that a new modular parallel pipelined stochastic approximation-based self-tuning regulator architecture which reduces the time required to generate the first control signal, reduces resource usage and reduces the number of intermediate variables is proposed. Fast matrix multiplication, pipelining and high-speed arithmetic function implementations were used for improving the performance. Results of implementation demonstrate that the proposed architecture has an improvement in control signal generation time by 38% and reduction in resource usage by 41% in terms of multipliers and 44.4% in terms of adders compared with the best existing related work, opening up new possibilities for the application of online embedded self-tuning regulators.
Gómez-Espinosa, Alfonso; Hernández-Guzmán, Víctor M; Bandala-Sánchez, Manuel; Jiménez-Hernández, Hugo; Rivas-Araiza, Edgar A; Rodríguez-Reséndiz, Juvenal; Herrera-Ruíz, Gilberto
2013-03-19
A New Adaptive Self-Tuning Fourier Coefficients Algorithm for Periodic Torque Ripple Minimization in Permanent Magnet Synchronous Motors (PMSM) Torque ripple occurs in Permanent Magnet Synchronous Motors (PMSMs) due to the non-sinusoidal flux density distribution around the air-gap and variable magnetic reluctance of the air-gap due to the stator slots distribution. These torque ripples change periodically with rotor position and are apparent as speed variations, which degrade the PMSM drive performance, particularly at low speeds, because of low inertial filtering. In this paper, a new self-tuning algorithm is developed for determining the Fourier Series Controller coefficients with the aim of reducing the torque ripple in a PMSM, thus allowing for a smoother operation. This algorithm adjusts the controller parameters based on the component's harmonic distortion in time domain of the compensation signal. Experimental evaluation is performed on a DSP-controlled PMSM evaluation platform. Test results obtained validate the effectiveness of the proposed self-tuning algorithm, with the Fourier series expansion scheme, in reducing the torque ripple.
Graph Subsumption in Abstract State Space Exploration
Zambon, Eduardo; Rensink, Arend; Wijs, A.; Bosnacki, D.; Edelkamp, S.
In this paper we present the extension of an existing method for abstract graph-based state space exploration, called neighbourhood abstraction, with a reduction technique based on subsumption. Basically, one abstract state subsumes another when it covers more concrete states; in such a case, the
Ding, Ruqi; Xu, Bing; Zhang, Junhui; Cheng, Min
2017-08-01
Independent metering control systems are promising fluid power technologies compared with traditional valve controlled systems. By breaking the mechanical coupling between the inlet and outlet, the meter-out valve can open as large as possible to reduce energy consumptions. However, the lack of damping in outlet causes stronger vibrations. To address the problem, the paper designs a hybrid control method combining dynamic pressure-feedback and active damping control. The innovation resides in the optimization of damping by introducing pressure feedback to make trade-offs between high stability and fast response. To achieve this goal, the dynamic response pertaining to the control parameters consisting of feedback gain and cut-off frequency, are analyzed via pole-zero locations. Accordingly, these parameters are tuned online in terms of guaranteed dominant pole placement such that the optimal damping can be accurately captured under a considerable variation of operating conditions. The experiment is deployed in a mini-excavator. The results pertaining to different control parameters confirm the theoretical expectations via pole-zero locations. By using proposed self-tuning controller, the vibrations are almost eliminated after only one overshoot for different operation conditions. The overshoots are also reduced with less decrease of the response time. In addition, the energy-saving capability of independent metering system is still not affected by the improvement of controllability.
Directory of Open Access Journals (Sweden)
M, Santhakumar
2010-01-01
Full Text Available Problem statement: Conventional Proportional-Integral-Derivative (PID controllers exhibit moderately good performance once the PID gains are properly tuned. However, when the dynamic characteristics of the system are time dependent or the operating conditions of the system vary, it is necessary to retune the gains to obtain desired performance. This situation has renewed the interest of researchers and practitioners in PID control. Self-tuning of PID controllers has emerged as a new and active area of research with the advent and easy availability of algorithms and computers. This study discusses self-tuning (auto-tuning algorithm for control of autonomous underwater vehicles. Approach: Self-tuning mechanism will avoid time consuming manual tuning of controllers and promises better results by providing optimal PID controller settings as the system dynamics or operating points change. Most of the self-tuning methods available in the literature were based on frequency response characteristics and search methods. In this study, we proposed a method based on Taguchis robust design method for self-tuning of an autonomous underwater vehicle controller. The algorithm, based on this method, tuned the controller gains optimally and robustly in real time with less computation effort by using desired and actual state variables. It can be used for the Single-Input Single-Output (SISO systems as well as Multi-Input Multi-Output (MIMO systems without mathematical models of plants. Results: A simulation study of the AUV control on the horizontal plane (yaw plane control was used to demonstrate and validate the performance and effectiveness of the proposed scheme. Simulation results of the proposed self-tuning scheme are compared with the conventional PID controllers which are tuned by Ziegler-Nichols (ZN and Taguchis tuning methods. These results showed that the Integral Square Error (ISE is significantly reduced from the conventional
Spin Testing for Durability Began on a Self-Tuning Impact Damper for Turbomachinery Blades
Duffy, Kirsten; Mehmed, Oral
2003-01-01
NASA and Pratt & Whitney will collaborate under a Space Act Agreement to perform spin testing of the impact damper to verify damping effectiveness and durability. Pratt & Whitney will provide the turbine blade and damper hardware for the tests. NASA will provide the facility and perform the tests. Effectiveness and durability will be investigated during and after sustained sweeps of rotor speed through resonance. Tests of a platform wedge damper are also planned to compare its effectiveness with that of the impact damper. Results from baseline tests without dampers will be used to measure damping effectiveness. The self-tuning impact damper combines two damping methods-the tuned mass damper and the impact damper. It consists of a ball located within a cavity in the blade. This ball rolls back and forth on a spherical trough under centrifugal load (tuned mass damper) and can strike the walls of the cavity (impact damper). The ball s rolling natural frequency is proportional to the rotor speed and can be designed to follow an engine-order line (integer multiple of rotor speed). Aerodynamic forcing frequencies typically follow these engineorder lines, and a damper tuned to the engine order will most effectively reduce blade vibrations when the resonant frequency equals the engine-order forcing frequency. This damper has been tested in flat plates and turbine blades in the Dynamic Spin Facility. During testing, a pair of plates or blades rotates in vacuum. Excitation is provided by one of three methods--eddy-current engine-order excitation (ECE), electromechanical shakers, and magnetic bearing excitation. The eddy-current system consists of magnets located circumferentially around the rotor. As a blade passes a magnet, a force is imparted on the blade. The number of magnets used can be varied to change the desired engine order of the excitation. The magnets are remotely raised or lowered to change the magnitude of the force on the blades. The other two methods apply
Approximate Methods for State-Space Models
Koyama, Shinsuke; Shalizi, Cosma Rohilla; Kass, Robert E; 10.1198/jasa.2009.tm08326
2010-01-01
State-space models provide an important body of techniques for analyzing time-series, but their use requires estimating unobserved states. The optimal estimate of the state is its conditional expectation given the observation histories, and computing this expectation is hard when there are nonlinearities. Existing filtering methods, including sequential Monte Carlo, tend to be either inaccurate or slow. In this paper, we study a nonlinear filter for nonlinear/non-Gaussian state-space models, which uses Laplace's method, an asymptotic series expansion, to approximate the state's conditional mean and variance, together with a Gaussian conditional distribution. This {\\em Laplace-Gaussian filter} (LGF) gives fast, recursive, deterministic state estimates, with an error which is set by the stochastic characteristics of the model and is, we show, stable over time. We illustrate the estimation ability of the LGF by applying it to the problem of neural decoding and compare it to sequential Monte Carlo both in simulat...
The State Space Models Toolbox for MATLAB
Directory of Open Access Journals (Sweden)
Jyh-Ying Peng
2011-05-01
Full Text Available State Space Models (SSM is a MATLAB toolbox for time series analysis by state space methods. The software features fully interactive construction and combination of models, with support for univariate and multivariate models, complex time-varying (dy- namic models, non-Gaussian models, and various standard models such as ARIMA and structural time-series models. The software includes standard functions for Kalman fil- tering and smoothing, simulation smoothing, likelihood evaluation, parameter estimation, signal extraction and forecasting, with incorporation of exact initialization for filters and smoothers, and support for missing observations and multiple time series input with com- mon analysis structure. The software also includes implementations of TRAMO model selection and Hillmer-Tiao decomposition for ARIMA models. The software will provide a general toolbox for time series analysis on the MATLAB platform, allowing users to take advantage of its readily available graph plotting and general matrix computation capabilities.
Projective loop quantum gravity. I. State space
Lanéry, Suzanne; Thiemann, Thomas
2016-12-01
Instead of formulating the state space of a quantum field theory over one big Hilbert space, it has been proposed by Kijowski to describe quantum states as projective families of density matrices over a collection of smaller, simpler Hilbert spaces. Beside the physical motivations for this approach, it could help designing a quantum state space holding the states we need. In a latter work by Okolów, the description of a theory of Abelian connections within this framework was developed, an important insight being to use building blocks labeled by combinations of edges and surfaces. The present work generalizes this construction to an arbitrary gauge group G (in particular, G is neither assumed to be Abelian nor compact). This involves refining the definition of the label set, as well as deriving explicit formulas to relate the Hilbert spaces attached to different labels. If the gauge group happens to be compact, we also have at our disposal the well-established Ashtekar-Lewandowski Hilbert space, which is defined as an inductive limit using building blocks labeled by edges only. We then show that the quantum state space presented here can be thought as a natural extension of the space of density matrices over this Hilbert space. In addition, it is manifest from the classical counterparts of both formalisms that the projective approach allows for a more balanced treatment of the holonomy and flux variables, so it might pave the way for the development of more satisfactory coherent states.
State-space Correlations and Stabilities
Bellucci, Stefano
2010-01-01
The state-space pair correlation functions and notion of stability of extremal and non-extremal black holes in string theory and M-theory are considered from the viewpoints of thermodynamic Ruppeiner geometry. From the perspective of intrinsic Riemannian geometry, the stability properties of these black branes are divulged from the positivity of principle minors of the space-state metric tensor. We have explicitly analyzed the state-space configurations for (i) the two and three charge extremal black holes, (ii) the four and six charge non-extremal black branes, which both arise from the string theory solutions. An extension is considered for the $D_6$-$D_4$-$D_2$-$D_0$ multi-centered black branes, fractional small black branes and two charge rotating fuzzy rings in the setup of Mathur's fuzzball configurations. The state-space pair correlations and nature of stabilities have been investigated for three charged bubbling black brane foams, and thereby the M-theory solutions are brought into the present conside...
DEFF Research Database (Denmark)
Knudsen, Jesper Viese; Bendtsen, Jan Dimon; Andersen, Palle;
2016-01-01
In this paper, a self-tuning linear quadratic supervisory regulator using a large-signal state estimator for a diesel driven generator set is proposed. The regulator improves operational efficiency, in comparison to current implementations, by (i) automating the initial tuning process and (ii......) enabling automated retuning capabilities. Utilizing a first principles-based nonlinear model detailed in [1], the procedure is demonstrated through simulations after real system measurements have been used for parameter identification. The regulator is able to suppress load-induced variations successfully...... throughout the operating range of the diesel generator....
Institute of Scientific and Technical Information of China (English)
Lei Wang; Wencai Du; Hai Wang; Hong Wu
2008-01-01
A two-staged membrane separation process for hydrogen recovery from refinery gases is introduced. The principle of the gas membrane separation process and the influence of the operation temperatures are analyzed. As the conventional PID controller is difficult to make the operation temperatures steady, a fuzzy self-tuning PID control algorithm is proposed. The application shows that the algorithm is effective, the operation temperatures of both stages can be controlled steadily, and the operation flexibility and adaptability of the hydrogen recovery unit are enhanced with safety. This study lays a foundation to optimize the control of the membrane separation process and thus ensure the membrane performance.
Adjoint method for hybrid guidance loop state-space models
Weiss, M.; Bucco, D.
2015-01-01
A framework is introduced to develop the theory of the adjoint method for models including both continuous and discrete dynamics. The basis of this framework consists of the class of impulsive linear dynamic systems. It allows extension of the adjoint method to more general models that include multi
Multimedia Mapping using Continuous State Space Models
DEFF Research Database (Denmark)
Lehn-Schiøler, Tue
2004-01-01
In this paper a system that transforms speech waveforms to animated faces are proposed. The system relies on continuous state space models to perform the mapping, this makes it possible to ensure video with no sudden jumps and allows continuous control of the parameters in 'face space'. Simulations...... are performed on recordings of 3-5 sec. video sequences with sentences from the Timit database. The model is able to construct an image sequence from an unknown noisy speech sequence fairly well even though the number of training examples are limited....
Fractional State Space Analysis of Economic Systems
Directory of Open Access Journals (Sweden)
J. A. Tenreiro Machado
2015-07-01
Full Text Available This paper examines modern economic growth according to the multidimensional scaling (MDS method and state space portrait (SSP analysis. Electing GDP per capita as the main indicator for economic growth and prosperity, the long-run perspective from 1870 to 2010 identifies the main similarities among 34 world partners’ modern economic growth and exemplifies the historical waving mechanics of the largest world economy, the USA. MDS reveals two main clusters among the European countries and their old offshore territories, and SSP identifies the Great Depression as a mild challenge to the American global performance, when compared to the Second World War and the 2008 crisis.
Adaptive control with self-tuning for non-invasive beat-by-beat blood pressure measurement.
Nogawa, Masamichi; Ogawa, Mitsuhiro; Yamakoshi, Takehiro; Tanaka, Shinobu; Yamakoshi, Ken-ichi
2011-01-01
Up to now, we have successfully carried out the non-invasive beat-by-beat measurement of blood pressure (BP) in the root of finger, superficial temporal and radial artery based on the volume-compensation technique with reasonable accuracy. The present study concerns with improvement of control method for this beat-by-beat BP measurement. The measurement system mainly consists of a partial pressurization cuff with a pair of LED and photo-diode for the detection of arterial blood volume, and a digital self-tuning control method. Using healthy subjects, the performance and accuracy of this system were evaluated through comparison experiments with the system using a conventional empirically tuned PID controller. The significant differences of BP measured in finger artery were not showed in systolic (SBP), p=0.52, and diastolic BP (DBP), p=0.35. With the advantage of the adaptive control with self-tuning method, which can tune the control parameters without disturbing the control system, the application area of the non-invasive beat-by-beat measurement method will be broadened.
Yang, Lei; Yang, Ming; Xu, Zihao; Zhuang, Xiaoqi; Wang, Wei; Zhang, Haibo; Han, Lu; Xu, Liang
2014-10-01
The purpose of this paper is to report the research and design of control system of magnetic coupling centrifugal blood pump in our laboratory, and to briefly describe the structure of the magnetic coupling centrifugal blood pump and principles of the body circulation model. The performance of blood pump is not only related to materials and structure, but also depends on the control algorithm. We studied the algorithm about motor current double-loop control for brushless DC motor. In order to make the algorithm adjust parameter change in different situations, we used the self-tuning fuzzy PI control algorithm and gave the details about how to design fuzzy rules. We mainly used Matlab Simulink to simulate the motor control system to test the performance of algorithm, and briefly introduced how to implement these algorithms in hardware system. Finally, by building the platform and conducting experiments, we proved that self-tuning fuzzy PI control algorithm could greatly improve both dynamic and static performance of blood pump and make the motor speed and the blood pump flow stable and adjustable.
Approximate Methods for State-Space Models.
Koyama, Shinsuke; Pérez-Bolde, Lucia Castellanos; Shalizi, Cosma Rohilla; Kass, Robert E
2010-03-01
State-space models provide an important body of techniques for analyzing time-series, but their use requires estimating unobserved states. The optimal estimate of the state is its conditional expectation given the observation histories, and computing this expectation is hard when there are nonlinearities. Existing filtering methods, including sequential Monte Carlo, tend to be either inaccurate or slow. In this paper, we study a nonlinear filter for nonlinear/non-Gaussian state-space models, which uses Laplace's method, an asymptotic series expansion, to approximate the state's conditional mean and variance, together with a Gaussian conditional distribution. This Laplace-Gaussian filter (LGF) gives fast, recursive, deterministic state estimates, with an error which is set by the stochastic characteristics of the model and is, we show, stable over time. We illustrate the estimation ability of the LGF by applying it to the problem of neural decoding and compare it to sequential Monte Carlo both in simulations and with real data. We find that the LGF can deliver superior results in a small fraction of the computing time.
Projective Loop Quantum Gravity I. State Space
Lanéry, Suzanne
2014-01-01
Instead of formulating the state space of a quantum field theory over one big Hilbert space, it has been proposed by Kijowski to describe quantum states as projective families of density matrices over a collection of smaller, simpler Hilbert spaces. Beside the physical motivations for this approach, it could help designing a quantum state space holding the states we need. In [Oko{\\l}\\'ow 2013, arXiv:1304.6330] the description of a theory of Abelian connections within this framework was developed, an important insight being to use building blocks labeled by combinations of edges and surfaces. The present work generalizes this construction to an arbitrary gauge group G (in particular, G is neither assumed to be Abelian nor compact). This involves refining the definition of the label set, as well as deriving explicit formulas to relate the Hilbert spaces attached to different labels. If the gauge group happens to be compact, we also have at our disposal the well-established Ashtekar-Lewandowski Hilbert space, wh...
Condensed State Spaces for Symmetrical Coloured Petri Nets
DEFF Research Database (Denmark)
Jensen, Kurt
1996-01-01
This paper deals with state spaces. A state space is a directed graph with a node for each reachable state and an arc for each possible state change. We describe how symmetries of the modelled system can be exploited to obtain much more succinct state space analysis. The symmetries induce equival...
Multivariable Wind Modeling in State Space
DEFF Research Database (Denmark)
Sichani, Mahdi Teimouri; Pedersen, B. J.
2011-01-01
Turbulence of the incoming wind field is of paramount importance to the dynamic response of wind turbines. Hence reliable stochastic models of the turbulence should be available from which time series can be generated for dynamic response and structural safety analysis. In the paper an empirical...... cross-spectral density function for the along-wind turbulence component over the rotor plane is taken as the starting point. The spectrum is spatially discretized in terms of a Hermitian cross-spectral density matrix for the turbulence state vector which turns out not to be positive definite. Since...... the succeeding state space and ARMA modeling of the turbulence rely on the positive definiteness of the cross-spectral density matrix, the problem with the non-positive definiteness of such matrices is at first addressed and suitable treatments regarding it are proposed. From the adjusted positive definite cross...
Li, Zhenyu; Wang, Bin; Liu, Hong
2016-01-01
Satellite capturing with free-floating space robots is still a challenging task due to the non-fixed base and unknown mass property issues. In this paper gyro and eye-in-hand camera data are adopted as an alternative choice for solving this problem. For this improved system, a new modeling approach that reduces the complexity of system control and identification is proposed. With the newly developed model, the space robot is equivalent to a ground-fixed manipulator system. Accordingly, a self-tuning control scheme is applied to handle such a control problem including unknown parameters. To determine the controller parameters, an estimator is designed based on the least-squares technique for identifying the unknown mass properties in real time. The proposed method is tested with a credible 3-dimensional ground verification experimental system, and the experimental results confirm the effectiveness of the proposed control scheme. PMID:27589748
Institute of Scientific and Technical Information of China (English)
胡志坤; 桂卫华; 彭小奇
2004-01-01
An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In this model, the mathematical model of support vector regression was converted into the same format as support vector machine for classification. Then a simplified sequential minimal optimization for classification was applied to train the regression coefficient vector α- α* and threshold b. Sequentially penalty parameter C was tuned dynamically through forecasting result during the training process. Finally, an on-line forecasting algorithm for zinc output was proposed. The simulation result shows that in spite of a relatively small industrial data set, the effective error is less than 10% with a remarkable performance of real time. The model was applied to the optimization operation and fault diagnosis system for imperial smelting furnace.
Directory of Open Access Journals (Sweden)
Zhenyu Li
2016-08-01
Full Text Available Satellite capturing with free-floating space robots is still a challenging task due to the non-fixed base and unknown mass property issues. In this paper gyro and eye-in-hand camera data are adopted as an alternative choice for solving this problem. For this improved system, a new modeling approach that reduces the complexity of system control and identification is proposed. With the newly developed model, the space robot is equivalent to a ground-fixed manipulator system. Accordingly, a self-tuning control scheme is applied to handle such a control problem including unknown parameters. To determine the controller parameters, an estimator is designed based on the least-squares technique for identifying the unknown mass properties in real time. The proposed method is tested with a credible 3-dimensional ground verification experimental system, and the experimental results confirm the effectiveness of the proposed control scheme.
Li, Zhenyu; Wang, Bin; Liu, Hong
2016-08-30
Satellite capturing with free-floating space robots is still a challenging task due to the non-fixed base and unknown mass property issues. In this paper gyro and eye-in-hand camera data are adopted as an alternative choice for solving this problem. For this improved system, a new modeling approach that reduces the complexity of system control and identification is proposed. With the newly developed model, the space robot is equivalent to a ground-fixed manipulator system. Accordingly, a self-tuning control scheme is applied to handle such a control problem including unknown parameters. To determine the controller parameters, an estimator is designed based on the least-squares technique for identifying the unknown mass properties in real time. The proposed method is tested with a credible 3-dimensional ground verification experimental system, and the experimental results confirm the effectiveness of the proposed control scheme.
Self-Tuning Insulin Adjustment Algorithm for Type 1 Diabetic Patients based on Multi-Doses Regime
Directory of Open Access Journals (Sweden)
D. U. Campos-Delgado
2005-01-01
Full Text Available A self-tuning algorithm is presented for on-line insulin dosage adjustment in type 1 diabetic patients (chronic stage. The algorithm suggested does not need information of the patient insulin–glucose dynamics (model-free. Three doses are programmed daily, where a combination of two types of insulin: rapid/short and intermediate/long acting is injected into the patient through a subcutaneous route. The doses adaptation is performed by reducing the error in the blood glucose level from euglycemics. In this way, a total of five doses are tuned per day: three rapid/short and two intermediate/long, where there is large penalty to avoid hypoglycemic scenarios. Closed-loop simulation results are illustrated using a detailed nonlinear model of the subcutaneous insulin–glucose dynamics in a type 1 diabetic patient with meal intake.
Granger causality for state-space models.
Barnett, Lionel; Seth, Anil K
2015-04-01
Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations-commonplace in application domains as diverse as climate science, econometrics, and the neurosciences-induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.
Topological properties of flat electroencephalography's state space
Ken, Tan Lit; Ahmad, Tahir bin; Mohd, Mohd Sham bin; Ngien, Su Kong; Suwa, Tohru; Meng, Ong Sie
2016-02-01
Neuroinverse problem are often associated with complex neuronal activity. It involves locating problematic cell which is highly challenging. While epileptic foci localization is possible with the aid of EEG signals, it relies greatly on the ability to extract hidden information or pattern within EEG signals. Flat EEG being an enhancement of EEG is a way of viewing electroencephalograph on the real plane. In the perspective of dynamical systems, Flat EEG is equivalent to epileptic seizure hence, making it a great platform to study epileptic seizure. Throughout the years, various mathematical tools have been applied on Flat EEG to extract hidden information that is hardly noticeable by traditional visual inspection. While these tools have given worthy results, the journey towards understanding seizure process completely is yet to be succeeded. Since the underlying structure of Flat EEG is dynamic and is deemed to contain wealthy information regarding brainstorm, it would certainly be appealing to explore in depth its structures. To better understand the complex seizure process, this paper studies the event of epileptic seizure via Flat EEG in a more general framework by means of topology, particularly, on the state space where the event of Flat EEG lies.
PCR仪模糊自整定PID温度控制算法的研究%Fuzzy Self-tuning PID Temperature Control about PCR Instrument
Institute of Scientific and Technical Information of China (English)
毕雪芹; 于媛美
2013-01-01
论文针对PCR基因扩增仪对温度的要求提出了与其适应的算法模糊自整定PID算法.首先给出了PCR反应的各个阶段对温度的响应速度及精度的要求.然后通过对系统建模仿真,分别得到变换隶属函数前后模糊PID与模糊自整定PID的两条曲线.最后给出了模糊PID与模糊自整定PID的对比关系.发现同模糊PID相比模糊自整定PID的响应速度快,超调量小,使系统更稳定.%Based on the temperature requirements of PCR gene amplification instrument and its adaptive algorithm, putting forward a fuzzy self-tuning PID algorithm. First, the polymerase chain reaction (PCR) of each stage of temperature response speed and precision requirements are given. Then through the system modeling simulation, the two curves are repectively got before and after the transformation membership about function of fuzzy PID and fuzzy self-tuning PID. Finally give the contrast relationship of fuzzy PID and fuzzy self-tuning PID. Found that compared with fuzzy PID, fuzzy self-tuning PID has fast response, small overshoot, and makes the system more stable.
Mao, Jun; Hou, Jian; Shen, Dong
2013-03-01
This article describes the control system of PID parameter self-tuning fuzzy controller. For cutting the coal of different hardness, adopt fuzzy techniques, automatically adjust the feed speed of operating mechanism, and maintain the control of operating mechanism of heading machine with constant power.
An introduction to state space time series analysis.
Commandeur, J.J.F. & Koopman, S.J.
2007-01-01
Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is...
ASAP: An Extensible Platform for State Space Analysis
DEFF Research Database (Denmark)
Westergaard, Michael; Evangelista, Sami; Kristensen, Lars Michael
2009-01-01
The ASCoVeCo State space Analysis Platform (ASAP) is a tool for performing explicit state space analysis of coloured Petri nets (CPNs) and other formalisms. ASAP supports a wide range of state space reduction techniques and is intended to be easy to extend and to use, making it a suitable tool...... for students, researchers, and industrial users that would like to analyze protocols and/or experiment with different algorithms. This paper presents ASAP from these two perspectives....
A Sweep-Line Method for State Space Exploration
DEFF Research Database (Denmark)
Christensen, Søren; Kristensen, Lars Michael; Mailund, Thomas
2001-01-01
We present a state space exploration method for on-the-fly verification. The method is aimed at systems for which it is possible to define a measure of progress based on the states of the system. The measure of progress makes it possible to delete certain states on-the-fly during state space...... of the method on a number of Coloured Petri Net models, and give a first evaluation of its practicality by means of an implementation based on the Design/CPN state space tool. Our experiments show significant reductions in both space and time used during state space exploration. The method is not specific...
A Compositional Sweep-Line State Space Exploration Method
DEFF Research Database (Denmark)
Kristensen, Lars Michael; Mailund, Thomas
2002-01-01
State space exploration is a main approach to verification of finite-state systems. The sweep-line method exploits a certain kind of progress present in many systems to reduce peak memory usage during state space exploration. We present a new sweep-line algorithm for a compositional setting where...
Complexity in Simplicity: Flexible Agent-based State Space Exploration
DEFF Research Database (Denmark)
Rasmussen, Jacob Illum; Larsen, Kim Guldstrand
2007-01-01
In this paper, we describe a new flexible framework for state space exploration based on cooperating agents. The idea is to let various agents with different search patterns explore the state space individually and communicate information about fruitful subpaths of the search tree to each other...
An introduction to state space modeling (in Russian)
Alexander Tsyplakov
2011-01-01
Many time series models, primarily various models with unobservable components, can be represented in a so called state space form. A state space model is a powerful tool that allows one to apply to the original model a wide range of standard procedures including estimation and forecasting. This essay provides a survey of this universal class of models and related procedures.
Adaptive importance sampling of random walks on continuous state spaces
Energy Technology Data Exchange (ETDEWEB)
Baggerly, K.; Cox, D.; Picard, R.
1998-11-01
The authors consider adaptive importance sampling for a random walk with scoring in a general state space. Conditions under which exponential convergence occurs to the zero-variance solution are reviewed. These results generalize previous work for finite, discrete state spaces in Kollman (1993) and in Kollman, Baggerly, Cox, and Picard (1996). This paper is intended for nonstatisticians and includes considerable explanatory material.
An introduction to state space time series analysis.
Commandeur, J.J.F. & Koopman, S.J.
2007-01-01
Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor wi
Complexity in Simplicity: Flexible Agent-based State Space Exploration
DEFF Research Database (Denmark)
Rasmussen, Jacob Illum; Larsen, Kim Guldstrand
2007-01-01
In this paper, we describe a new flexible framework for state space exploration based on cooperating agents. The idea is to let various agents with different search patterns explore the state space individually and communicate information about fruitful subpaths of the search tree to each other...
A Sweep-Line Method for State Space Exploration
DEFF Research Database (Denmark)
Christensen, Søren; Kristensen, Lars Michael; Mailund, Thomas
2001-01-01
We present a state space exploration method for on-the-fly verification. The method is aimed at systems for which it is possible to define a measure of progress based on the states of the system. The measure of progress makes it possible to delete certain states on-the-fly during state space...... of the method on a number of Coloured Petri Net models, and give a first evaluation of its practicality by means of an implementation based on the Design/CPN state space tool. Our experiments show significant reductions in both space and time used during state space exploration. The method is not specific...... generation, since these states can never be reached again. This in turn reduces the memory used for state space storage during the task of verification. Examples of progress measures are sequence numbers in communication protocols and time in certain models with time. We illustrate the application...
Optimal Self-Tuning PID Controller Based on Low Power Consumption for a Server Fan Cooling System
Lee, Chengming; Chen, Rongshun
2015-01-01
Recently, saving the cooling power in servers by controlling the fan speed has attracted considerable attention because of the increasing demand for high-density servers. This paper presents an optimal self-tuning proportional-integral-derivative (PID) controller, combining a PID neural network (PIDNN) with fan-power-based optimization in the transient-state temperature response in the time domain, for a server fan cooling system. Because the thermal model of the cooling system is nonlinear and complex, a server mockup system simulating a 1U rack server was constructed and a fan power model was created using a third-order nonlinear curve fit to determine the cooling power consumption by the fan speed control. PIDNN with a time domain criterion is used to tune all online and optimized PID gains. The proposed controller was validated through experiments of step response when the server operated from the low to high power state. The results show that up to 14% of a server’s fan cooling power can be saved if the fan control permits a slight temperature response overshoot in the electronic components, which may provide a time-saving strategy for tuning the PID controller to control the server fan speed during low fan power consumption. PMID:26007725
Optimal Self-Tuning PID Controller Based on Low Power Consumption for a Server Fan Cooling System.
Lee, Chengming; Chen, Rongshun
2015-05-20
Recently, saving the cooling power in servers by controlling the fan speed has attracted considerable attention because of the increasing demand for high-density servers. This paper presents an optimal self-tuning proportional-integral-derivative (PID) controller, combining a PID neural network (PIDNN) with fan-power-based optimization in the transient-state temperature response in the time domain, for a server fan cooling system. Because the thermal model of the cooling system is nonlinear and complex, a server mockup system simulating a 1U rack server was constructed and a fan power model was created using a third-order nonlinear curve fit to determine the cooling power consumption by the fan speed control. PIDNN with a time domain criterion is used to tune all online and optimized PID gains. The proposed controller was validated through experiments of step response when the server operated from the low to high power state. The results show that up to 14% of a server's fan cooling power can be saved if the fan control permits a slight temperature response overshoot in the electronic components, which may provide a time-saving strategy for tuning the PID controller to control the server fan speed during low fan power consumption.
Optimal Self-Tuning PID Controller Based on Low Power Consumption for a Server Fan Cooling System
Directory of Open Access Journals (Sweden)
Chengming Lee
2015-05-01
Full Text Available Recently, saving the cooling power in servers by controlling the fan speed has attracted considerable attention because of the increasing demand for high-density servers. This paper presents an optimal self-tuning proportional-integral-derivative (PID controller, combining a PID neural network (PIDNN with fan-power-based optimization in the transient-state temperature response in the time domain, for a server fan cooling system. Because the thermal model of the cooling system is nonlinear and complex, a server mockup system simulating a 1U rack server was constructed and a fan power model was created using a third-order nonlinear curve fit to determine the cooling power consumption by the fan speed control. PIDNN with a time domain criterion is used to tune all online and optimized PID gains. The proposed controller was validated through experiments of step response when the server operated from the low to high power state. The results show that up to 14% of a server’s fan cooling power can be saved if the fan control permits a slight temperature response overshoot in the electronic components, which may provide a time-saving strategy for tuning the PID controller to control the server fan speed during low fan power consumption.
Luo, Yanting; Yang, Yongmin; Chen, Zhongsheng
2014-04-10
Sub-resonances often happen in wireless power transmission (WPT) systems using coupled magnetic resonances (CMR) due to environmental changes, coil movements or component degradations, which is a serious challenge for high efficiency power transmission. Thus self-tuning is very significant to keep WPT systems following strongly magnetic resonant conditions in practice. Traditional coupled-mode ways is difficult to solve this problem. In this paper a two-port power wave model is presented, where power matching and the overall systemic power transmission efficiency are firstly defined by scattering (S) parameters. Then we propose a novel self-tuning scheme based on on-line S parameters measurements and two-side power matching. Experimental results testify the feasibility of the proposed method. These findings suggest that the proposed method is much potential to develop strongly self-adaptive WPT systems with CMR.
基于自律计算的系统可信性自调节模型%System dependability self-tuning model based on autonomic computing
Institute of Scientific and Technical Information of China (English)
吴庆涛; 郑瑞娟; 张明川; 魏汪洋; 李冠峰
2011-01-01
Ensuring and strengthening the reliability, security and stability of computer systems are advanced requirement to system service.Based on autonomic computing, a self-tuning model on system dependability is proposed.By dynamically analyzing the change trend of object system dependability, and making reference to the grads fluctuation of accient dependability curve, it implements the on-line dependability evaluation, dependability dynamic prediction and self-tuning scheme selection in turn, which accomplishes the self-tuning function of system dependability.Confidence field grads calculation method is adopted to solve the optimization problem of self-tuning.%保障和增强系统的可靠性、安全性和稳定性等可信性质是对系统服务的高级要求.提出了一个基于自律计算理念的系统可信性自调节模型,该模型动态分析目标系统的可信度变化趋势,参考先验可信度变化曲线的梯度升降,依序实现可信度在线评估、可信度动态预测和自调节方案选择,完成系统可信度非降的自主调节过程,采用信赖域梯度计算策略解决自调节的最优化问题.
2014-01-01
In this paper, an alternative control method is proposed to improve the harmonic suppression efficiency of the active power filter in a distorted and an unbalanced power system to compensate for the perturbations caused by the unbalanced non-linear loads. The proposed method uses a self-tuning filter (STF) to process the grid voltage in order to provide a uniform reference voltage to obtain the correct angular position of the phase locked loop. Moreover, the required compensation currents are...
Realization of Intelligent Neuron PID Control with Relay Self-Tuning Arithmetic%用继电自整定实现神经元PID智能控制
Institute of Scientific and Technical Information of China (English)
楚彦君; 马龙; 巨林仓
2001-01-01
In order to meet the requirement of in dustry process control,new methods for tuning PID need to be applied.The neuron self-tuning PID controller combined with relay self-tuning is presented.The i n itial values for neuron self-tuning PID controller are acquired from relay self -tuning arithmetic,then the control mode is switched to adaptive mode.The intel ligent neuron PID control is thus realized.Its stability is analyzed and the sim ulation by using simulink of matlab is done.The satisfied results are achieved.%当前工业过程对控制品质要求越来越高，需要更先进的PID控制器参数整定方法来满足过程控制的要求。该文提出了神经元自适应PID控制器与PID继电自整定相结合构成PID智能控制系统的方法，即利用PID继电自整定算法获取神经元自适应PID控制器权系数的初值，然后将得到的初值送到神经元自适应PID控制器，并且切换到自适应控制模式，从而实现神经元PID智能控制。同时分析了神经元自适应控制系统的稳定性，用Matlab中的Simulink进行了仿真模拟，得到了满意的结果。
The HIVE Tool for Informed Swarm State Space Exploration
Wijs, Anton
2011-01-01
Swarm verification and parallel randomised depth-first search are very effective parallel techniques to hunt bugs in large state spaces. In case bugs are absent, however, scalability of the parallelisation is completely lost. In recent work, we proposed a mechanism to inform the workers which parts of the state space to explore. This mechanism is compatible with any action-based formalism, where a state space can be represented by a labelled transition system. With this extension, each worker can be strictly bounded to explore only a small fraction of the state space at a time. In this paper, we present the HIVE tool together with two search algorithms which were added to the LTSmin tool suite to both perform a preprocessing step, and execute a bounded worker search. The new tool is used to coordinate informed swarm explorations, and the two new LTSmin algorithms are employed for preprocessing a model and performing the individual searches.
A General Theory of Additive State Space Abstractions
Yang, Fan; Holte, Robert; Zahavi, Uzi; Felner, Ariel; 10.1613/jair.2486
2011-01-01
Informally, a set of abstractions of a state space S is additive if the distance between any two states in S is always greater than or equal to the sum of the corresponding distances in the abstract spaces. The first known additive abstractions, called disjoint pattern databases, were experimentally demonstrated to produce state of the art performance on certain state spaces. However, previous applications were restricted to state spaces with special properties, which precludes disjoint pattern databases from being defined for several commonly used testbeds, such as Rubiks Cube, TopSpin and the Pancake puzzle. In this paper we give a general definition of additive abstractions that can be applied to any state space and prove that heuristics based on additive abstractions are consistent as well as admissible. We use this new definition to create additive abstractions for these testbeds and show experimentally that well chosen additive abstractions can reduce search time substantially for the (18,4)-TopSpin puz...
Black Strings, Black Rings and State-space Manifold
Bellucci, Stefano
2011-01-01
State-space geometry is considered, for diverse three and four parameter non-spherical horizon rotating black brane configurations, in string theory and $M$-theory. We have explicitly examined the case of unit Kaluza-Klein momentum $D_1D_5P$ black strings, circular strings, small black rings and black supertubes. An investigation of the state-space pair correlation functions shows that there exist two classes of brane statistical configurations, {\\it viz.}, the first category divulges a degenerate intrinsic equilibrium basis, while the second yields a non-degenerate, curved, intrinsic Riemannian geometry. Specifically, the solutions with finitely many branes expose that the two charged rotating $D_1D_5$ black strings and three charged rotating small black rings consort real degenerate state-space manifolds. Interestingly, arbitrary valued $M_5$-dipole charged rotating circular strings and Maldacena Strominger Witten black rings exhibit non-degenerate, positively curved, comprehensively regular state-space con...
Lucas Filho, Edson Ramiro
2013-01-01
Resumo: Bancos de dados construídos sobre MapReduce, tais como o Hive e Pig, traduzem suas consultas para um ou mais programas MapReduce. Tais programas sao organizados em um Grafo Acíclico Dirigido (GAD) e sao executados seguindo sua ordem de dependencia no GAD. O desempenho dos programas MapReduce depende diretamente da otimizacao (i.e., sintonia) dos parâmetros de configuracao definidos no codigo-fonte. Sistemas como Hive e Pig traduzem consultas para programas sem otimizar estes parâmetro...
RESULTS OF INTERBANK EXCHANGE RATES FORECASTING USING STATE SPACE MODEL
Directory of Open Access Journals (Sweden)
Muhammad Kashif
2008-07-01
Full Text Available This study evaluates the performance of three alternative models for forecasting daily interbank exchange rate of U.S. dollar measured in Pak rupees. The simple ARIMA models and complex models such as GARCH-type models and a state space model are discussed and compared. Four different measures are used to evaluate the forecasting accuracy. The main result is the state space model provides the best performance among all the models.
Institute of Scientific and Technical Information of China (English)
冯本勇
2014-01-01
Aiming at the problem that online self-tuning of intelligent PID controller parameters is very complex and large computing,an improved PID control algorithm(OO-PID)is proposed to tune the PID controller parameters,which an improved particle swarm optimization(PSO)based on the objective initialization of particle swarm(PSOOI)is proposed to tune PID parameters offline and an online optimization algorithm of PID control parameters based on virtual error is proposed to tune the control variable.Information entropy is adopted to tune the initialization of particle swarm in order to enhance the convergence speed and global search of PSO.Virtual error is adopted to tune the control variable in order to enhance static and dynamic performance,reduce caculation time and realize easily.It is demonstrated by numerical simulations on the classical objects to tune the parameters of the PID controller that the processed OOPID algorithm has the excellent global optimization performance and the easy online implementation.%针对现行的智能PID控制算法参数在线整定实现比较复杂、计算量大等问题，提出一种对PID控制器参数进行离线自整定（采用带有目标性初始化粒子群的改进粒子群优化算法进行PID参数整定，即PSOOI）和在线优化（引入基于参考轨迹的虚拟误差进行控制量调整）的控制算法（OOPID）。通过引入信息熵对初始化粒子种群进行调整以提高粒子群算法（PSO）的全局搜索能力和收敛速度。通过引入虚拟误差对控制量进行在线调节提高系统动态性能和稳态性能，且计算量少易于实现。针对典型对象进行PID控制参数自整定，研究结果表明所提出的OO-PID控制算法具有全局优化能力，易于在线实现等优点。
A Learning State-Space Model for Image Retrieval
Directory of Open Access Journals (Sweden)
Lee Greg C
2007-01-01
Full Text Available This paper proposes an approach based on a state-space model for learning the user concepts in image retrieval. We first design a scheme of region-based image representation based on concept units, which are integrated with different types of feature spaces and with different region scales of image segmentation. The design of the concept units aims at describing similar characteristics at a certain perspective among relevant images. We present the details of our proposed approach based on a state-space model for interactive image retrieval, including likelihood and transition models, and we also describe some experiments that show the efficacy of our proposed model. This work demonstrates the feasibility of using a state-space model to estimate the user intuition in image retrieval.
State Space Exploration of RT Systems in the Cloud
Bellettini, Carlo; Capra, Lorenzo; Monga, Mattia
2012-01-01
The growing availability of distributed and cloud computing frameworks make it possible to face complex computational problems in a more effective and convenient way. A notable example is state-space exploration of discrete-event systems specified in a formal way. The exponential complexity of this task is a major limitation to the usage of consolidated analysis techniques and tools. We present and compare two different approaches to state-space explosion, relying on distributed and cloud frameworks, respectively. These approaches were designed and implemented following the same computational schema, a sort of map & fold. They are applied on symbolic state-space exploration of real-time systems specified by (a timed extension of) Petri Nets, by readapting a sequential algorithm implemented as a command-line Java tool. The outcome of several tests performed on a benchmarking specification are presented, thus showing the convenience of cloud approaches.
Neuromorphic Continuous-Time State Space Pole Placement Adaptive Control
Institute of Scientific and Technical Information of China (English)
卢钊; 孙明伟
2003-01-01
A neuromorphic continuous-time state space pole assignment adaptive controller is proposed, which is particularly appropriate for controlling a large-scale time-variant state-space model due to the parallely distributed nature of neurocomputing. In our approach, Hopfield neural network is exploited to identify the parameters of a continuous-time state-space model, and a dedicated recurrent neural network is designed to compute pole placement feedback control law in real time. Thus the identification and the control computation are incorporated in the closed-loop, adaptive, real-time control system. The merit of this approach is that the neural networks converge to their solutions very quickly and simultaneously.
Multivariate time series with linear state space structure
Gómez, Víctor
2016-01-01
This book presents a comprehensive study of multivariate time series with linear state space structure. The emphasis is put on both the clarity of the theoretical concepts and on efficient algorithms for implementing the theory. In particular, it investigates the relationship between VARMA and state space models, including canonical forms. It also highlights the relationship between Wiener-Kolmogorov and Kalman filtering both with an infinite and a finite sample. The strength of the book also lies in the numerous algorithms included for state space models that take advantage of the recursive nature of the models. Many of these algorithms can be made robust, fast, reliable and efficient. The book is accompanied by a MATLAB package called SSMMATLAB and a webpage presenting implemented algorithms with many examples and case studies. Though it lays a solid theoretical foundation, the book also focuses on practical application, and includes exercises in each chapter. It is intended for researchers and students wor...
Determining state-space models from sequential output data
Lin, Jiguan Gene
1988-01-01
This talk focuses on the determination of state-space models for large space systems using only the output data. The output data could be generated by the unknown or deliberate initial conditions of the space structure in question. We shall review some relevant fundamental work on the state-space modeling of sequential output data that is potentially applicable to large space structures. If formulated in terms of some generalized Markov parameters, this approach is in some sense similar to, but much simpler than, the Juang-Pappa Eigensystem Realization Algorithm (ERA) and the Ho-Kalman construction procedure.
State-Space Modelling of Loudspeakers using Fractional Derivatives
DEFF Research Database (Denmark)
King, Alexander Weider; Agerkvist, Finn T.
2015-01-01
This work investigates the use of fractional order derivatives in modeling moving-coil loudspeakers. A fractional order state-space solution is developed, leading the way towards incorporating nonlinearities into a fractional order system. The method is used to calculate the response....... It is shown that the identified parameters can be used in a linear fractional order state-space model to simulate the loudspeakers’ time domain response...... of a fractional harmonic oscillator, representing the mechanical part of a loudspeaker, showing the effect of the fractional derivative and its relationship to viscoelasticity. Finally, a loudspeaker model with a fractional order viscoelastic suspension and fractional order voice coil is fit to measurement data...
Set point control in the state space setting
DEFF Research Database (Denmark)
Poulsen, Niels Kjølstad
. The focus is in this report related to the problem of handling a set point or a constant reference in a state space setting. In principle just about any (state space control) design methodology may be applied. Here the presentation is based on LQ design, but other types such as poleplacement can be applied......This report is intented as a supplement or an extension to the material used in connection to or after the courses Stochastic Adaptive Control (02421) and Static and Dynamic Optimization (02711) given at the Department of Informatics and Mathematical Modelling, The Technical University of Denmark...
State space modeling of Memristor-based Wien oscillator
Talukdar, Abdul Hafiz Ibne
2011-12-01
State space modeling of Memristor based Wien \\'A\\' oscillator has been demonstrated for the first time considering nonlinear ion drift in Memristor. Time dependant oscillating resistance of Memristor is reported in both state space solution and SPICE simulation which plausibly provide the basis of realizing parametric oscillation by Memristor based Wien oscillator. In addition to this part Memristor is shown to stabilize the final oscillation amplitude by means of its nonlinear dynamic resistance which hints for eliminating diode in the feedback network of conventional Wien oscillator. © 2011 IEEE.
A Compositional Sweep-Line State Space Exploration Method
DEFF Research Database (Denmark)
Kristensen, Lars Michael; Mailund, Thomas
2002-01-01
State space exploration is a main approach to verification of finite-state systems. The sweep-line method exploits a certain kind of progress present in many systems to reduce peak memory usage during state space exploration. We present a new sweep-line algorithm for a compositional setting where...... systems are composed of subsystems. The compositional setting makes it possible to divide subsystem progress measures into monotone and non-monotone progress measures to further reduce peak memory usage. We show that in a compositional setting, it is possible to automatically obtain a progress measure...
Transformation of state space for two-parameter Markov processes
Institute of Scientific and Technical Information of China (English)
周健伟
1996-01-01
Let X=(X) be a two-parameter *-Markov process with a transition function (p1, p2, p), where X, takes values in the state space (Er,), T=[0,)2. For each r T, let f, be a measurable transformation of (E,) into the state space (E’r, ). Set Y,=f,(X,), r T. A sufficient condition is given for the process Y=(Yr) still to be a two-parameter *-Markov process with a transition function in terms of transition function (p1, p2, p) and fr. For *-Markov families of two-parameter processes with a transition function, a similar problem is also discussed.
A Right Coprime Factorization of Neural State Space Models
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon
2007-01-01
In recent years, various methods for identification of nonlinear systems in closed loop using open-loop approaches have received considerable attention. However, these methods rely on differentially coprime factorizations of the nonlinear plants, which can be difficult to compute in practice....... To address this issue, this paper presents various technical results leading up to explicit formulae for right coprime factorizations of neural state space models, i.e., nonlinear system models represented in state space using neural networks, which satisfy a Bezout identity. ...
Interaction Network, State Space and Control in Social Dynamics
Aydogdu, Aylin; McQuade, Sean; Piccoli, Benedetto; Duteil, Nastassia Pouradier; Rossi, Francesco; Trélat, Emmanuel
2016-01-01
In the present chapter we study the emergence of global patterns in large groups in first and second-order multi-agent systems, focusing on two ingredients that influence the dynamics: the interaction network and the state space. The state space determines the types of equilibrium that can be reached by the system. Meanwhile, convergence to specific equilibria depends on the connectivity of the interaction network and on the interaction potential. When the system does not satisfy the necessary conditions for convergence to the desired equilibrium, control can be exerted, both on finite-dimensional systems and on their mean-field limit.
Self-tuning of PID Parameters Basedon Genetic Algorithms%基于遗传算法PID参数的自动整定
Institute of Scientific and Technical Information of China (English)
占永明; 罗中明
2000-01-01
A method of self-tuning of PID parameters based on genetic algorithms is presented.ItisdesignedforminimumITSE,andthebestPIDparameteris attained.Inaddition,animprovedgeneticalgorithmsispresented.%提出了一种基于遗传算法PID参数自整定的方法，算法以ITSE最小作为优化目标且进行全局寻优，实现了PID参数的全局最优化，并结合工程 实际提出了一种改进的遗传算法。
PID自整定调节器及在自动调节中的应用%PID self- tuning regulator and its application in the automatic regulation
Institute of Scientific and Technical Information of China (English)
饶明胜
2011-01-01
PID controlling is the most widely used control method in the proceed control. The paper presents the way of self - regulation of the I/A Series PID self- tuning regulator produced by the FOXBORO corporation and its application in the drum level control system.%PID控制是过程控制中应用最广泛的控制方法。文中简述了FOXBORO公司的I／A’SeriesPIDE自整定调节器的参数整定方式以及该调节器在汽包水位调节控制系统中的应用。
A state-space algorithm for the spectral factorization
Kraffer, F.; Kwakernaak, H.
1997-01-01
This paper presents an algorithm for the spectral factorization of a para-Hermitian polynomial matrix. The algorithm is based on polynomial matrix to state space and vice versa conversions, and avoids elementary polynomial operations in computations; It relies on well-proven methods of numerical lin
Fast Filtering and Smoothing for Multivariate State Space Models
Koopman, S.J.M.; Durbin, J.
1998-01-01
This paper gives a new approach to diffuse filtering and smoothing for multivariate state space models. The standard approach treats the observations as vectors while our approach treats each element of the observational vector individually. This strategy leads to computationally efficient methods f
Dynamic State Space Partitioning for External Memory Model Checking
DEFF Research Database (Denmark)
Evangelista, Sami; Kristensen, Lars Michael
2009-01-01
We describe a dynamic partitioning scheme usable by model checking techniques that divide the state space into partitions, such as most external memory and distributed model checking algorithms. The goal of the scheme is to reduce the number of transitions that link states belonging to different...
Parameter redundancy in discrete state-space and integrated models.
Cole, Diana J; McCrea, Rachel S
2016-09-01
Discrete state-space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state-space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state-space models using discrete analogues of methods for continuous state-space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant. © 2016 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Balanced state-space representations : a polynomial algebraic approach
Rapisarda, P.; Trentelman, H.L.
2009-01-01
We show how to compute a minimal Riccati-balanced state map and a minimal Riccati-balanced state space representation starting from an image representation of a strictly dissipative system. The result is based on an iterative procedure to solve a generalization of the Nevanlinna interpolation proble
Embedding a State Space Model Into a Markov Decision Process
DEFF Research Database (Denmark)
Nielsen, Lars Relund; Jørgensen, Erik; Højsgaard, Søren
2011-01-01
estimated from data collected from the animal or herd. State space models (SSMs) are a general tool for modeling repeated measurements over time where the model parameters can evolve dynamically. In this paper we consider methods for embedding an SSM into an MDP with finite state and action space. Different...
The sweep-line state space exploration method
DEFF Research Database (Denmark)
Jensen, Kurt; Kristensen, Lars M.; Mailund, Thomas
2012-01-01
The sweep-line method exploits intrinsic progress in concurrent systems to alleviate the state explosion problem in explicit state model checking. The concept of progress makes it possible to delete states from the memory during state space exploration and thereby reduce peak memory usage...
State Space Reduction of Linear Processes Using Control Flow Reconstruction
Pol, van de Jaco; Timmer, Mark; Liu, Z.; Ravn, A.P.
2009-01-01
We present a new method for fighting the state space explosion of process algebraic specifications, by performing static analysis on an intermediate format: linear process equations (LPEs). Our method consists of two steps: (1) we reconstruct the LPE's control flow, detecting control flow parameters
State Space Reduction of Linear Processes using Control Flow Reconstruction
Pol, van de Jaco; Timmer, Mark
2009-01-01
We present a new method for fighting the state space explosion of process algebraic specifications, by performing static analysis on an intermediate format: linear process equations (LPEs). Our method consists of two steps: (1) we reconstruct the LPE's control flow, detecting control flow parameters
On Path Dependent State Space for the Proca Field
Gaitan, R
1999-01-01
A gauge formulation for the Proca model quantum theory in an open path functional space representation is revisited. The path dependent vacuum state is obtained. Starting from this one, other excited states can be obtained too. Additionally, the functional integration measure needed to define an internal product in the state space is constructed.
Optimal State-Space Reduction for Pedigree Hidden Markov Models
Kirkpatrick, Bonnie
2012-01-01
To analyze whole-genome genetic data inherited in families, the likelihood is typically obtained from a Hidden Markov Model (HMM) having a state space of 2^n hidden states where n is the number of meioses or edges in the pedigree. There have been several attempts to speed up this calculation by reducing the state-space of the HMM. One of these methods has been automated in a calculation that is more efficient than the naive HMM calculation; however, that method treats a special case and the efficiency gain is available for only those rare pedigrees containing long chains of single-child lineages. The other existing state-space reduction method treats the general case, but the existing algorithm has super-exponential running time. We present three formulations of the state-space reduction problem, two dealing with groups and one with partitions. One of these problems, the maximum isometry group problem was discussed in detail by Browning and Browning. We show that for pedigrees, all three of these problems hav...
An Embeddable Virtual Machine for State Space Generation
Weber, M.; Bosnacki, D.; Edelkamp, S.
2007-01-01
The semantics of modelling languages are not always specified in a precise and formal way, and their rather complex underlying models make it a non-trivial exercise to reuse them in newly developed tools. We report on experiments with a virtual machine-based approach for state space generation. The
Discrete state space modeling and control of nonlinear unknown systems.
Savran, Aydogan
2013-11-01
A novel procedure for integrating neural networks (NNs) with conventional techniques is proposed to design industrial modeling and control systems for nonlinear unknown systems. In the proposed approach, a new recurrent NN with a special architecture is constructed to obtain discrete-time state-space representations of nonlinear dynamical systems. It is referred as the discrete state-space neural network (DSSNN). In the DSSNN, the outputs of the hidden layer neurons of the DSSNN represent the system's (pseudo) state. The inputs are fed to output neurons and the delayed outputs of the hidden layer neurons are fed to their inputs via adjustable weights. The discrete state space model of the actual system is directly obtained by training the DSSNN with the input-output data. A training procedure based on the back-propagation through time (BPTT) algorithm is developed. The Levenberg-Marquardt (LM) method with a trust region approach is used to update the DSSNN weights. Linear state space models enable to use well developed conventional analysis and design techniques. Thus, building a linear model of a system has primary importance in industrial applications. Thus, a suitable linearization procedure is proposed to derive the linear state space model from the nonlinear DSSNN representation. The controllability, observability and stability properties are examined. The state feedback controllers are designed with both the linear quadratic regulator (LQR) and the pole placement techniques. The regulator and servo control problems are both addressed. A full order observer is also designed to estimate the state variables. The performance of the proposed procedure is demonstrated by applying for both single-input single-output (SISO) and multiple-input multiple-output (MIMO) nonlinear control problems. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Marzbanrad, Javad; Tahbaz-zadeh Moghaddam, Iman
2016-09-01
The main purpose of this paper is to design a self-tuning control algorithm for an adaptive cruise control (ACC) system that can adapt its behaviour to variations of vehicle dynamics and uncertain road grade. To this aim, short-time linear quadratic form (STLQF) estimation technique is developed so as to track simultaneously the trend of the time-varying parameters of vehicle longitudinal dynamics with a small delay. These parameters are vehicle mass, road grade and aerodynamic drag-area coefficient. Next, the values of estimated parameters are used to tune the throttle and brake control inputs and to regulate the throttle/brake switching logic that governs the throttle and brake switching. The performance of the designed STLQF-based self-tuning control (STLQF-STC) algorithm for ACC system is compared with the conventional method based on fixed control structure regarding the speed/distance tracking control modes. Simulation results show that the proposed control algorithm improves the performance of throttle and brake controllers, providing more comfort while travelling, enhancing driving safety and giving a satisfactory performance in the presence of different payloads and road grade variations.
Directory of Open Access Journals (Sweden)
Kutz J. Nathan
2015-12-01
Full Text Available We demonstrate that a software architecture using innovations in machine learning and adaptive control provides an ideal integration platform for self-tuning optics. For mode-locked lasers, commercially available optical telecom components can be integrated with servocontrollers to enact a training and execution software module capable of self-tuning the laser cavity even in the presence of mechanical and/or environmental perturbations, thus potentially stabilizing a frequency comb. The algorithm training stage uses an exhaustive search of parameter space to discover best regions of performance for one or more objective functions of interest. The execution stage first uses a sparse sensing procedure to recognize the parameter space before quickly moving to the near optimal solution and maintaining it using the extremum seeking control protocol. The method is robust and equationfree, thus requiring no detailed or quantitatively accurate model of the physics. It can also be executed on a broad range of problems provided only that suitable objective functions can be found and experimentally measured.
Zhang, Zhen; Ma, Cheng; Zhu, Rong
2016-10-14
High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial-temporal complexity. This paper presents a multi-input multi-output (MIMO) self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional-integral-derivative (PID) neural network (FCPIDNN) and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions.
AC Speed Regulating Systems Based on the Fuzzy Self-tuning PID Control%交流调速系统的模糊自校正PID控制
Institute of Scientific and Technical Information of China (English)
王丁磊; 马辉
2011-01-01
分析了异步电动机的变频调速原理,在利用矢量控制技术实现转矩和磁通解耦的基础上,增加模糊自校正PID算法实现了对速度的精确控制.通过MATLAB与传统PID速度控制算法进行仿真比较,证明了模糊自校正PID的控制性能要优越于传统PID的控制效果.%The principle of asynchronous motor's frequency control has been analyzed in this paper. Achieve precise control of speed by used the fuzzy self-tuning PID algorithm based on the technology of vector control torque and decoupling of magnetic flux. It has been proved that the control effect of the fuzzy self-tuning PID algorithm is superior to the control effect of traditional PID through simulation in MATLAB.
基于模糊PID的PMSM矢量控制系统研究%Research of PMSM Vector Control System Based on Fuzzy Self-tuning PID
Institute of Scientific and Technical Information of China (English)
张涛; 张晓江; 陆文龙; 叶玉龙
2012-01-01
在MATLAB7／Simulink环境下，建立了永磁交流伺服电机的矢量控制系统模型，并在速度环建立了基于模糊自整定PID控制的FUZZY—pid模型，对PMSM电机的位置控制和突加负载情况进行了仿真研究，并与基于常规PID的仿真结果进行了对比．对比结果表明．采用模糊自整定PID控制算法，系统位置控制性能明显优于常规PID控制算法。%Established the simulation model for PMSM (permanent magnet synchronous motor) vector control system and the model of FUZZY-pid controller based on FUZZY self-tuning PID control algorithm in the environment of MATLAB 7/Simulink .Simulated the position control and the case of sudden load of PMSM. The simulated results were compared with that of PMSM vector control based on conventional PID, which indicated that the performance of PMSM vector control system by FUZZY self-tuning PID control algo- rithm is obviously better than that by conventional PID control algorithm.
Application of fuzzy logic controller with self-tuning PID parameters%PID参数自整定模糊控制器的应用
Institute of Scientific and Technical Information of China (English)
李付举
2011-01-01
In view of electrical heating furnace's non-linear control object characteristics of large inertia, pure time-delay and parameters time-variation and the hard-to-tune characteristic of conventional PID control parameter, a new method for fuzzy control with self-tuning PID parameters was put forward. A fuzzy controller with self-tuning PID parameters was designed and applied in the furnace's temperature control system. The result shows that fuzzy control with self-ttming PID parameters eliminates the system's steady state error, has neither overshoot nor oscillation but great robustness, and is easily handled; therefore it is of some practical value.%针对电加热炉大惯性、纯滞后、参数时变的非线性对象的控制的特点,以及常规PID控制参数不易调节的特点,提出了一种PID参数自整定模糊控制方法,设计了PID参数自整定模糊控制器,并在炉温控制系统中应用.实验结果表明:PID参数自整定模糊控制消除了系统的稳态误差,没有超调和振荡,鲁棒性较强,而且简单易行,具有一定的实用价值.
自校正P ID算法在再热器中的应用%Application Of Self-tuning PID In Reheater
Institute of Scientific and Technical Information of China (English)
李志明
2011-01-01
针对再热器的温度控制问题,设计了一种极点配置PID控制器。给出了CARMA模型,引入带遗忘因子的最小二乘实时参数估计算法和带数字滤波器的增量式PID控制算法,同时给出了极点配置自校正PID的整定方法过程,建立PID参数与系统参数及控制性能指标之间的关系式,并进行了MATLAB仿真。%In order to solve the problem of temperature control of Power plant reheater,this paper designs a self-tuning PID（Proportional-Integral-Derivative） controller based on Pole placement.System CARMA model,recursive least squares method with forgetting factor and the digital filter increment PID control algorithm were presented.Pol placement self-tuning PID method and process were introduced,and the relation of PID with system parameters and Control performance parameter were formulate.Model of temperature control system of multifunctional dehumidifier was applied in MATLAB simulation with this method.
Validation of ecological state space models using the Laplace approximation
DEFF Research Database (Denmark)
Thygesen, Uffe Høgsbro; Albertsen, Christoffer Moesgaard; Berg, Casper Willestofte
2017-01-01
Many statistical models in ecology follow the state space paradigm. For such models, the important step of model validation rarely receives as much attention as estimation or hypothesis testing, perhaps due to lack of available algorithms and software. Model validation is often based on a naive...... for estimation in general mixed effects models. Implementing one-step predictions in the R package Template Model Builder, we demonstrate that it is possible to perform model validation with little effort, even if the ecological model is multivariate, has non-linear dynamics, and whether observations...... are continuous or discrete. With both simulated data, and a real data set related to geolocation of seals, we demonstrate both the potential and the limitations of the techniques. Our results fill a need for convenient methods for validating a state space model, or alternatively, rejecting it while indicating...
Estimation methods for nonlinear state-space models in ecology
DEFF Research Database (Denmark)
Pedersen, Martin Wæver; Berg, Casper Willestofte; Thygesen, Uffe Høgsbro
2011-01-01
The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta...... logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden...... Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance...
Latent state-space models for neural decoding.
Aghagolzadeh, Mehdi; Truccolo, Wilson
2014-01-01
Ensembles of single-neurons in motor cortex can show strong low-dimensional collective dynamics. In this study, we explore an approach where neural decoding is applied to estimated low-dimensional dynamics instead of to the full recorded neuronal population. A latent state-space model (SSM) approach is used to estimate the low-dimensional neural dynamics from the measured spiking activity in population of neurons. A second state-space model representation is then used to decode kinematics, via a Kalman filter, from the estimated low-dimensional dynamics. The latent SSM-based decoding approach is illustrated on neuronal activity recorded from primary motor cortex in a monkey performing naturalistic 3-D reach and grasp movements. Our analysis show that 3-D reach decoding performance based on estimated low-dimensional dynamics is comparable to the decoding performance based on the full recorded neuronal population.
State-space Manifold and Rotating Black Holes
Bellucci, Stefano
2010-01-01
We study a class of fluctuating higher dimensional black hole configurations obtained in string theory/ $M$-theory compactifications. We explore the intrinsic Riemannian geometric nature of Gaussian fluctuations arising from the Hessian of the coarse graining entropy, defined over an ensemble of brane microstates. It has been shown that the state-space geometry spanned by the set of invariant parameters is non-degenerate, regular and has a negative scalar curvature for the rotating Myers-Perry black holes, Kaluza-Klein black holes, supersymmetric $AdS_5$ black holes, $D_1$-$D_5$ configurations and the associated BMPV black holes. Interestingly, these solutions demonstrate that the principal components of the state-space metric tensor admit a positive definite form, while the off diagonal components do not. Furthermore, the ratio of diagonal components weakens relatively faster than the off diagonal components, and thus they swiftly come into an equilibrium statistical configuration. Novel aspects of the scali...
Prediction and interpolation of time series by state space models
Helske, Jouni
2015-01-01
A large amount of data collected today is in the form of a time series. In order to make realistic inferences based on time series forecasts, in addition to point predictions, prediction intervals or other measures of uncertainty should be presented. Multiple sources of uncertainty are often ignored due to the complexities involved in accounting them correctly. In this dissertation, some of these problems are reviewed and some new solutions are presented. A state space approach...
State Space identification of Civil Engineering Structures from Output Measurements
1996-01-01
This paper presents the results from a state space system identification simulation study of a 5-degrees-of freedom system driven by white noise. The aim of the study is to compare the durability of the fairly new Stochastic Subspace Technique (SST) with more well-known techniques for identification of civil engineering structures. The SST is compared with the stochastic realization estimator Matrix Block Hankel (MBH) and a prediction error method (PEM). The results show that the investigated...
State-Space Methods for µ-Analysis
Helmersson, Anders
1994-01-01
This paper discusses state-space methods for analyzing stability of continuous time linear systems subject to structured uncertainties. Four types of uncertainties are discussed: linear parametric and dynamic uncertainties (real and complex µ) and nonlinear parametric and dynamic uncertainties. The method employs LMIs equipped with a scaling matrix adapted to the type of uncertainty. For parametric uncertainties conservativeness is reduced by branch and bound schemes. Different types of uncer...
Automatic Design of a Maglev Controller in State Space
1991-12-01
conventional trains with steel wheels on steel rails. Several experimen- tal maglev systems in Germany and Japan have demonstrated that this mode of...Design of a Maglev Controller in State Space Feng Zhao Richard Thornton Abstract We describe the automatic synthesis of a global nonlinear controller for...the global switching points of the controller is presented. The synthesized control system can stabilize the maglev vehicle with large initial displace
Quantum-dot Semiconductor Optical Amplifiers in State Space Model
Institute of Scientific and Technical Information of China (English)
Hussein Taleb; Kambiz Abedi; Saeed Golmohammadi
2013-01-01
A state space model (SSM) is derived for quantum-dot semiconductor optical amplifiers (QD-SOAs).Rate equations of QD-SOA are formulated in the form of state update equations,where average occupation probabilities along QD-SOA cavity are considered as state variables of the system.Simulations show that SSM calculates QD-SOA's static and dynamic characteristics with high accuracy.
State Space identification of Civil Engineering Structures from Output Measurements
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Andersen, P.
1997-01-01
This paper presents the results from a state space system identification simulation study of a 5-degrees-of freedom system driven by white noise. The aim of the study is to compare the durability of the fairly new Stochastic Subspace Technique (SST) with more well-known techniques......, it is found that the new SST technique gives quickly good results compared with the PEM which takes more time with only a limited improvement of the fit on data....
State-Space Methods for µ-Analysis
Helmersson, Anders
1994-01-01
This paper discusses state-space methods for analyzing stability of continuous time linear systems subject to structured uncertainties. Four types of uncertainties are discussed: linear parametric and dynamic uncertainties (real and complex µ) and nonlinear parametric and dynamic uncertainties. The method employs LMIs equipped with a scaling matrix adapted to the type of uncertainty. For parametric uncertainties conservativeness is reduced by branch and bound schemes. Different types of uncer...
Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models.
Liu, Ziyue; Cappola, Anne R; Crofford, Leslie J; Guo, Wensheng
2014-01-01
The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this paper, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with both population-average and subject-specific components. The bivariate model is constructed by concatenating the univariate models based on the hypothesized relationship. Because of the flexible framework of state space form, the resultant models not only can handle complex individual profiles, but also can incorporate complex relationships between two hormones, including both concurrent and feedback relationship. Estimation and inference are based on marginal likelihood and posterior means and variances. Computationally efficient Kalman filtering and smoothing algorithms are used for implementation. Application of the proposed method to a study of chronic fatigue syndrome and fibromyalgia reveals that the relationships between adrenocorticotropic hormone and cortisol in the patient group are weaker than in healthy controls.
一种改进的模式识别自整定PID控制方法%Improved Self-tuning PID Control Based on Pattern Recognition
Institute of Scientific and Technical Information of China (English)
穆克; 苏成利
2012-01-01
To solve the shortcomings of traditional PID conlroller in dealing with disturbance rejection and robustness. A new self-tuning PID-based algorithm for pattern recognition is proposed. In the algorithm, the parameter tuning rules of the PID controller are innovated, and the specificformula for setting the rules is put forward. In order to achieve the water tank level control algorithm in the application of laboratory, using OPC technology to achieve the MATLAB software and data configuration software MCGS real-time interaction. Experimental results show that the given tuning rules in MATLAB simulation and tank level control applications take the effect of good tuning. The control performance of the proposed self-tuning PID control method is superior to that of traditional PID method.%针对常规PID控制器不能很好地兼顾抗干扰性与鲁棒性的缺点,提出一种新的基于模式识别自整定PID控制算法.该算法对参数整定规则进行了探索和创新,并给出具体的整定规则公式.为了实现算法在实验室水箱液位控制的应用,采用OPC技术实现了MATLAB软件与MCGS组态软件的数据实时交互.实验结果表明,该规则在MALAB仿真和水箱液位控制应用中取得到了很好的整定效果,控制性能优于常规PID控制.
Nonlinear state space model identification of synchronous generators
Energy Technology Data Exchange (ETDEWEB)
Dehghani, M.; Nikravesh, S.K.Y. [Electrical Engineering Department, Amirkabir University of Technology, Tehran (Iran)
2008-05-15
A method for identification of a synchronous generator is suggested in this paper. The method uses the theoretical relations of machine parameters and the Prony method to find the state space model of the system. Such models are useful for controller design and stability tests. The proposed identification method is applied to a third order model of a synchronous generator. In this study, the field voltage is considered as the input and the active output power and the rotor angle are considered as the outputs of the synchronous generator. Simulation results show good accuracy of the identified model. (author)
Macro and micro view on steady states in state space
Sobota, Branislav
2010-01-01
This paper describes visualization of chaotic attractor and elements of the singularities in 3D space. 3D view of these effects enables to create a demonstrative projection about relations of chaos generated by physical circuit, the Chua's circuit. Via macro views on chaotic attractor is obtained not only visual space illustration of representative point motion in state space, but also its relation to planes of singularity elements. Our created program enables view on chaotic attractor both in 2D and 3D space together with plane objects visualization -- elements of singularities.
Parameter and State Estimator for State Space Models
Directory of Open Access Journals (Sweden)
Ruifeng Ding
2014-01-01
Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
Parameter and state estimator for state space models.
Ding, Ruifeng; Zhuang, Linfan
2014-01-01
This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
Averaging in Parametrically Excited Systems – A State Space Formulation
Directory of Open Access Journals (Sweden)
Pfau Bastian
2016-01-01
Full Text Available Parametric excitation can lead to instabilities as well as to an improved stability behavior, depending on whether a parametric resonance or anti-resonance is induced. In order to calculate the stability domains and boundaries, the method of averaging is applied. The problem is reformulated in state space representation, which allows a general handling of the averaging method especially for systems with non-symmetric system matrices. It is highlighted that this approach can enhance the first order approximation significantly. Two example systems are investigated: a generic mechanical system and a flexible rotor in journal bearings with adjustable geometry.
Replacement Capability Options for the United States Space Shuttle
2013-09-01
first designed for reuse ” (NASA, 2000). 1. United States Space Shuttle Program (1981–2011) The first operational Space Shuttle was Columbia (OV-102...Week article on China’s future plans for their Long March Launch vehicles, “China is developing three basic rocket modules, with diameters of 2.25... wastewater , which will burn up with the spacecraft when it re-enters the Earth’s atmosphere. The Cargo Module can hold 1,000 to 1,700 kilograms (2,205
Institute of Scientific and Technical Information of China (English)
邓自立; 李春波
2007-01-01
For the multisensor systems with unknown noise statistics, using the modern time series analysis method, based on on-line identification of the moving average (MA) innovation models, and based on the solution of the matrix equations for correlation function, estimators of the noise variances are obtained, and under the linear minimum variance optimal information fusion criterion weighted by diagonal matrices, a self-tuning information fusion Kalman predictor is presented, which realizes the self-tuning decoupled fusion Kalman predictors for the state components. Based on the dynamic error system, a new convergence analysis method is presented for self-tuning fuser. A new concept of convergence in a realization is presented, which is weaker than the convergence with probability one. It is strictly proved that if the parameter estimation of the MA innovation models is consistent, then the self-tuning fusion Kalman predictor will converge to the optimal fusion Kalman predictor in a realization, or with probability one, so that it has asymptotic optimality. It can reduce the computational burden, and is suitable for real time applications. A simulation example for a target tracking system shows its effectiveness.
Complex network analysis of state spaces for random Boolean networks
Energy Technology Data Exchange (ETDEWEB)
Shreim, Amer [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Berdahl, Andrew [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Sood, Vishal [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Grassberger, Peter [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Paczuski, Maya [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada)
2008-01-15
We apply complex network analysis to the state spaces of random Boolean networks (RBNs). An RBN contains N Boolean elements each with K inputs. A directed state space network (SSN) is constructed by linking each dynamical state, represented as a node, to its temporal successor. We study the heterogeneity of these SSNs at both local and global scales, as well as sample to-sample fluctuations within an ensemble of SSNs. We use in-degrees of nodes as a local topological measure, and the path diversity (Shreim A et al 2007 Phys. Rev. Lett. 98 198701) of an SSN as a global topological measure. RBNs with 2 {<=} K {<=} 5 exhibit non-trivial fluctuations at both local and global scales, while K = 2 exhibits the largest sample-to-sample (possibly non-self-averaging) fluctuations. We interpret the observed 'multi scale' fluctuations in the SSNs as indicative of the criticality and complexity of K = 2 RBNs. 'Garden of Eden' (GoE) states are nodes on an SSN that have in-degree zero. While in-degrees of non-GoE nodes for K > 1 SSNs can assume any integer value between 0 and 2{sup N}, for K = 1 all the non-GoE nodes in a given SSN have the same in-degree which is always a power of two.
State space approach to single molecule localization in fluorescence microscopy.
Vahid, Milad R; Chao, Jerry; Kim, Dongyoung; Ward, E Sally; Ober, Raimund J
2017-03-01
Single molecule super-resolution microscopy enables imaging at sub-diffraction-limit resolution by producing images of subsets of stochastically photoactivated fluorophores over a sequence of frames. In each frame of the sequence, the fluorophores are accurately localized, and the estimated locations are used to construct a high-resolution image of the cellular structures labeled by the fluorophores. Many methods have been developed for localizing fluorophores from the images. The majority of these methods comprise two separate steps: detection and estimation. In the detection step, fluorophores are identified. In the estimation step, the locations of the identified fluorophores are estimated through an iterative approach. Here, we propose a non-iterative state space-based localization method which combines the detection and estimation steps. We demonstrate that the estimated locations obtained from the proposed method can be used as initial conditions in an estimation routine to potentially obtain improved location estimates. The proposed method models the given image as the frequency response of a multi-order system obtained with a balanced state space realization algorithm based on the singular value decomposition of a Hankel matrix. The locations of the poles of the resulting system determine the peak locations in the frequency domain, and the locations of the most significant peaks correspond to the single molecule locations in the original image. The performance of the method is validated using both simulated and experimental data.
Projective Limits of State Spaces IV. Fractal Label Sets
Lanéry, Suzanne
2015-01-01
Instead of formulating the state space of a quantum field theory over one big Hilbert space, it has been proposed by Kijowski [Kijowski 1977] to represent quantum states as projective families of density matrices over a collection of smaller, simpler Hilbert spaces. One can thus bypass the need to select a vacuum state for the theory, and still be provided with an explicit and constructive description of the quantum state space, at least as long as the label set indexing the projective structure is countable. Because uncountable label sets are much less practical in this context, we develop in the present article a general procedure to trim an originally uncountable label set down to countable cardinality. In particular, we investigate how to perform this tightening of the label set in a way that preserves both the physical content of the algebra of observables and its symmetries. This work is notably motivated by applications to the holonomy-flux algebra underlying Loop Quantum Gravity. Building on earlier w...
A hierarchical state space approach to affective dynamics
Lodewyckx, Tom; Tuerlinckx, Francis; Kuppens, Peter; Allen, Nicholas; Sheeber, Lisa
2010-01-01
Linear dynamical system theory is a broad theoretical framework that has been applied in various research areas such as engineering, econometrics and recently in psychology. It quantifies the relations between observed inputs and outputs that are connected through a set of latent state variables. State space models are used to investigate the dynamical properties of these latent quantities. These models are especially of interest in the study of emotion dynamics, with the system representing the evolving emotion components of an individual. However, for simultaneous modeling of individual and population differences, a hierarchical extension of the basic state space model is necessary. Therefore, we introduce a Bayesian hierarchical model with random effects for the system parameters. Further, we apply our model to data that were collected using the Oregon adolescent interaction task: 66 normal and 67 depressed adolescents engaged in a conflict interaction with their parents and second-to-second physiological and behavioral measures were obtained. System parameters in normal and depressed adolescents were compared, which led to interesting discussions in the light of findings in recent literature on the links between cardiovascular processes, emotion dynamics and depression. We illustrate that our approach is flexible and general: The model can be applied to any time series for multiple systems (where a system can represent any entity) and moreover, one is free to focus on whatever component of the versatile model. PMID:21516216
Nonlinear regime-switching state-space (RSSS) models.
Chow, Sy-Miin; Zhang, Guangjian
2013-10-01
Nonlinear dynamic factor analysis models extend standard linear dynamic factor analysis models by allowing time series processes to be nonlinear at the latent level (e.g., involving interaction between two latent processes). In practice, it is often of interest to identify the phases--namely, latent "regimes" or classes--during which a system is characterized by distinctly different dynamics. We propose a new class of models, termed nonlinear regime-switching state-space (RSSS) models, which subsumes regime-switching nonlinear dynamic factor analysis models as a special case. In nonlinear RSSS models, the change processes within regimes, represented using a state-space model, are allowed to be nonlinear. An estimation procedure obtained by combining the extended Kalman filter and the Kim filter is proposed as a way to estimate nonlinear RSSS models. We illustrate the utility of nonlinear RSSS models by fitting a nonlinear dynamic factor analysis model with regime-specific cross-regression parameters to a set of experience sampling affect data. The parallels between nonlinear RSSS models and other well-known discrete change models in the literature are discussed briefly.
Mapping from Speech to Images Using Continuous State Space Models
DEFF Research Database (Denmark)
Lehn-Schiøler, Tue; Hansen, Lars Kai; Larsen, Jan
2005-01-01
In this paper a system that transforms speech waveforms to animated faces are proposed. The system relies on continuous state space models to perform the mapping, this makes it possible to ensure video with no sudden jumps and allows continuous control of the parameters in 'face space......'. The performance of the system is critically dependent on the number of hidden variables, with too few variables the model cannot represent data, and with too many overfitting is noticed. Simulations are performed on recordings of 3-5 sec.\\$\\backslash\\$ video sequences with sentences from the Timit database. From...... a subjective point of view the model is able to construct an image sequence from an unknown noisy speech sequence even though the number of training examples are limited....
A nonlinear state-space approach to hysteresis identification
Noël, J. P.; Esfahani, A. F.; Kerschen, G.; Schoukens, J.
2017-02-01
Most studies tackling hysteresis identification in the technical literature follow white-box approaches, i.e. they rely on the assumption that measured data obey a specific hysteretic model. Such an assumption may be a hard requirement to handle in real applications, since hysteresis is a highly individualistic nonlinear behaviour. The present paper adopts a black-box approach based on nonlinear state-space models to identify hysteresis dynamics. This approach is shown to provide a general framework to hysteresis identification, featuring flexibility and parsimony of representation. Nonlinear model terms are constructed as a multivariate polynomial in the state variables, and parameter estimation is performed by minimising weighted least-squares cost functions. Technical issues, including the selection of the model order and the polynomial degree, are discussed, and model validation is achieved in both broadband and sine conditions. The study is carried out numerically by exploiting synthetic data generated via the Bouc-Wen equations.
On Volterra quadratic stochastic operators with continual state space
Energy Technology Data Exchange (ETDEWEB)
Ganikhodjaev, Nasir; Hamzah, Nur Zatul Akmar [Department of Computational and Theoretical Sciences, Faculty of Science, International Islamic University, Jalan Sultan Ahmad Shah, Bandar Indera Mahkota, 25200 Kuantan, Pahang (Malaysia)
2015-05-15
Let (X,F) be a measurable space, and S(X,F) be the set of all probability measures on (X,F) where X is a state space and F is σ - algebraon X. We consider a nonlinear transformation (quadratic stochastic operator) defined by (Vλ)(A) = ∫{sub X}∫{sub X}P(x,y,A)dλ(x)dλ(y), where P(x, y, A) is regarded as a function of two variables x and y with fixed A ∈ F . A quadratic stochastic operator V is called a regular, if for any initial measure the strong limit lim{sub n→∞} V{sup n }(λ) is exists. In this paper, we construct a family of quadratic stochastic operators defined on the segment X = [0,1] with Borel σ - algebra F on X , prove their regularity and show that the limit measure is a Dirac measure.
Entangled Bloch Spheres: Bloch Matrix And Two Qubit State Space
Gamel, Omar
2016-01-01
We represent a two qubit density matrix in the basis of Pauli matrix tensor products, with the coefficients constituting a Bloch matrix, analogous to the single qubit Bloch vector. We find the quantum state positivity requirements on the Bloch matrix components, leading to three important inequalities, allowing us to parameterize and visualize the two qubit state space. Applying the singular value decomposition naturally separates the degrees of freedom to local and nonlocal, and simplifies the positivity inequalities. It also allows us to geometrically represent a state as two entangled Bloch spheres with superimposed correlation axes. It is shown that unitary transformations, local or nonlocal, have simple interpretations as axis rotations or mixing of certain degrees of freedom. The nonlocal unitary invariants of the state are then derived in terms of local unitary invariants. The positive partial transpose criterion for entanglement is generalized, and interpreted as a reflection, or a change of a single ...
State Space Path Integrals for Electronically Nonadiabatic Reaction Rates
Duke, Jessica Ryan
2016-01-01
We present a state-space-based path integral method to calculate the rate of electron transfer (ET) in multi-state, multi-electron condensed-phase processes. We employ an exact path integral in discrete electronic states and continuous Cartesian nuclear variables to obtain a transition state theory (TST) estimate to the rate. A dynamic recrossing correction to the TST rate is then obtained from real-time dynamics simulations using mean field ring polymer molecular dynamics. We employ two different reaction coordinates in our simulations and show that, despite the use of mean field dynamics, the use of an accurate dividing surface to compute TST rates allows us to achieve remarkable agreement with Fermi's golden rule rates for nonadiabatic ET in the normal regime of Marcus theory. Further, we show that using a reaction coordinate based on electronic state populations allows us to capture the turnover in rates for ET in the Marcus inverted regime.
Some Modal Relations and Generalized Velocity Method in State Space
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Real mode theory in configuration space has shown that the mode acceleration method converges faster than the mode displacement method. This paper demonstrates a similar conclusion in the state space. Some new expressions on modal parameter matrices were set up first. A generalized velocity method (GVM) is then demonstrated in a systematic way. This method is the so-called complex mode velocity method, but the expressions and schemes are given in terms of parametric matrices in configuration space. Theoretical comparison of this GVM with the traditional complex mode method shows some interesting conclusions. The latter approach is actually a generalized displacement method (GDM). Without mode reduction, the displacement responses of the concerned system resulting from both approaches are identical. On the other hand, both approaches have to adopt mode reduction to become practical. Under this situation, GVM has advantages because it compensates for the contribution of the omitted high-order modes to the displacement responses.
Practical Application of Neural Networks in State Space Control
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon
In the present thesis we address some problems in discrete-time state space control of nonlinear dynamical systems and attempt to solve them using generic nonlinear models based on artificial neural networks. The main aim of the work is to examine how well such control algorithms perform when...... applied to a realistic process. The thesis therefore strives to provide a thorough treatment of two classes of neural network-based controllers, and to make a rigorous comparison between them and a classical linear controller. Thus, the thesis starts out with a short review of some relevant system...... theoretic notions followed by a detailed description of the topology, neuron functions and learning rules of the two types of neural networks treated in the thesis, the multilayer perceptron and the neurofuzzy networks. In both cases, a Least Squares second-order gradient method is used to train...
The structures of state space concerning Quantum Dynamical Semigroups
Baumgartner, Bernhard
2011-01-01
Each semigroup describing the time evolution of an open quantum system on a finite dimensional Hilbert space is related to a special structure of this space. It is shown how the space can be decomposed into subspaces: One is related to decay, orthogonal subspaces support the stationary states. Specialities where the complete positivity of evolutions is actually needed for analysis, mainly for evolution of coherence, are highlighted. Decompositions are done the same way for evolutions in discrete as in continuous time, but evolutions may show differences, only for discrete semigroups there may appear cases of sudden decay and of perpetual oscillation. Concluding the analysis we identify the relation of the state space structure to the processes of Decay, Decoherence, Dissipation and Dephasing.
Noise in oscillators: a review of state space decomposition approaches
Traversa, Fabio L; Corinto, Fernando; Bonani, Fabrizio
2014-01-01
We review the state space decomposition techniques for the assessment of the noise properties of autonomous oscillators, a topic of great practical and theoretical importance for many applications in many different fields, from electronics, to optics, to biology. After presenting a rigorous definition of phase, given in terms of the autonomous system isochrons, we provide a generalized projection technique that allows to decompose the oscillator fluctuations in terms of phase and amplitude noise, pointing out that the very definition of phase (and orbital) deviations depends of the base chosen to define the aforementioned projection. After reviewing the most advanced theories for phase noise, based on the use of the Floquet basis and of the reduction of the projected model by neglecting the orbital fluctuations, we discuss the intricacies of the phase reduction process pointing out the presence of possible variations of the noisy oscillator frequency due to amplitude-related effects.
Rapid State Space Modeling Tool for Rectangular Wing Aeroservoelastic Studies
Suh, Peter M.; Conyers, Howard Jason; Mavris, Dimitri N.
2015-01-01
This report introduces a modeling and simulation tool for aeroservoelastic analysis of rectangular wings with trailing-edge control surfaces. The inputs to the code are planform design parameters such as wing span, aspect ratio, and number of control surfaces. Using this information, the generalized forces are computed using the doublet-lattice method. Using Roger's approximation, a rational function approximation is computed. The output, computed in a few seconds, is a state space aeroservoelastic model which can be used for analysis and control design. The tool is fully parameterized with default information so there is little required interaction with the model developer. All parameters can be easily modified if desired. The focus of this report is on tool presentation, verification, and validation. These processes are carried out in stages throughout the report. The rational function approximation is verified against computed generalized forces for a plate model. A model composed of finite element plates is compared to a modal analysis from commercial software and an independently conducted experimental ground vibration test analysis. Aeroservoelastic analysis is the ultimate goal of this tool, therefore, the flutter speed and frequency for a clamped plate are computed using damping-versus-velocity and frequency-versus-velocity analysis. The computational results are compared to a previously published computational analysis and wind-tunnel results for the same structure. A case study of a generic wing model with a single control surface is presented. Verification of the state space model is presented in comparison to damping-versus-velocity and frequency-versus-velocity analysis, including the analysis of the model in response to a 1-cos gust.
一种神经网络直接自校正PID控制器%A Type of Directly Self-tuning PID Controller Based on Neural Network
Institute of Scientific and Technical Information of China (English)
韩冲
2015-01-01
A new type of directly self-tuning PID controller based on neural network is proposed in this paper. its main characteristic is that it no longer includes independent PID controller and put neural network and the law of PID controller together. Showing the study algorithm of this neural network controller and analyzing the stability of this control system. The simulated results prove that this kind of control system is more adaptive and robust.%该文提出了一种神经网络直接自校正PID控制器。其主要特点是，在控制结构上不再包含独立的PID控制器，而是将神经网络和PID控制规律融为一体。并给出了这种神经网络控制器的学习算法和控制系统的稳定性分析。仿真结果表明，该控制系统具有较强的适应性和鲁棒性。
Directory of Open Access Journals (Sweden)
N.Ramesh Raju
2015-10-01
Full Text Available PID controller is mostly used in process plants to control the system performance by properly choosing its parameters. The optimum PID parameters can be obtained in offline using genetic algorithm if the mathematical model of the system is exactly known. In all process plants the process parameters such as properties of materials like thermal conductivity, electrical conductivity, physical dimensions such as diameter, length of the pipes, parameters of valves and pumps will change as time runs. This happens due to corrosion, scaling, aging, repairs during the maintenance, wear and tear. When the system is robust these changes slightly affect the performance of the system. When the system is not robust they make the system performance worst. Due to above reasons the process plant parameters changes as time runs. It is not easy to measure the changes in system parameters while plant is running and could not be evaluated optimum PID parameters through mathematical model. In this paper a new approach using genetic algorithm and neural network is established for optimum self tuning of PID parameters by observing the time response of the system at any time while plant is running.
Directory of Open Access Journals (Sweden)
Dedid Cahya Happyanto
2012-05-01
Full Text Available Driving system of electric car for low speed has a performance of controller that is not easily set up on large span so it does not give a comfort to passengers. The study has been tested in the bumpy road conditions, by providing disturbances in the motor load, it is to describe the condition of the road. To improve the system performance, the speed and torque controller was applied using Field Oriented Control (FOC method. In this method, On-Line Proportional Integral Derivative Fuzzy Logic Controller (PID-FLC is used to give dynamic response to the change of speed and maximum torque on the electric car and this results the smooth movement on every change of car performance both in fast and slow movement when breaking action is taken. Optimization of membership functions in Fuzzy PID controller is required to obtain a new PID parameter values which is done in autotuning in any changes of the input or disturbance. PID parameter tuning in this case using the Ziegler-Nichols method based on frequency response. The mechanism is done by adjusting the PID parameters and the strengthening of the system output. The test results show that the controller Fuzzy Self-Tuning PID appropriate for Electric cars because they have a good response about 0.85% overshoot at to changes in speed and braking of electric cars.
A Bayesian state-space formulation of dynamic occupancy models.
Royle, J Andrew; Kéry, Marc
2007-07-01
Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by non-detection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and WinBUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site
A Bayesian state-space formulation of dynamic occupancy models
Royle, J. Andrew; Kery, M.
2007-01-01
Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by nondetection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and Win BUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site
Interactive state-space analysis of concurrent systems
Energy Technology Data Exchange (ETDEWEB)
Morgan, E.T.; Razouk, R.R.
1987-10-01
The introduction of concurrency into programs has added to the complexity of the software design process. This is most evident in the design of communications protocols where concurrency is inherent to the behavior of the system. The complexity exhibited by such software systems makes more evident the need for computer-aided tools for automatically analyzing behavior. The Distributed Systems project at UCI has been developing techniques and tools, based on Petri nets, which support the design and evaluation of concurrent software systems. Techniques based on constructing reachability graphs that represent projections and selections of complete state-spaces have been developed. This paper focuses attention on the computer-aided analysis of these graphs for the purpose of proving correctness of the modeled system. The application of the analysis technique to evaluating simulation results for correctness is discussed. The tool which supports this analysis (the reachability graph analyzer, RGA) is also described. This tool provides mechanisms for proving general system properties (e.g., deadlock-freeness) as well as system-specific properties. The tool is sufficiently general to allow a user to apply complex user-defined analysis algorithms to reachability graphs. The alternating-bit protocol, with a bounded channel, is used to demonstrate the power of the tool and to point to future extensions.
Modeling diurnal hormone profiles by hierarchical state space models.
Liu, Ziyue; Guo, Wensheng
2015-10-30
Adrenocorticotropic hormone (ACTH) diurnal patterns contain both smooth circadian rhythms and pulsatile activities. How to evaluate and compare them between different groups is a challenging statistical task. In particular, we are interested in testing (1) whether the smooth ACTH circadian rhythms in chronic fatigue syndrome and fibromyalgia patients differ from those in healthy controls and (2) whether the patterns of pulsatile activities are different. In this paper, a hierarchical state space model is proposed to extract these signals from noisy observations. The smooth circadian rhythms shared by a group of subjects are modeled by periodic smoothing splines. The subject level pulsatile activities are modeled by autoregressive processes. A functional random effect is adopted at the pair level to account for the matched pair design. Parameters are estimated by maximizing the marginal likelihood. Signals are extracted as posterior means. Computationally efficient Kalman filter algorithms are adopted for implementation. Application of the proposed model reveals that the smooth circadian rhythms are similar in the two groups but the pulsatile activities in patients are weaker than those in the healthy controls. Copyright © 2015 John Wiley & Sons, Ltd.
A Knowledge Discovery from POS Data using State Space Models
Sato, Tadahiko; Higuchi, Tomoyuki
The number of competing-brands changes by new product's entry. The new product introduction is endemic among consumer packaged goods firm and is an integral component of their marketing strategy. As a new product's entry affects markets, there is a pressing need to develop market response model that can adapt to such changes. In this paper, we develop a dynamic model that capture the underlying evolution of the buying behavior associated with the new product. This extends an application of a dynamic linear model, which is used by a number of time series analyses, by allowing the observed dimension to change at some point in time. Our model copes with a problem that dynamic environments entail: changes in parameter over time and changes in the observed dimension. We formulate the model with framework of a state space model. We realize an estimation of the model using modified Kalman filter/fixed interval smoother. We find that new product's entry (1) decreases brand differentiation for existing brands, as indicated by decreasing difference between cross-price elasticities; (2) decreases commodity power for existing brands, as indicated by decreasing trend; and (3) decreases the effect of discount for existing brands, as indicated by a decrease in the magnitude of own-brand price elasticities. The proposed framework is directly applicable to other fields in which the observed dimension might be change, such as economic, bioinformatics, and so forth.
Nonlinear State Space Modeling and System Identification for Electrohydraulic Control
Directory of Open Access Journals (Sweden)
Jun Yan
2013-01-01
Full Text Available The paper deals with nonlinear modeling and identification of an electrohydraulic control system for improving its tracking performance. We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a linear time-invariant dynamic block, and a static output nonlinear block with single polynomial nonlinearity to describe it. We simplify the H-W model into a linear-in-parameters structure by using the key term separation principle and then use a modified recursive least square method with iterative estimation of internal variables to identify all the unknown parameters simultaneously. It is found that the proposed H-W model approximates the actual system better than the independent Hammerstein, Wiener, and ARX models. The prediction error of the H-W model is about 13%, 54%, and 58% less than the Hammerstein, Wiener, and ARX models, respectively.
Analysis of Life Histories: A State Space Approach
Directory of Open Access Journals (Sweden)
Rajulton, Fernando
2001-01-01
Full Text Available EnglishThe computer package LIFEHIST written by the author, is meant for analyzinglife histories through a state-space approach. Basic ideas on which the various programs have beenbuilt are described in this paper in a non-mathematical language. Users can use various programs formultistate analyses based on Markov and semi-Markov frameworks and sequences of transitions implied inlife histories. The package is under constant revision and programs for using a few specific modelsthe author thinks will be useful for analyzing longitudinal data will be incorporated in the nearfuture.FrenchLe système d'ordinateur LIFEHIST écrit par l'auteur est établi pour analyser desévénements au cours de la vie par une approche qui tient compte des états aucours du temps. Les idées fondamentales à la base des divers programmes dumodule sont décrites dans un langage non-mathématique. Le systèmeLIFEHIST peut être utilisé pour des analyses Markov et semi-Markov desséquences d’événements au cours de la vie. Le module est sous révisionconstante, et des programmes que l’auteur compte ajouter pour l'usage dedonnées longitudinales sont décrit.
Iterative feedback tuning of uncertain state space systems
Directory of Open Access Journals (Sweden)
J. K. Huusom
2010-09-01
Full Text Available Iterative Feedback Tuning is a purely data driven tuning algorithm for optimizing control parameters based on closed loop data. The algorithm is designed to produce an unbiased estimate of the performance cost function gradient for iteratively improving the control parameters to achieve optimal loop performance. This tuning method has been developed for systems based on a transfer function representation. This paper presents a state feedback control system with a state observer and its transfer function equivalent in terms of input output dynamics. It is shown how the parameters in the closed loop state space system can be tuned by Iterative Feedback Tuning utilizing this equivalent representation. A simulation example illustrates that the tuning converges to the known analytical solution for the feedback control gain and to the Kalman gain in the state observer. In case of parametric uncertainty, different choices of tuning parameters are investigated. It is shown that the data driven tuning method produces optimal performance for convex problems when it is the model parameter estimates in the observer that are tuned.
Projective limits of state spaces II. Quantum formalism
Lanéry, Suzanne; Thiemann, Thomas
2017-06-01
In this series of papers, we investigate the projective framework initiated by Kijowski (1977) and Okołów (2009, 2014, 2013), which describes the states of a quantum theory as projective families of density matrices. A short reading guide to the series can be found in Lanéry (2016). After discussing the formalism at the classical level in a first paper (Lanéry, 2017), the present second paper is devoted to the quantum theory. In particular, we inspect in detail how such quantum projective state spaces relate to inductive limit Hilbert spaces and to infinite tensor product constructions (Lanéry, 2016, subsection 3.1) [1]. Regarding the quantization of classical projective structures into quantum ones, we extend the results by Okołów (2013), that were set up in the context of linear configuration spaces, to configuration spaces given by simply-connected Lie groups, and to holomorphic quantization of complex phase spaces (Lanéry, 2016, subsection 2.2) [1].
Geometry of state space in plane Couette flow
Cvitanović, P.; Gibson, J. F.
A large conceptual gap separates the theory of low-dimensional chaotic dynamics from the infinite-dimensional nonlinear dynamics of turbulence. Recent advances in experimental imaging, computational methods, and dynamical systems theory suggest a way to bridge this gap in our understanding of turbulence. Recent discoveries show that recurrent coherent structures observed in wall-bounded shear flows (such as pipes and plane Couette flow) result from close passes to weakly unstable invariant solutions of the Navier-Stokes equations. These 3D, fully nonlinear solutions (equilibria, traveling waves, and periodic orbits) structure the state space of turbulent flows and provide a skeleton for analyzing their dynamics. We calculate a hierarchy of invariant solutions for plane Couette, a canonical wall-bounded shear flow. These solutions reveal organization in the flow's turbulent dynamics and can be used to predict directly from the fundamental equations physical quantities such as bulk flow rate and mean wall drag. All results and the code that generates them are disseminated through through our group's open-source CFD software and solution database Channelflow.org and the collaborative e-book ChaosBook.org.
Forecasting seasonal influenza with a state-space SIR model.
Osthus, Dave; Hickmann, Kyle S; Caragea, Petruţa C; Higdon, Dave; Del Valle, Sara Y
2017-03-01
Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the Centers for Disease Control and Prevention's influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts. Despite the relatively rich surveillance data and the recurrent nature of seasonal influenza, forecasting the timing and intensity of seasonal influenza in the U.S. remains challenging because the form of the disease transmission process is uncertain, the disease dynamics are only partially observed, and the public health observations are noisy. Fitting a probabilistic state-space model motivated by a deterministic mathematical model [a susceptible-infectious-recovered (SIR) model] is a promising approach for forecasting seasonal influenza while simultaneously accounting for multiple sources of uncertainty. A significant finding of this work is the importance of thoughtfully specifying the prior, as results critically depend on its specification. Our conditionally specified prior allows us to exploit known relationships between latent SIR initial conditions and parameters and functions of surveillance data. We demonstrate advantages of our approach relative to alternatives via a forecasting comparison using several forecast accuracy metrics.
Rank functions and state spaces of K0
Institute of Scientific and Technical Information of China (English)
Feng; Lianggui
2001-01-01
［1］Cohn, P.M., Rank functions on rings, J. Alg., 1990, 133: 373.［2］Goodearl, K.R., Von Neumann Regular Rings, London: Pitman, 1979.［3］Bass, H., Algebraic K-theory, New York: Benjamin, 1968.［4］Silvester, J. R., Introduction to Algebraic K-Theory, London: Chapman and Hall, 1981.［5］Zhu, X. S., Tong, W. T., The category of power stably free modules and its K0 group, Science in China (in Chinese), Ser. A, 1997, 27(9): 812.［6］Cohn, P.M., Schofield, A. H., On the law of nullity, Math. Proc. Cambridge Philos. Soc., 1982, 91: 357.［7］Cohn, P. M., An invariant characterization of pseudo-valuations on a field, Proc. Cambridge Philos Soc., 1954, 50: 159.［8］Goodearl, K. R., State space of K0 of noetherian rings, J. Alg., 1981, 71: 322.［9］Goodearl, K. R., K-theoretically simple Von Neumann regular rings, J. Alg., 1995, 174: 659.［10］Kaplansky, I., Projective modules, Math. Ann., 1958, 68: 372.［11］Tong, W. T., PF-rings and the Grothendieck groups of groups rings, J. of Math. Res. and Exposition(in Chinese), 1990, 10(2): 157.
Hierarchical state-space estimation of leatherback turtle navigation ability.
Mills Flemming, Joanna; Jonsen, Ian D; Myers, Ransom A; Field, Christopher A
2010-12-28
Remotely sensed tracking technology has revealed remarkable migration patterns that were previously unknown; however, models to optimally use such data have developed more slowly. Here, we present a hierarchical Bayes state-space framework that allows us to combine tracking data from a collection of animals and make inferences at both individual and broader levels. We formulate models that allow the navigation ability of animals to be estimated and demonstrate how information can be combined over many animals to allow improved estimation. We also show how formal hypothesis testing regarding navigation ability can easily be accomplished in this framework. Using Argos satellite tracking data from 14 leatherback turtles, 7 males and 7 females, during their southward migration from Nova Scotia, Canada, we find that the circle of confusion (the radius around an animal's location within which it is unable to determine its location precisely) is approximately 96 km. This estimate suggests that the turtles' navigation does not need to be highly accurate, especially if they are able to use more reliable cues as they near their destination. Moreover, for the 14 turtles examined, there is little evidence to suggest that male and female navigation abilities differ. Because of the minimal assumptions made about the movement process, our approach can be used to estimate and compare navigation ability for many migratory species that are able to carry electronic tracking devices.
基于模糊自整定PID控制方法的雷达伺服系统%Design of Radar Servo System Based on Fuzzy Self-tuning PID Control
Institute of Scientific and Technical Information of China (English)
赵爽; 邓先荣
2012-01-01
传统PID控制器因结构简单、易于实现而在控制系统中得到了广泛运用,但往往因实际系统中的非线性因素影响而不得不采用变结构、变参数等手段来提高实际控制效果,而模糊控制对非线性因素的影响却能明显改善系统控制品质.文中主要研究了模糊自整定PID控制方法在雷达伺服系统中的应用,并在实际系统中进行了实验验证.仿真结果表明该方法能明显提高系统控制品质,具有一定的工程推广价值.%Although PID control is widely used, it is linear at working point. It can't ensure dynamic quality when away from working point, while fuzzy self-tuning PID control can. In this paper, we research on application of fuzzy self-tuning pid control to radar servo system. Firstly we study the fuzzy self-tuning PID ontrol and then we verified in a practical servo drive system model. It proved that the fuzzy self-tuning pid control can be applied to the radar servo system with precision and flexibility.
State-space reduction and equivalence class sampling for a molecular self-assembly model.
Packwood, Daniel M; Han, Patrick; Hitosugi, Taro
2016-07-01
Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving 'target information' from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of a Markov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.
Self-tuning PID Parameters by Using Artificial Bee Colony Algorithm%人工蜂群算法整定PID控制器参数
Institute of Scientific and Technical Information of China (English)
蔡超; 周武能
2015-01-01
针对工业控制中常用的PID控制器参数整定困难的问题，提出一种基于人工蜂群算法的参数整定方法。将PID控制器待整定参数看作蜜源，利用蜂群特有的角色转变机制搜索优质的参数组合；选取绝对误差矩积分性能指标作为参数寻优的目标函数。仿真实验结果表明，所采用的算法能够提高控制系统的动态性能，增强系统的快速性和稳定性，适用于PID 控制器的自整定。%Aiming at the difficult problems in parameter tuning of PID controllers in industrial control, the parameter tuning method based on artificial bee colony algorithm is proposed. In this algorithm, the parameter of PID controller need to be tuned is seen as the nectar source;the high-quality combination of parameters is searched using the unique role change mechanism of the bees;and the ITAE index is selected as the objective function for parameter optimization. The simulation experiments show that this algorithm can enhance dynamic performance of the control system, and strengthen the speediness and stability of the system, it’ s suitable for PID controller self-tuning.
Muniandy, V.; Samin, P. M.; Jamaluddin, H.
2015-11-01
A fuzzy proportional-integral-derivative (PID) controller has not been widely investigated for active anti-roll bar (AARB) application due to its unspecific mathematical analysis and the derivative kick problem. This paper briefly explains how the derivative kick problem arises due to the nature of the PID controller as well as the conventional fuzzy PID controller in association with an AARB. There are two types of controllers proposed in this paper: self-tuning fuzzy proportional-integral-proportional-derivative (STF PI-PD) and PI-PD-type fuzzy controller. Literature reveals that the PI-PD configuration can avoid the derivative kick, unlike the standard PID configuration used in fuzzy PID controllers. STF PI-PD is a new controller proposed and presented in this paper, while the PI-PD-type fuzzy controller was developed by other researchers for robotics and automation applications. Some modifications were made on these controllers in order to make them work with an AARB system. The performances of these controllers were evaluated through a series of handling tests using a full car model simulated in MATLAB Simulink. The simulation results were compared with the performance of a passive anti-roll bar and the conventional fuzzy PID controller in order to show improvements and practicality of the proposed controllers. Roll angle signal was used as input for all the controllers. It is found that the STF PI-PD controller is able to suppress the derivative kick problem but could not reduce the roll motion as much as the conventional fuzzy PID would. However, the PI-PD-type fuzzy controller outperforms the rest by improving ride and handling of a simulated passenger car significantly.
Development of Self-tuning Belt Scale Weighting Device%自整定式皮带秤称重装置
Institute of Scientific and Technical Information of China (English)
席建中; 韩成春; 乔淑云
2012-01-01
To develop a core technology with independent intellectual property rights in the self-tuning belt scale weighing device , this paper used the structural design of the cylinder piston and hydraulic oil control to increase the flow of liquid travel through the fluid flow damping and pressure drop,to absorbs vibration energy and to achieve the purpose of reducing vibration. This device is balanced by energy transfer to stay frame of the stress state, and through a variety of vibration energy absorber damping fluid flow is converted to heat release,reducing vibration generated weighing errors,and improves the measurement accuracy of electronic belt scale.%开发了一种在核心技术上具有自主知识产权的自整定式皮带秤称重装置,利用对油缸活塞的结构设计和液压油路控制增加液体流动的行程,通过液体流动阻尼和压力降,以此吸收振动能量,达到减振的目的.该装置是通过能量的传递来均衡称架的受力状态,将各种振动能通过能量吸收器的流体流动阻尼转换为热量释放,降低振动而产生的称量误差、提高电子皮带秤的计量精度.
An Approach to Distributed State Space Exploration for Coloured Petri Nets
DEFF Research Database (Denmark)
Kristensen, Lars Michael; Petrucci, Laure
2004-01-01
We present an approach and associated computer tool support for conducting distributed state space exploration for Coloured Petri Nets (CPNs). The distributed state space exploration is based on the introduction of a coordinating process and a number of worker processes. The worker processes...... Tools. This makes the distributed state space exploration and analysis largely transparent to the analyst. We illustrate the use of the developed tool on an example....
Formulating state space models in R with focus on longitudinal regression models
DEFF Research Database (Denmark)
Dethlefsen, Claus; Lundbye-Christensen, Søren
We provide a language for formulating a range of state space models. The described methodology is implemented in the R -package sspir available from cran.r-project.org . A state space model is specified similarly to a generalized linear model in R , by marking the time-varying terms in the form...... We provide a language for formulating a range of state space models. The described methodology is implemented in the R -package sspir available from cran.r-project.org . A state space model is specified similarly to a generalized linear model in R , by marking the time-varying terms...
Quantum-Dot Semiconductor Optical Amplifiers: State Space Model versus Rate Equation Model
Directory of Open Access Journals (Sweden)
Hussein Taleb
2013-01-01
Full Text Available A simple and accurate dynamic model for QD-SOAs is proposed. The proposed model is based on the state space theory, where by eliminating the distance dependence of the rate equation model of the QD-SOA; we derive a state space model for the device. A comparison is made between the rate equation model and the state space model under both steady state and transient regimes. Simulation results demonstrate that the derived state space model not only is much simpler and faster than the rate equation model, but also it is as accurate as the rate equation model.
Fuzzy Self-tuning PID Variable Spray Control System Based on PLC Control%基于PLC控制的模糊自整定PID变量喷雾控制系统
Institute of Scientific and Technical Information of China (English)
董志明; 宋乐鹏
2014-01-01
变量喷雾控制系统具有非线性、时变性、大滞后等特点，常规PID控制不能满足变量喷雾控制系统在实际作业中理想的控制效果。因此提出了一种基于PLC控制的模糊自整定PID控制方法。PLC控制的模糊自整定PID控制结合了PLC控制灵活、多变和自适应模糊控制等特点，通过对变量喷雾控制系统的数学建模，建立了以电动PI调节阀为核心的模糊自整定PID控制系统。利用Matlab/Simulink 和模糊逻辑工具箱对普通模糊PID控制系统和基于PLC控制的模糊自整定PID控制系统进行Simulink仿真研究。实验结果表明，基于PLC控制的模糊自整定PID控制比常规PID控制在非线性、时变性、减小超调量的方面具有更好的控制品质。%Variable spray control system has nonlinear, time-varying, big lag, etc. Conventional PID control can't satisfy the variable spray control system in the actual operation of the ideal control effect, so as to put forward a fuzzy self-tuning PID control based on PLC control method. PLC control of the fuzzy self-tuning PID control combined with PLC control flexible, changeable and adaptive fuzzy control, etc. through the mathematical modeling of variable spray control system, set up electric PI regulator for the core of the fuzzy self-tuning PID control system. Using Matlab/Simulink and fuzzy logic toolbox to general fuzzy PID control system and fuzzy self-tuning PID control based on PLC control system with Simulink simulation. The experimental results showed that fuzzy self-tuning PID control based on PLC control had the better quality than the conventional PID control in the nonlinear, time-varying and the reduce of the overshoot amount.
The state-space approach to the method of adjoints for hybrid guidance loop models
Weiss, M.; Bucco, D.
2009-01-01
A framework is introduced to develop the theory of the Adjoint Method for models including both continuous and discrete dynamics. The basis of this framework consists of the class of impulsive linear dynamical systems. It allows extension of the Adjoint Method to more general models that include mul
Non-Parametric Bayesian State Space Estimator for Negative Information
Directory of Open Access Journals (Sweden)
Guillaume de Chambrier
2017-09-01
Full Text Available Simultaneous Localization and Mapping (SLAM is concerned with the development of filters to accurately and efficiently infer the state parameters (position, orientation, etc. of an agent and aspects of its environment, commonly referred to as the map. A mapping system is necessary for the agent to achieve situatedness, which is a precondition for planning and reasoning. In this work, we consider an agent who is given the task of finding a set of objects. The agent has limited perception and can only sense the presence of objects if a direct contact is made, as a result most of the sensing is negative information. In the absence of recurrent sightings or direct measurements of objects, there are no correlations from the measurement errors that can be exploited. This renders SLAM estimators, for which this fact is their backbone such as EKF-SLAM, ineffective. In addition for our setting, no assumptions are taken with respect to the marginals (beliefs of both the agent and objects (map. From the loose assumptions we stipulate regarding the marginals and measurements, we adopt a histogram parametrization. We introduce a Bayesian State Space Estimator (BSSE, which we name Measurement Likelihood Memory Filter (MLMF, in which the values of the joint distribution are not parametrized but instead we directly apply changes from the measurement integration step to the marginals. This is achieved by keeping track of the history of likelihood functions’ parameters. We demonstrate that the MLMF gives the same filtered marginals as a histogram filter and show two implementations: MLMF and scalable-MLMF that both have a linear space complexity. The original MLMF retains an exponential time complexity (although an order of magnitude smaller than the histogram filter while the scalable-MLMF introduced independence assumption such to have a linear time complexity. We further quantitatively demonstrate the scalability of our algorithm with 25 beliefs having up to
Transformation of Neural State Space Models into LFT Models for Robust Control Design
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2000-01-01
This paper considers the extraction of linear state space models and uncertainty models from neural networks trained as state estimators with direct application to robust control. A new method for writing a neural state space model in a linear fractional transformation form in a non...
Gu, Fei; Preacher, Kristopher J; Wu, Wei; Yung, Yiu-Fai
2014-01-01
Although the state space approach for estimating multilevel regression models has been well established for decades in the time series literature, it does not receive much attention from educational and psychological researchers. In this article, we (a) introduce the state space approach for estimating multilevel regression models and (b) extend the state space approach for estimating multilevel factor models. A brief outline of the state space formulation is provided and then state space forms for univariate and multivariate multilevel regression models, and a multilevel confirmatory factor model, are illustrated. The utility of the state space approach is demonstrated with either a simulated or real example for each multilevel model. It is concluded that the results from the state space approach are essentially identical to those from specialized multilevel regression modeling and structural equation modeling software. More importantly, the state space approach offers researchers a computationally more efficient alternative to fit multilevel regression models with a large number of Level 1 units within each Level 2 unit or a large number of observations on each subject in a longitudinal study.
A fast, reliable algorithm for computing frequency responses of state space models
Wette, Matt
1991-01-01
Computation of frequency responses for large order systems described by time invariant state space systems often provides a bottleneck in control system analysis. It is shown that banding the A-matrix in the state space model can effectively reduce the computation time for such systems while maintaining reliability in the results produced.
State-Space Modeling, System Identification and Control of a 4th Order Rotational Mechanical System
2009-12-01
state-space form. Identification of the state-space parameters was accomplished using the parameter estimation function in Matlab’s System ... Identification Toolbox utilizing experimental input/output data. The identified model was then constructed in Simulink and the accuracy of the identified model
Exponential estimation of generalized state-space time-delay systems
Energy Technology Data Exchange (ETDEWEB)
Lien, C-H; Yu, K-W [Department of Marine Engineering, National Kaohsiung Marine University, Taiwan 811 (China); Lin, J-S; Hung, M-L [Department of Electrical Engineering, Far East University, Tainan, Taiwan 744 (China)], E-mail: chlien@mail.nkmu.edu.tw
2008-02-15
In this paper, global exponential stability for a class of generalized state-space time-delay systems is considered. Delay-dependent criteria are proposed to guarantee the exponential stability and estimate the convergence rate for the generalized state-space systems with two cases of uncertainties. Finally, some numerical examples are illustrated to show the usefulness of the theory.
Formulating state space models in R with focus on longitudinal regression models
DEFF Research Database (Denmark)
Dethlefsen, Claus; Lundbye-Christensen, Søren
2006-01-01
We provide a language for formulating a range of state space models with response densities within the exponential family. The described methodology is implemented in the R-package sspir. A state space model is specified similarly to a generalized linear model in R, and then the time-varying terms...
Parallel symbolic state-space exploration is difficult, but what is the alternative?
Ciardo, Gianfranco; Jin, Xiaoqing; 10.4204/EPTCS.14.1
2009-01-01
State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is...
基于数据驱动的PID自整定方法的研究%Research on PID parameter self-tuning based on data-driven control
Institute of Scientific and Technical Information of China (English)
房耀; 董学平; 张薛礼
2016-01-01
The traditional PID parameter tuning depends on mathematical model ,but also needs a large number of engineering experiments to ensure parameter ,which has limited the practical application of PID severely .In this paper ,an online PID parameter self-tuning method is proposed based on data-driven control ,which has the advantages of strong adaptability ,anti-interference ,and reliability ,and can solve the problem of the dependence of model and realize the online PID parameter self-tuning in discrete nonlinear system .And the feasibility and effectiveness of the proposed PID parameter self-tuning algorithm based on data-driven control are verified by comparative simulation tests .%传统的PID参数整定依赖于数学模型 ,通过大量工程实验来确定 ,这给 PID 的实际应用带来很大的局限性 . 文章基于数据驱动理论 ,提出一种 PID 参数在线自整定方法 ,适用于非线性离散系统 ,该方法无需系统模型的相关信息 ,且具有 PID 方法的适应性 、抗干扰性 、可靠性 . 通过仿真比较试验结果 ,验证了这种自适应数据驱动 PID 参数整定算法的可行性和有效性 .
Institute of Scientific and Technical Information of China (English)
孙玉新; 闫思江; 李凡国
2015-01-01
针对铸造用热处理炉温度的动态响应延迟和滞后的特点，本文基于综合式自校正PID原理，利用自校正PID控制器对热处理炉温控系统进行串级控制再设计，并设计了二级调节器的串级温度控制系统。结果表明，综合式自校正PID串级温度控制系统可以有效地提高热处理炉温度控制系统的稳定性并改善了系统的鲁棒性，优于传统的PID控制方法。%Aimed at the characteristics of the delay and lag of the dynamic response of the temperature reheat thermal power plant, based on the integrated self-tuning PID principle, this paper used the integrated self-tuning PID controller to redesign the cascade control of the temperature control system of the reheater for the thermal power plant. W The results show that the temperature control system for the reheater fo the thermal power plant using the integrated self-tuning PID can effectively improve the stability of the reheater temperature control system and improve the robustness of the temperature control system, and this control method is obviously superior to the traditional PID control method.
Gain Scheduling Control of Nonlinear Systems Based on Neural State Space Models
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Stoustrup, Jakob
2003-01-01
This paper presents a novel method for gain scheduling control of nonlinear systems based on extraction of local linear state space models from neural networks with direct application to robust control. A neural state space model of the system is first trained based on in- and output training...... samples from the system, after which linearized state space models are extracted from the neural network in a number of operating points according to a simple and computationally cheap scheme. Robust observer-based controllers can then be designed in each of these operating points,and gain scheduling...
A new adaptive state space construction method for the mobile robot navigation
Institute of Scientific and Technical Information of China (English)
Huang Bingqiang; Cao Guangyi; Fei Yanqiong; Li Jianhua
2008-01-01
In order to solve the combinative explosion problems in a continuous and high dimensional state space, a function approximation approach is usually used to represent the state space. The normalized radial basis function (NRBF) was adopted as the local function approximator and a kind of adaptive state space construction strategy based on the NRBF (ASC-NRBF) was proposed, which enables the system to allocate appropriate number and size of the basis functions automatically. Combined with the reinforcement learning method, the proposed ASC-NRBF method was applied to the robot navigation problem. Simulation results illustrate the performance of the proposed method.
Symbolic Algorithmic Analysis of Rectangular Hybrid Systems
Institute of Scientific and Technical Information of China (English)
Hai-Bin Zhang; Zhen-Hua Duan
2009-01-01
This paper investigates symbolic algorithmic analysis of rectangular hybrid systems. To deal with the symbolic reachability problem, a restricted constraint system called hybrid zone is formalized for the representation and manipulation of rectangular automata state-spaces. Hybrid zones are proved to be closed over symbolic reachability operations of rectangular hybrid systems. They are also applied to model-checking procedures for verifying some important classes of timed computation tree logic formulas. To represent hybrid zones, a data structure called difference constraint matrix is defined.These enable us to deal with the symbolic algorithmic analysis of rectangular hybrid systems in an efficient way.
Gu, Fei; Wu, Hao
2016-09-01
The specifications of state space model for some principal component-related models are described, including the independent-group common principal component (CPC) model, the dependent-group CPC model, and principal component-based multivariate analysis of variance. Some derivations are provided to show the equivalence of the state space approach and the existing Wishart-likelihood approach. For each model, a numeric example is used to illustrate the state space approach. In addition, a simulation study is conducted to evaluate the standard error estimates under the normality and nonnormality conditions. In order to cope with the nonnormality conditions, the robust standard errors are also computed. Finally, other possible applications of the state space approach are discussed at the end.
Transformation of Neural State Space Models into LFT Models for Robust Control Design
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2000-01-01
This paper considers the extraction of linear state space models and uncertainty models from neural networks trained as state estimators with direct application to robust control. A new method for writing a neural state space model in a linear fractional transformation form in a non-conservative ......This paper considers the extraction of linear state space models and uncertainty models from neural networks trained as state estimators with direct application to robust control. A new method for writing a neural state space model in a linear fractional transformation form in a non......-conservative way is proposed, and it is demonstrated how a standard robust control law can be designed for a system described by means of a multi layer perceptron....
Abellán-Nebot, J. V.; Liu, J.; Romero, F.
2009-11-01
The State Space modelling approach has been recently proposed as an engineering-driven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.
Monthly version of HadISST sea surface temperature state-space components
National Oceanic and Atmospheric Administration, Department of Commerce — State-Space Decomposition of Monthly version of HadISST sea surface temperature component (1-degree). See Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C....
State-Space Realization of the Wave-Radiation Force within FAST: Preprint
Energy Technology Data Exchange (ETDEWEB)
Duarte, T.; Sarmento, A.; Alves, M.; Jonkman, J.
2013-06-01
Several methods have been proposed in the literature to find a state-space model for the wave-radiation forces. In this paper, four methods were compared, two in the frequency domain and two in the time domain. The frequency-response function and the impulse response of the resulting state-space models were compared against the ones derived by the numerical code WAMIT. The implementation of the state-space module within the FAST offshore wind turbine computer-aided engineering (CAE) tool was verified, comparing the results against the previously implemented numerical convolution method. The results agreed between the two methods, with a significant reduction in required computational time when using the state-space module.
State-space models for bio-loggers: A methodological road map
DEFF Research Database (Denmark)
Jonsen, I.D.; Basson, M.; Bestley, S.
2012-01-01
development of state-space modelling approaches for animal movement data provides statistical rigor for inferring hidden behavioural states, relating these states to bio-physical data, and ultimately for predicting the potential impacts of climate change. Despite the widespread utility, and current popularity......-physical datasets to understand physiological and ecological influences on habitat selection. In most cases, however, the behavioural context is not directly observable and therefore, must be inferred. Animal movement data are complex in structure, entailing a need for stochastic analysis methods. The recent......, of state-space models for analysis of animal tracking data, these tools are not simple and require considerable care in their use. Here we develop a methodological “road map” for ecologists by reviewing currently available state-space implementations. We discuss appropriate use of state-space methods...
Making Faces - State-Space Models Applied to Multi-Modal Signal Processing
DEFF Research Database (Denmark)
Lehn-Schiøler, Tue
2005-01-01
The two main focus areas of this thesis are State-Space Models and multi modal signal processing. The general State-Space Model is investigated and an addition to the class of sequential sampling methods is proposed. This new algorithm is denoted as the Parzen Particle Filter. Furthermore, the Ma...... application an information theoretic vector quantizer is also proposed. Based on interactions between particles, it is shown how a quantizing scheme based on an analytic cost function can be derived....
A theory of state space reconstruction in the presence of noise
Energy Technology Data Exchange (ETDEWEB)
Casdagli, M.; Eubank, S.; Farmer, J.D.; Gibson, J. (Los Alamos National Lab., NM (USA) Santa Fe Inst., NM (USA))
1990-01-01
Takens' theorem demonstrates that in the absence of noise a multidimensional state space can be reconstructed from a single time series. This theorem does not treat the effect of noise, however, and so gives no guidance about practical considerations for reconstructing a good state space. We study the problem of reconstructing a state space with observational noise, examining the likelihood for a particular state given a series of noisy observations. We define a quantity called distortion, which is proportional to the covariance of the likelihood function in a reconstructed state space. This is related to the noise amplification, which corresponds to the root-mean-square errors for time series prediction with an ideal model. We prove that in the low noise limit minimizing the distortion is equivalent to minimizing the noise amplification. We derive several asymptotic scaling laws for distortion and noise amplification. They depend on properties of the state space reconstruction, such as the sampling time and the reconstruction dimension, and properties of the dynamical system, such as the dimension and Lyapunov exponents. When the dimension and Lyapunov exponents are sufficiently large these scaling laws show that, no matter how the state space is reconstructed, there is an explosion in the noise amplification -- from a practical point of view all determinism is lost, even for short times, so that the time series is effectively a random process. In the low noise, large data limit we show that the technique of local principal value decomposition (PVD) is an optimal method of state space reconstruction, in the sense that it achieves the minimum distortion in a state space of the lowest possible dimension. 20 refs., 12 figs.
Minimal state space realisation of continuous-time linear time-variant input-output models
Goos, J.; Pintelon, R.
2016-04-01
In the linear time-invariant (LTI) framework, the transformation from an input-output equation into state space representation is well understood. Several canonical forms exist that realise the same dynamic behaviour. If the coefficients become time-varying however, the LTI transformation no longer holds. We prove by induction that there exists a closed-form expression for the observability canonical state space model, using binomial coefficients.
Global exponential stability conditions for generalized state-space systems with time-varying delays
Energy Technology Data Exchange (ETDEWEB)
Yu, K.-W. [Department of Marine Engineering, National Kaohsiung Marine University, Kaohsiung 811, Taiwan (China)], E-mail: kwyu@mail.nkmu.edu.tw; Lien, C.-H. [Department of Marine Engineering, National Kaohsiung Marine University, Kaohsiung 811, Taiwan (China)], E-mail: chlien.ee@msa.hinet.net
2008-05-15
A unified approach is proposed to deal with the exponential stability for generalized state-space systems with time-varying delays. Many systems models can be regarded as special cases of the considered systems; such as neutral time-delay systems and delayed cellular neural networks. Delay-dependent stability criteria are proposed to guarantee the global exponential stability for generalized state-space systems with two cases of uncertainties. Two numerical examples are given to show the effectiveness of our method.
Mixed-Effects State Space Models for Analysis of Longitudinal Dynamic Systems
Liu, Dacheng; Lu, Tao; Niu, Xu-Feng; Wu, Hulin
2010-01-01
The rapid development of new biotechnologies allows us to deeply understand biomedical dynamic systems in more detail and at a cellular level. Many of the subject-specific biomedical systems can be described by a set of differential or difference equations which are similar to engineering dynamic systems. In this paper, motivated by HIV dynamic studies, we propose a class of mixed-effects state space models based on the longitudinal feature of dynamic systems. State space models with mixed-ef...
THE DECOMPOSITION OF STATE SPACE FOR MARKOV CHAIN IN RANDOM ENVIRONMENT
Institute of Scientific and Technical Information of China (English)
Hu Dihe
2005-01-01
This paper is a continuation of [8] and [9]. The author obtains the decomposition of state space χof an Markov chain in random environment by making use of the results in [8] and [9], gives three examples, random walk in random environment, renewal process in random environment and queue process in random environment, and obtains the decompositions of the state spaces of these three special examples.
Walrus Bayesian State-space Model Output from the Bering Sea and Chukchi Sea, 2008-2012
U.S. Geological Survey, Department of the Interior — State-space models offer researchers an objective approach to modeling complex animal location datasets, and state-space model behavior classifications are often...
Robustness and state-space structure of Boolean gene regulatory models.
Willadsen, Kai; Wiles, Janet
2007-12-21
Robustness to perturbation is an important characteristic of genetic regulatory systems, but the relationship between robustness and model dynamics has not been clearly quantified. We propose a method for quantifying both robustness and dynamics in terms of state-space structures, for Boolean models of genetic regulatory systems. By investigating existing models of the Drosophila melanogaster segment polarity network and the Saccharomyces cerevisiae cell-cycle network, we show that the structure of attractor basins can yield insight into the underlying decision making required of the system, and also the way in which the system maximises its robustness. In particular, gene networks implementing decisions based on a few genes have simple state-space structures, and their attractors are robust by virtue of their simplicity. Gene networks with decisions that involve many interacting genes have correspondingly more complicated state-space structures, and robustness cannot be achieved through the structure of the attractor basins, but is achieved by larger attractor basins that dominate the state space. These different types of robustness are demonstrated by the two models: the D. melanogaster segment polarity network is robust due to simple attractor basins that implement decisions based on spatial signals; the S. cerevisiae cell-cycle network has a complicated state-space structure, and is robust only due to a giant attractor basin that dominates the state space.
Directory of Open Access Journals (Sweden)
Nataliya Chukhrova
2017-05-01
Full Text Available This paper gives a detailed overview of the current state of research in relation to the use of state space models and the Kalman-filter in the field of stochastic claims reserving. Most of these state space representations are matrix-based, which complicates their applications. Therefore, to facilitate the implementation of state space models in practice, we present a scalar state space model for cumulative payments, which is an extension of the well-known chain ladder (CL method. The presented model is distribution-free, forms a basis for determining the entire unobservable lower and upper run-off triangles and can easily be applied in practice using the Kalman-filter for prediction, filtering and smoothing of cumulative payments. In addition, the model provides an easy way to find outliers in the data and to determine outlier effects. Finally, an empirical comparison of the scalar state space model, promising prior state space models and some popular stochastic claims reserving methods is performed.
Modeling and Control of a Photovoltaic Energy System Using the State-Space Averaging Technique
Directory of Open Access Journals (Sweden)
Mohd S. Jamri
2010-01-01
Full Text Available Problem statement: This study presented the modeling and control of a stand-alone Photovoltaic (PV system using the state-space averaging technique. Approach: The PV module was modeled based on the parameters obtained from a commercial PV data sheet while state-space method is used to model the power converter. A DC-DC boost converter was chosen to step up the input DC voltage of the PV module while the DC-AC single-phase full-bridge square-wave inverter was chosen to convert the input DC comes from boost converter into AC element. The integrated state-space model was simulated under a constant and a variable change of solar irradiance and temperature. In addition to that, maximum power point tracking method was also included in the model to ensure that optimum use of PV module is made. A circuitry simulation was performed under the similar test conditions in order to validate the state-space model. Results: Results showed that the state-space averaging model yields the similar performance as produced by the circuitry simulation in terms of the voltage, current and power generated. Conclusion/Recommendations: The state-space averaging technique is simple to be implemented in modeling and control of either simple or complex system, which yields the similar performance as the results from circuitry method.
A method for fuzzy self-tuning PID parameters of motion control systems%一种运动控制系统的PID参数模糊自整定方法
Institute of Scientific and Technical Information of China (English)
余震; 陈玮; 周学才
2012-01-01
针对运动控制系统设计了一种模糊自整定PID参数控制器,该控制器首先采用基于继电特性的方法求得PID控制器参数的初值,进而利用模糊控制器对运动控制系统的PID参数进行在线整定、计算和调整,并将取得的对应系统最佳性能的PID参数作为输出结果。目前该方法已在Matlab/Simulink中进行了仿真和实验,结果表明该自整定方法能使运动控制系统快速获得满意的PID参数和控制效果。%The paper proposes a design for a fuzzy self-tuning PID controller. The controller finds the initial value of PID controller parameters via a relay-based method, and performs online tuning, computing and adjusting for PID parameters with a fuzzy controller, which then exports PID parameters optimized for performance for the specific system. Simulation in Matlah/Simulink shows that this self-tuning method is fast to obtain satisfactory PID parameters and high performance for motion control systems.
Intelligent controller of a flexible hybrid robot machine for ITER assembly and maintenance
Energy Technology Data Exchange (ETDEWEB)
Al-saedi, Mazin I., E-mail: mazin.al-saedi@lut.fi; Wu, Huapeng; Handroos, Heikki
2014-10-15
Highlights: • Studying flexible multibody dynamic of hybrid parallel robot. • Investigating fuzzy-PD controller to control a hybrid flexible hydraulically driven robot. • Investigating ANFIS-PD controller to control a hybrid flexible robot. Compare to traditional PID this method gives better performance. • Using the equilibrium of reaction forces between the parallel and serial parts of hybrid robot to control the serial part hydraulically driven. - Abstract: The assembly and maintenance of International Thermonuclear Experimental Reactor (ITER) vacuum vessel (VV) is highly challenging since the tasks performed by the robot involve welding, material handling, and machine cutting from inside the VV. To fulfill the tasks in ITER application, this paper presents a hybrid redundant manipulator with four DOFs provided by serial kinematic axes and six DOFs by parallel mechanism. Thus, in machining, to achieve greater end-effector trajectory tracking accuracy for surface quality, a robust control of the actuators for the flexible link has to be deduced. In this paper, the intelligent control of a hydraulically driven parallel robot part based on the dynamic model and two control schemes have been investigated: (1) fuzzy-PID self tuning controller composed of the conventional PID control and with fuzzy logic; (2) adaptive neuro-fuzzy inference system-PID (ANFIS-PID) self tuning of the gains of the PID controller, which are implemented independently to control each hydraulic cylinder of the parallel robot based on rod position predictions. The obtained results of the fuzzy-PID and ANFIS-PID self tuning controller can reduce more tracking errors than the conventional PID controller. Subsequently, the serial component of the hybrid robot can be analyzed using the equilibrium of reaction forces at the universal joint connections of the hexa-element. To achieve precise positional control of the end effector for maximum precision machining, the hydraulic cylinder should
Unification and extension of monolithic state space and iterative cochlear models.
Rapson, Michael J; Tapson, Jonathan C; Karpul, David
2012-05-01
Time domain cochlear models have primarily followed a method introduced by Allen and Sondhi [J. Acoust. Soc. Am. 66, 123-132 (1979)]. Recently the "state space formalism" proposed by Elliott et al. [J. Acoust. Soc. Am. 122, 2759-2771 (2007)] has been used to simulate a wide range of nonlinear cochlear models. It used a one-dimensional approach that is extended to two dimensions in this paper, using the finite element method. The recently developed "state space formalism" in fact shares a close relationship to the earlier approach. Working from Diependaal et al. [J. Acoust. Soc. Am. 82, 1655-1666 (1987)] the two approaches are compared and the relationship formalized. Understanding this relationship allows models to be converted from one to the other in order to utilize each of their strengths. A second method to derive the state space matrices required for the "state space formalism" is also presented. This method offers improved numerical properties because it uses the information available about the model more effectively. Numerical results support the claims regarding fluid dimension and the underlying similarity of the two approaches. Finally, the recent advances in the state space formalism [Bertaccini and Sisto, J. Comp. Phys. 230, 2575-2587 (2011)] are discussed in terms of this relationship.
A review of Bayesian state-space modelling of capture-recapture-recovery data.
King, Ruth
2012-04-06
Traditionally, state-space models are fitted to data where there is uncertainty in the observation or measurement of the system. State-space models are partitioned into an underlying system process describing the transitions of the true states of the system over time and the observation process linking the observations of the system to the true states. Open population capture-recapture-recovery data can be modelled in this framework by regarding the system process as the state of each individual observed within the study in terms of being alive or dead, and the observation process the recapture and/or recovery process. The traditional observation error of a state-space model is incorporated via the recapture/recovery probabilities being less than unity. The models can be fitted using a Bayesian data augmentation approach and in standard BUGS packages. Applying this state-space framework to such data permits additional complexities including individual heterogeneity to be fitted to the data at very little additional programming effort. We consider the efficiency of the state-space model fitting approach by considering a random effects model for capture-recapture data relating to dippers and compare different Bayesian model-fitting algorithms within WinBUGS.
A review of Bayesian state-space modelling of capture–recapture–recovery data
King, Ruth
2012-01-01
Traditionally, state-space models are fitted to data where there is uncertainty in the observation or measurement of the system. State-space models are partitioned into an underlying system process describing the transitions of the true states of the system over time and the observation process linking the observations of the system to the true states. Open population capture–recapture–recovery data can be modelled in this framework by regarding the system process as the state of each individual observed within the study in terms of being alive or dead, and the observation process the recapture and/or recovery process. The traditional observation error of a state-space model is incorporated via the recapture/recovery probabilities being less than unity. The models can be fitted using a Bayesian data augmentation approach and in standard BUGS packages. Applying this state-space framework to such data permits additional complexities including individual heterogeneity to be fitted to the data at very little additional programming effort. We consider the efficiency of the state-space model fitting approach by considering a random effects model for capture–recapture data relating to dippers and compare different Bayesian model-fitting algorithms within WinBUGS. PMID:23565333
基于自整定Fuzzy-PI控制的电流跟踪型光伏并网逆变器%CURRENT TRACKING PV INVERTER BASED ON SELF-TUNING FUZZY-PI CONTROL
Institute of Scientific and Technical Information of China (English)
胡晓青; 程启明; 王映斐; 汪明媚
2013-01-01
An unipolar grid-connected inverter was presented,which possesses the advantage such as low loss,small harmonics,small electromagnetic interference etc.comparing with bipolar inverter,more suitable for on-grid inverter control.Firstly,the DC/DC converter was modeled,then self-tuning Fuzzy-PI control strategy was put forward,which integrating fuzzy self-tuning control theory with PID control law,the direct current produced by solar panels was inverted into the sinusoidal AC 220V/50Hz to provide to the grid.Finally,Matlab simulation model for current tracking PV inverter was completed based on the Fuzzy self-tuning PI control.The results show that the generated PWM control signal based on the method can effectively reduce the tracking error of traditional PID control and quickly track the target and net current waveform to improve the system' s dynamic response.%介绍了一种单极性并网逆变器,相对于双极性并网逆变器具有损耗低、谐波小、电磁干扰小的优势,更适用于并网逆变控制.首先对DC/DC变换器进行建模,随后提出一种将模糊自整定控制理论融合PID控制规律之中的自整定Fuzzy-PI控制策略,将太阳电池板产生的直流电逆变为220V/50Hz的正弦交流电提供给电网,最后建立了基于Fuzzy自整定PI控制的电流跟踪型光伏并网逆变器完整的Matlab仿真模型.仿真结果表明,基于该方法生成的PWM控制信号较之传统PID控制等可有效减小跟踪误差,并迅速跟踪目标并网电流波形,从而提高系统的动态响应性能.
A simplified state-space model of biventricular assist device-cardiovascular system interaction.
Koh, Vivian C A; Einly Lim; Boon Chiang Ng; Yong Kuen Ho; Lovell, Nigel H
2016-08-01
A simplified state-space model of biventricular assist device (BiVAD)-cardiovascular system (CVS) interaction is presented. The state-space equations includes a six-compartments CVS model incorporating the ventricles, the pulmonary and systemic circulations as well as the non-linear behavior of the valve flow, together with a left ventricular assist device (LVAD) and a right ventricular assist device (RVAD) component. The left and right pump speeds serve as the input variables for the state-space model. The model is simulated with three operational modes, i.e. (i) RVAD speed state hemodynamics is also studied with and without an outflow banding restriction. Our simulated results are validated with experimental data obtained from clinical, in vivo and in vitro studies provided in the literatures. We observed that despite its simplicity, the model is able to reproduce the observed trends in the reported studies, thus making it feasible for the development of robust yet practical control algorithms.
An application of gain-scheduled control using state-space interpolation to hydroactive gas bearings
DEFF Research Database (Denmark)
Theisen, Lukas Roy Svane; Camino, Juan F.; Niemann, Hans Henrik
2016-01-01
, it is possible to design a gain-scheduled controller using multiple controllers optimised for a single frequency. Gain-scheduling strategies using the Youla parametrisation can guarantee stability at the cost of increased controller order and performance loss in the interpolation region. This paper contributes...... with a gain-scheduling strategy using state-space interpolation, which avoids both the performance loss and the increase of controller order associated to the Youla parametrisation. The proposed state-space interpolation for gain-scheduling is applied for mass imbalance rejection for a controllable gas...... bearing scheduled in two parameters. Comparisons against the Youla-based scheduling demonstrate the superiority of the state-space interpolation....
State-Space Geometry, Statistical Fluctuations, and Black Holes in String Theory
Directory of Open Access Journals (Sweden)
Stefano Bellucci
2014-01-01
Full Text Available We study the state-space geometry of various extremal and nonextremal black holes in string theory. From the notion of the intrinsic geometry, we offer a state-space perspective to the black hole vacuum fluctuations. For a given black hole entropy, we explicate the intrinsic geometric meaning of the statistical fluctuations, local and global stability conditions, and long range statistical correlations. We provide a set of physical motivations pertaining to the extremal and nonextremal black holes, namely, the meaning of the chemical geometry and physics of correlation. We illustrate the state-space configurations for general charge extremal black holes. In sequel, we extend our analysis for various possible charge and anticharge nonextremal black holes. From the perspective of statistical fluctuation theory, we offer general remarks, future directions, and open issues towards the intrinsic geometric understanding of the vacuum fluctuations and black holes in string theory.
A circular polymer chain in a gel - the reduction of the state space
Krawczyk, Malgorzata J
2012-01-01
The state space of a polymer molecule is analysed. We show how the size of the state space can be reduced on the basis of symmetry. In the reduced state space, the probability of a new state (termed below as class) is equal to the number of old states represented by the new state multiplied by the probability of each old state. As an application, the electrophoretic motion of the molecule in gel is considered. We discuss the influence of the gel medium and of external field on the molecule states, with absorbing states of hooked molecules playing a major role. We show that in the case of strong fields both the velocity and the diffusion coefficient decrease with field. Finally, we evaluate the time of relaxation to and from the absorbing states. This is done with a continuous version of the exact enumeration method for weighted networks.
State-space Geometry, Statistical Fluctuations and Black Holes in String Theory
Bellucci, Stefano
2011-01-01
We study the state-space geometry of various extremal and nonextremal black holes in string theory. From the notion of the intrinsic geometry, we offer a new perspective of black hole vacuum fluctuations. For a given black hole entropy, we explicate the intrinsic state-space geometric meaning of the statistical fluctuations, local and global stability conditions and long range statistical correlations. We provide a set of physical motivations pertaining to the extremal and nonextremal black holes, \\textit{viz.}, the meaning of the chemical geometry and physics of correlation. We illustrate the state-space configurations for general charge extremal black holes. In sequel, we extend our analysis for various possible charge and anticharge nonextremal black holes. From the perspective of statistical fluctuation theory, we offer general remarks, future directions and open issues towards the intrinsic geometric understanding of the vacuum fluctuations and black holes in string theory. Keywords: Intrinsic Geometry; ...
A simpler and elegant algorithm for computing fractal dimension in higher dimensional state space
Indian Academy of Sciences (India)
S Ghorui; A K Das; N Venkatramani
2000-02-01
Chaotic systems are now frequently encountered in almost all branches of sciences. Dimension of such systems provides an important measure for easy characterization of dynamics of the systems. Conventional algorithms for computing dimension of such systems in higher dimensional state space face an unavoidable problem of enormous storage requirement. Here we present an algorithm, which uses a simple but very powerful technique and faces no problem in computing dimension in higher dimensional state space. The unique indexing technique of hypercubes, used in this algorithm, provides a clever means to drastically reduce the requirement of storage. It is shown that theoretically this algorithm faces no problem in computing capacity dimension in any dimension of the embedding state space as far as the actual dimension of the attractor is ﬁnite. Unlike the existing algorithms, memory requirement offered by this algorithm depends only on the actual dimension of the attractor and has no explicit dependence on the number of data points considered.
Establishing formal state space models via quantization for quantum control systems
Institute of Scientific and Technical Information of China (English)
Dong Daoyi; Chen Zonghai
2005-01-01
Formal state space models of quantum control systems are deduced and a scheme to establish formal state space models via quantization could been obtained for quantum control systems is proposed. State evolution of quantum control systems must accord with Schrodinger equations, so it is foremost to obtain Hamiltonian operators of systems. There are corresponding relations between operators of quantum systems and corresponding physical quantities of classical systems,such as momentum, energy and Hamiltonian, so Schrodinger equation models of corresponding quantum control systems via quantization could been obtained from classical control systems, and then establish formal state space models through the suitable transformation from Schrodinger equations for these quantum control systems. This method provides a new kind of path for modeling in quantum control.
Parallel symbolic state-space exploration is difficult, but what is the alternative?
Directory of Open Access Journals (Sweden)
Gianfranco Ciardo
2009-12-01
Full Text Available State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1 parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2 symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal.
Tracking with particle filter for high-dimensional observation and state spaces
Dubuisson, Séverine
2015-01-01
This title concerns the use of a particle filter framework to track objects defined in high-dimensional state-spaces using high-dimensional observation spaces. Current tracking applications require us to consider complex models for objects (articulated objects, multiple objects, multiple fragments, etc.) as well as multiple kinds of information (multiple cameras, multiple modalities, etc.). This book presents some recent research that considers the main bottleneck of particle filtering frameworks (high dimensional state spaces) for tracking in such difficult conditions.
Robust Quasi-LPV Control Based on Neural State Space Models
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2002-01-01
In this paper we derive a synthesis result for robust LPV output feedback controllers for nonlinear systems modelled by neural state space models. This result is achieved by writing the neural state space model on a linear fractional transformation form in a non-conservative way, separating...... that there is some uncertainty on the identified nonlinearities. The control law is therefore made robust to noise perturbations. After formulating the controller synthesis as a set of LMIs with added constraints, some implementation issues are addressed and a simulation example is presented....
State-space models - from the EM algorithm to a gradient approach
DEFF Research Database (Denmark)
Olsson, Rasmus Kongsgaard; Petersen, Kaare Brandt; Lehn-Schiøler, Tue
2007-01-01
Slow convergence is observed in the EM algorithm for linear state-space models. We propose to circumvent the problem by applying any off-the-shelf quasi-Newton-type optimizer, which operates on the gradient of the log-likelihood function. Such an algorithm is a practical alternative due to the fact...... that the exact gradient of the log-likelihood function can be computed by recycling components of the expectation-maximization (EM) algorithm. We demonstrate the efficiency of the proposed method in three relevant instances of the linear state-space model. In high signal-to-noise ratios, where EM is particularly...
Identification of a class of nonlinear state-space models using RPE techniques
DEFF Research Database (Denmark)
Zhou, W. W.; Blanke, Mogens
1986-01-01
The recursive prediction error methods in state-space form have been efficiently used as parameter identifiers for linear systems, and especially Ljung's innovations filter using a Newton search direction has proved to be quite ideal. In this paper, the RPE method in state-space form is developed...... to the nonlinear case and extended to include the exact form of a nonlinearity, thus enabling structure preservation for certain classes of nonlinear systems. Both the discrete and the continuous-discrete versions of the algorithm in an innovations model are investigated, and a nonlinear simulation example shows...... a quite convincing performance of the filter as combined parameter and state estimator....
Robust Quasi-LPV Control Based on Neural State Space Models
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2000-01-01
In this paper we derive a synthesis result for robust LPV output feedback controllers for nonlinear systems modelled by neural state space models. This result is achieved by writing the neural state space model on a linear fractional transformation form in a non-conservative way, separating...... the system description into a linear part and a nonlinear part. Linear parameter-varying control synthesis methods are then applied to design a nonlinear control law for this system. Since the model is assumed to have been identified from input-output measurement data only, it must be expected...
Global properties of linear constraints in state space and motion planning
Institute of Scientific and Technical Information of China (English)
陈滨; 朱海平
1997-01-01
Study of nonholonomic motion planning needs further research into the global properties of linear constraints in state space.The global properties of constraints,which contain the holonomicity and the nonholonomici-ty by regions,the existence of the isolated integral manifolds and the singular points and so on,have essential influence on motion planning.By analysis of the point sets in total space,the complete sketch of the global properties of linear constraints in state space is obtained,which can directly be applied to motion planning.
Reinforcement Learning in Large State Spaces Simulated Robotic Soccer as a Testbed
Tuyls, Karl; Maes, Sam; Manderick, Bernard
2003-01-01
Large state spaces and incomplete information are two problems that stand out in learning in multi-agent systems. In this paper we tackle them both by using a combination of decision trees and Bayesian networks (BNs) to model the environment and the Q-function. Simulated robotic soccer is used as a testbed, since there agents are faced with both large state spaces and incomplete information. The long-term goal of this research is to define generic techniques that allow agents to learn in larg...
A State Space Method for Modal Identification of Mechanical Systems from Time Domain Responses
Directory of Open Access Journals (Sweden)
Xiaobo Liu
2005-01-01
Full Text Available A new state space method is presented for modal identification of a mechanical system from its time domain impulse or initial condition responses. A key step in this method is the identification of the characteristic polynomial coefficients of an adjoint system. Once these coefficients are determined, a canonical state space realization of the adjoint system and the system's modal parameters are formulated straightforwardly. This method is conceptually and mathematically simple and is easy to be implemented. Detailed mathematical treatments are demonstrated and numerical examples are provided to illustrate the use and effectiveness of the method.
基于改进自调整图谱方法的SAR图像分割%SAR image segmentation based on improved self-tuning spectral clustering method
Institute of Scientific and Technical Information of China (English)
公徐路; 田铮; 赵伟
2014-01-01
谱聚类是在给定数据集上用基于图论的方法进行分类,并已广泛地应用于SAR图像分割.自调整谱聚类(self-tuning spectral clustering,简称STSC)方法是一种可以自动确定尺度因子和分类数的方法.本文给出了一种改进的STSC方法,使用熵函数作为自动求分类数的代价函数,使得分类数的计算更加准确和有效,提高了方法的分类精度.实验表明,改进的STSC方法对自然图像、SAR图像的分割精度高于原STSC方法.
Institute of Scientific and Technical Information of China (English)
魏凤美; 赵育善; 师鹏
2014-01-01
建立了带有太阳翼的挠性航天器的姿态动力学模型,应用改进的罗德里格参数来描述姿态运动学模型。针对挠性航天器模型参数不确定性和环境干扰等问题,提出了变论域自整定模糊比例积分微分(PID)控制方案,构建了计算简单并且可以达到控制精度的伸缩因子。基于Matlab/Simulink进行了仿真验证,结果表明,变论域自整定模糊 PID 控制响应速度比传统PID控制、模糊PID控制快350 s,且无超调,不仅能够使航天器完成对目标姿态的机动,而且能够有效地抑制挠性太阳翼的振动。%The attitude dynamical equations of flexible spacecraft with solar panels were established, and the attitude kinematic equations were described by modified Rodrigues parameters (MRPs ) in order to prevent the singularity of the large angle maneuver. A variable universe self-tuning fuzzy PID controller was proposed for model uncertainties and environmental disturbances. A new shrinkable factor was designed, and it was easy to calculate and could achieve the accuracy. Numerical simulation based on Matlab/Simulink shows that the response of the variable universe self-tuning fuzzy PID controller are 350 s faster than that of the conventional PID and fuzzy PID controllers, and that it has no overshoot and can realize the effective control of attitude maneuver and effectively suppress the vibration of solar panels.
State-space modeling indicates rapid invasion of an alien shrub in coastal dunes
DEFF Research Database (Denmark)
Damgaard, Christian Frølund; Nygaard, Bettina; Ejrnæs, Rasmus
2011-01-01
Invasion by alien plants has negative effects on coastal dunes. Monitoring local spread of invasive species depends on long-term data with sufficient spatial resolution. Bayesian state-space models are a new method for monitoring invasive plants based on unbalanced permanent-plot data. The method...
Strict System Equivalence of 2D Linear Discrete State Space Models
Directory of Open Access Journals (Sweden)
Mohamed S. Boudellioua
2012-01-01
Full Text Available The connection between the polynomial matrix descriptions (PMDs of the well-known regular and singular 2D linear discrete state space models is considered. It is shown that the transformation of strict system equivalence in the sense of Fuhrmann provides the basis for this connection. The exact form of the transformation is established for both the regular and singular cases.
Analysis of Convergence Rates of Some Gibbs Samplers on Continuous State Spaces
Smith, Aaron
2011-01-01
We use a non-Markovian coupling and small modi?cations of techniques from the theory of ?nite Markov chains to analyze some Markov chains on continuous state spaces. The ?rst is a Gibbs sampler on narrow contingency tables, the second a gen- eralization of a sampler introduced by Randall and Winkler.
Statistical Algorithms for Models in State Space Using SsfPack 2.2
Koopman, S.J.M.; Shephard, N.; Doornik, J.A.
1998-01-01
This paper discusses and documents the algorithms of SsfPack 2.2. SsfPack is a suite of C routines for carrying out computations involving the statistical analysis of univariate and multivariate models in state space form. The emphasis is on documenting the link we have made to the Ox computing envi
Equivalence and Differences between Structural Equation Modeling and State-Space Modeling Techniques
Chow, Sy-Miin; Ho, Moon-ho R.; Hamaker, Ellen L.; Dolan, Conor V.
2010-01-01
State-space modeling techniques have been compared to structural equation modeling (SEM) techniques in various contexts but their unique strengths have often been overshadowed by their similarities to SEM. In this article, we provide a comprehensive discussion of these 2 approaches' similarities and differences through analytic comparisons and…
Mixed-effects state-space models for analysis of longitudinal dynamic systems.
Liu, Dacheng; Lu, Tao; Niu, Xu-Feng; Wu, Hulin
2011-06-01
The rapid development of new biotechnologies allows us to deeply understand biomedical dynamic systems in more detail and at a cellular level. Many of the subject-specific biomedical systems can be described by a set of differential or difference equations that are similar to engineering dynamic systems. In this article, motivated by HIV dynamic studies, we propose a class of mixed-effects state-space models based on the longitudinal feature of dynamic systems. State-space models with mixed-effects components are very flexible in modeling the serial correlation of within-subject observations and between-subject variations. The Bayesian approach and the maximum likelihood method for standard mixed-effects models and state-space models are modified and investigated for estimating unknown parameters in the proposed models. In the Bayesian approach, full conditional distributions are derived and the Gibbs sampler is constructed to explore the posterior distributions. For the maximum likelihood method, we develop a Monte Carlo EM algorithm with a Gibbs sampler step to approximate the conditional expectations in the E-step. Simulation studies are conducted to compare the two proposed methods. We apply the mixed-effects state-space model to a data set from an AIDS clinical trial to illustrate the proposed methodologies. The proposed models and methods may also have potential applications in other biomedical system analyses such as tumor dynamics in cancer research and genetic regulatory network modeling. © 2010, The International Biometric Society.
Comment on "Network analysis of the state space of discrete dynamical systems"
Li, Chengqing; Shu, Shi
2016-01-01
This paper comments the letter entitled "Network analysis of the state space of discrete dynamical systems" by A. Shreim et al. [Physical Review Letters, 98, 198701 (2007)]. We found that some theoretical analyses are wrong and the proposed indicators based on parameters of phase network can not discriminate dynamical complexity of the discrete dynamical systems composed by 1-D Cellular Automata.
Identification of Nonlinear Nonautonomous State Space Systems from Input-Output Measurements
Verdult, Vincent; Verhaegen, Michel; Scherpen, Jacquelien
2000-01-01
This paper presents a method to determine a nonlinear state space model from a finite number of measurements of the inputs and outputs. The method is based on embedding theory for nonlinear systems, and can be viewed as an extension of the subspace identification method for linear systems. The paper
State space investigation of the bullwhip problem with ARMA(1,1) demand processes
Gaalman, Gerard; Disney, Stephen M.
2006-01-01
Using state space techniques we study a "myopic" order-up-to policy. The policy is myopic because it is optimal at minimising local inventory holding and shortage costs. In particular we study the bullwhip effect produced by the replenishment policy reacting to a stochastic ARMA(l,l) demand processe
Choosing the observational likelihood in state-space stock assessment models
DEFF Research Database (Denmark)
Albertsen, Christoffer Moesgaard; Nielsen, Anders; Thygesen, Uffe Høgsbro
By implementing different observational likelihoods in a state-space age-based stock assessment model, we are able to compare the goodness-of-fit and effects on estimated fishing mortallity for different model choices. Model fit is improved by estimating suitable correlations between agegroups. We...
A discounted model for a repairable system with continuous state space
Bruns, P.B.
2000-01-01
We examine repairable systems with a continous state space and partial repair options, carried out at fixed times $n=1,2,...$. Every time interval $[n,n+1)$ there is a manufacturing cost and a repair cost. These cost functions are not restricted to the class of bounded functions in this study. Condi
Statistical Algorithms for Models in State Space Using SsfPack 2.2
Koopman, S.J.M.; Shephard, N.; Doornik, J.A.
1998-01-01
This paper discusses and documents the algorithms of SsfPack 2.2. SsfPack is a suite of C routines for carrying out computations involving the statistical analysis of univariate and multivariate models in state space form. The emphasis is on documenting the link we have made to the Ox computing envi
State Space Composition Technique for Intelligent Wheel Chair Adapting to Environment.
Hamagami, Tomoki; Hirata, Hironori
This paper describes a state space composition technique for the adaptation to environment in the autonomous behavior of intelligent wheel chair (IWC).In the product like IWC with actual sensors, composing state space is difficult problem since environmental information can not be observed sufficiently from restricted sensor inputs.A lot of states observed from same environment position raise the fail of the learning and adaptation with active learning approach.In order to compensate for the effects of the sensor configuration, that is sensor position, angle and precision, a normalization processing of position detector is introduced.In sensor normalization process, IWC scans present environment via range sensors with executing spot-turn, and prepare scan-patterns of each sensor.Then the normalization process adjusts the phase and dynamic range of each pattern to the reference sensor scan-pattern, analyzing phase differences and scale factors of each pattern against reference pattern.Using phase difference and scale factors, automated state space composition is possible.From the simulation experiment with both artificial and real-worlddraft, the automated state space construction is confirmed as a practical approach for pre-processing for environment learning and adaptation.
Equivalence and differences between structural equation modeling and state-space modeling techniques
Chow, Sy-Miin; Ho, Moon-ho R.; Hamaker, E.L.; Dolan, C.V.
2010-01-01
State-space modeling techniques have been compared to structural equation modeling (SEM) techniques in various contexts but their unique strengths have often been overshadowed by their similarities to SEM. In this article, we provide a comprehensive discussion of these 2 approaches' similarities and
Approximate controllability of infinite dimensional linear systems in nonreflexive state spaces
Institute of Scientific and Technical Information of China (English)
Xin YU; Chao XU
2005-01-01
This paper deals with the problem of approximate controllability of infinite dimensional linear systems in nonreflexive state spaces.A necessary and sufficient condition for approximate controllability via Lp([0,T],U),1≤p<∞ is obtained,where Lp([0,T],U) is the control function space.
Linear State-Space Identification of Interconnected Systems: A structured approach
Torres Tapia, P.I.
2014-01-01
In this thesis, three novel state-space identification algorithms for linear interconnected systems are proposed. The computational complexity and the topology reconstruction of the interconnected system are addressed. Possible applications of this theory can be found in Biology, Economics, Transpor
State-space solutions to the h_inf/ltr design problem
DEFF Research Database (Denmark)
Niemann, Hans Henrik
1993-01-01
phase case, though, the order of the controllers can be reduced to n in all cases. The control problems corresponding to the various controller types are given as four different singular state-space problems, and the solutions are given in terms of the relevant equations and inequalities...
Equivalence and Differences between Structural Equation Modeling and State-Space Modeling Techniques
Chow, Sy-Miin; Ho, Moon-ho R.; Hamaker, Ellen L.; Dolan, Conor V.
2010-01-01
State-space modeling techniques have been compared to structural equation modeling (SEM) techniques in various contexts but their unique strengths have often been overshadowed by their similarities to SEM. In this article, we provide a comprehensive discussion of these 2 approaches' similarities and differences through analytic comparisons and…
A state-space model for estimating detailed movements and home range from acoustic receiver data
DEFF Research Database (Denmark)
Pedersen, Martin Wæver; Weng, Kevin
2013-01-01
We present a state-space model for acoustic receiver data to estimate detailed movement and home range of individual fish while accounting for spatial bias. An integral part of the approach is the detection function, which models the probability of logging tag transmissions as a function...... is used to estimate home range and movement of a reef fish in the Pacific Ocean....
State-space-based harmonic stability analysis for paralleled grid-connected inverters
DEFF Research Database (Denmark)
Wang, Yanbo; Wang, Xiongfei; Chen, Zhe
2016-01-01
This paper addresses a state-space-based harmonic stability analysis of paralleled grid-connected inverters system. A small signal model of individual inverter is developed, where LCL filter, the equivalent delay of control system, and current controller are modeled. Then, the overall small signa...
Scherpen, Jacquelien M.A.; Gray, W. Steven
2000-01-01
In this paper a set of sufficient conditions is developed in terms of controllability and observability functions under which a given state-space realization of a formal power series is minimal. Specifically, it is shown that positivity of these functions, in addition to a stability requirement and
System Identification of Civil Engineering Structures using State Space and ARMAV Models
DEFF Research Database (Denmark)
Andersen, P.; Kirkegaard, Poul Henning; Brincker, Rune
In this paper the relations between an ambient excited structural system, represented by an innovation state space system, and the Auto-Regressive Moving Average Vector (ARMAV) model are considered. It is shown how to obtain a multivariate estimate of the ARMAV model from output measurements, usi...
State Space Formulas for a Solution of the Suboptimal Nehari Problem on the Unit Disc
Curtain, Ruth F.; Opmeer, Mark R.
2009-01-01
We give state space formulas for a ("central") solution of the suboptimal Nehari problem for functions defined on the unit disc and taking values in the space of bounded operators in separable Hilbert spaces. Instead of assuming exponential stability, we assume a weaker stability concept (the combin
Cao, Youfang; Terebus, Anna; Liang, Jie
2016-04-01
The discrete chemical master equation (dCME) provides a general framework for studying stochasticity in mesoscopic reaction networks. Since its direct solution rapidly becomes intractable due to the increasing size of the state space, truncation of the state space is necessary for solving most dCMEs. It is therefore important to assess the consequences of state space truncations so errors can be quantified and minimized. Here we describe a novel method for state space truncation. By partitioning a reaction network into multiple molecular equivalence groups (MEGs), we truncate the state space by limiting the total molecular copy numbers in each MEG. We further describe a theoretical framework for analysis of the truncation error in the steady-state probability landscape using reflecting boundaries. By aggregating the state space based on the usage of a MEG and constructing an aggregated Markov process, we show that the truncation error of a MEG can be asymptotically bounded by the probability of states on the reflecting boundary of the MEG. Furthermore, truncating states of an arbitrary MEG will not undermine the estimated error of truncating any other MEGs. We then provide an overall error estimate for networks with multiple MEGs. To rapidly determine the appropriate size of an arbitrary MEG, we also introduce an a priori method to estimate the upper bound of its truncation error. This a priori estimate can be rapidly computed from reaction rates of the network, without the need of costly trial solutions of the dCME. As examples, we show results of applying our methods to the four stochastic networks of (1) the birth and death model, (2) the single gene expression model, (3) the genetic toggle switch model, and (4) the phage lambda bistable epigenetic switch model. We demonstrate how truncation errors and steady-state probability landscapes can be computed using different sizes of the MEG(s) and how the results validate our theories. Overall, the novel state space
H2-optimal control with generalized state-space models for use in control-structure optimization
Wette, Matt
1991-01-01
Several advances are provided solving combined control-structure optimization problems. The author has extended solutions from H2 optimal control theory to the use of generalized state space models. The generalized state space models preserve the sparsity inherent in finite element models and hence provide some promise for handling very large problems. Also, expressions for the gradient of the optimal control cost are derived which use the generalized state space models.
Ramakrishnan, Rajasekhar; Ramakrishnan, Janak D
2010-11-01
Tracer studies are analyzed almost universally by multicompartmental models where the state variables are tracer amounts or activities in the different pools. The model parameters are rate constants, defined naturally by expressing fluxes as fractions of the source pools. We consider an alternative state space with tracer enrichments or specific activities as the state variables, with the rate constants redefined by expressing fluxes as fractions of the destination pools. Although the redefinition may seem unphysiological, the commonly computed fractional synthetic rate actually expresses synthetic flux as a fraction of the product mass (destination pool). We show that, for a variety of structures, provided the structure is linear and stationary, the model in the enrichment state space has fewer parameters than that in the activities state space, and is hence better both to study identifiability and to estimate parameters. The superiority of enrichment modeling is shown for structures where activity model unidentifiability is caused by multiple exit pathways; on the other hand, with a single exit pathway but with multiple untraced entry pathways, activity modeling is shown to be superior. With the present-day emphasis on mass isotopes, the tracer in human studies is often of a precursor, labeling most or all entry pathways. It is shown that for these tracer studies, models in the activities state space are always unidentifiable when there are multiple exit pathways, even if the enrichment in every pool is observed; on the other hand, the corresponding models in the enrichment state space have fewer parameters and are more often identifiable. Our results suggest that studies with labeled precursors are modeled best with enrichments.
状态空间与Birkhoff力学%The State Space and Birkhoffian Mechanics
Institute of Scientific and Technical Information of China (English)
丁光涛
2012-01-01
In this paper, we expound in three ways that Birkhoffian mechanics is the analytical mechanics in the state space. (1) In the reduction of Newton' s equations to the first-order form to obtain the Birkhoffian representation of the equations, it is declared that Birkhoffian variables are the general state variables which are transformed form the coordinate-velocity state variables. (2) Birkhoffian equations are the analytical equations of motion according as the structure of Lagrange's equations in the state space and of Birkhoff's equations are the same, and the Lagrangian in the state space can consist of Birkhoffian function and functions. (3) Since by noncanonical transformation the phase space as a particular state space can be transformed into the general state space, and Hamiltonian equation can be transformed into Birkhoffian equation, Birkhoffian mechanics can be regarded as the analytical dynamics in the state space.%本文从三方面论述Birkhoff力学是状态空间中的分析动力学:(1)从Newton运动微分方程一次化而引入Birkhoff表示的过程中,说明Birkhoff变量是从坐标-速度状态变量变换而来的,即Birkhoff变量本质上是系统的广义状态变量.(2)论证系统状态空间中Lagrange方程与Birkhoff方程具有相同的结构,状态空间中系统的Lagrange函数可以由Birkhoff函数和函数组构成,就是说Birkhoff方程是状态空间中系统的分析力学运动方程.(3)相空间是一种特殊的状态空间,经非正则变换成为一般的状态空间,而Hamilton方程经非正则变换成为Birkhoff方程,再次说明Birkhoff力学是状态空间中分析动力学.
Institute of Scientific and Technical Information of China (English)
马静; 孙书利
2011-01-01
基于线性最小方差标量加权融合算法和射影理论,对带多个传感器和带相关噪声的广义系统,提出了分布式标量加权融合稳态满阶Kalman滤波器.推得了任两个传感器子系统之间的稳态满阶滤波误差互协方差阵,其解可任选初值离线迭代计算.所提出的稳态融合滤波器避免了每时刻计算协方差阵和融合权重,减小了在线计算负担.当系统含有未知模型参数时,基于递推增广最小二乘算法和标量加权融合算法,提出了一种两段融合自校正状态滤波器.其中第1段融合获得未知参数的融合估计;第2段融合获得分布式自校正融合状态滤波器.与局部估计和加权平均融合估计相比,所提出的标量加权融合参数估计和自校正状态估计都具有更高的精度.仿真研究验证了其有效性.%Based on the fusion algorithm weighted by scalars in the linear minimum variance sense and the projection theory,a distributed fusion steady-state full-order Kalman filter weighted by scalars is presented for descriptor systems with multiple sensors and correlated noises.The cross-covariance matrix of steady-state full-order filtering errors between any two sensor subsystems is derived.The solution can be computed by iteration with any initial value off-line.The proposed steady-state fusion filter avoids computing covariance matrices and fusion weights at each time step,so the online computational burden can be reduced.When the unknown model parameters are involved in the system,a two-stage fusion self-tuning state filter is presented based on the recursive extended least squares algorithm and fusion algorithm weighted by scalars.The first-stage fusion is to obtain the fusion estimate of the unknown parameters.The second-stage fusion is to obtain the distributed self-tuning fusion state filter.Compared with local estimates and weighted-average fusion estimate,the presented scalar-weighted fusion estimates for parameters
Development of Unsteady Aerodynamic State-Space Models from CFD-Based Pulse Responses
Silva, Walter A.; Raveh, Daniella E.
2001-01-01
A method for computing discrete-time state-space models of linearized unsteady aerodynamic behavior directly from aeroelastic CFD codes is presented. The method involves the treatment of CFD-based pulse responses as Markov parameters for use in a system identification /realization algorithm. Results are presented for the AGARD 445.6 Aeroelastic Wing with four aeroelastic modes at a Mach number of 0.96 using the EZNSS Euler/Navier-Stokes flow solver with aeroelastic capability. The System/Observer/Controller Identification Toolbox (SOCIT) algorithm, based on the Ho-Kalman realization algorithm, is used to generate 15th- and 32nd-order discrete-time state-space models of the unsteady aerodynamic response of the wing over the entire frequency range of interest.
Cointegration between trends and their estimators in state space models and CVAR models
DEFF Research Database (Denmark)
Johansen, Søren; Tabor, Morten Nyboe
2017-01-01
In a linear state space model Y(t)=BT(t) e(t), we investigate if the unobserved trend, T(t), cointegrates with the predicted trend, E(t), and with the estimated predicted trend, in the sense that the spreads are stationary. We find that this result holds for the spread B......(T(t)-E(t)) and the estimated spread. For the spread between the trend and the estimated trend, T(t)-E(t), however, cointegration depends on the identification of B. The same results are found, if the observations Y(t), from the state space model are analysed using a cointegrated vector autoregressive model, where the trend...... is defined as the common trend. Finally, we investigate cointegration between the spread between trends and their estimators based on the two models, and find the same results. We illustrate with two examples and confirm the results by a small simulation study....
Algorithms for a parallel implementation of Hidden Markov Models with a small state space
DEFF Research Database (Denmark)
Nielsen, Jesper; Sand, Andreas
2011-01-01
Two of the most important algorithms for Hidden Markov Models are the forward and the Viterbi algorithms. We show how formulating these using linear algebra naturally lends itself to parallelization. Although the obtained algorithms are slow for Hidden Markov Models with large state spaces......, they require very little communication between processors, and are fast in practice on models with a small state space. We have tested our implementation against two other imple- mentations on artificial data and observe a speed-up of roughly a factor of 5 for the forward algorithm and more than 6...... for the Viterbi algorithm. We also tested our algorithm in the Coalescent Hidden Markov Model framework, where it gave a significant speed-up....
The state space of a model for the Bray-Liebhafsky oscillating reaction
Schmitz, G.; Kolar-Anić, Lj.
2007-09-01
It has been known for a long time that the decomposition of hydrogen peroxide catalyzed by hydrogen and iodate ions, the Bray-Liebhafsky reaction, can generate oscillations in a batch reactor. Recently, mixed-mode oscillations and chaos have also been observed in a CSTR. The model we had previously proposed to explain the kinetics in a batch reactor can also simulate these new complex behaviors. Time series give only a limited view of the features of the calculated behaviors and more information is obtained studying the properties of the state space. We use projections of the trajectories, calculation of the correlation dimension of the attractor, Poincaré sections, and return maps. As the state space of the model is six-dimensional, we try to answer the questions of whether the projections into a 3D subspace give correct pictures of the real trajectories and whether we have reasons to prefer a special subspace.
Robust control of uncertain dynamic systems a linear state space approach
Yedavalli, Rama K
2014-01-01
This textbook aims to provide a clear understanding of the various tools of analysis and design for robust stability and performance of uncertain dynamic systems. In model-based control design and analysis, mathematical models can never completely represent the “real world” system that is being modeled, and thus it is imperative to incorporate and accommodate a level of uncertainty into the models. This book directly addresses these issues from a deterministic uncertainty viewpoint and focuses on the interval parameter characterization of uncertain systems. Various tools of analysis and design are presented in a consolidated manner. This volume fills a current gap in published works by explicitly addressing the subject of control of dynamic systems from linear state space framework, namely using a time-domain, matrix-theory based approach. This book also: Presents and formulates the robustness problem in a linear state space model framework Illustrates various systems level methodologies with examples and...
Modeling individual effects in the Cormack-Jolly-Seber Model: A state-space formulation
Royle, J. Andrew
2008-01-01
In population and evolutionary biology, there exists considerable interest in individual heterogeneity in parameters of demographic models for open populations. However, flexible and practical solutions to the development of such models have proven to be elusive. In this article, I provide a state-space formulation of open population capture-recapture models with individual effects. The state-space formulation provides a generic and flexible framework for modeling and inference in models with individual effects, and it yields a practical means of estimation in these complex problems via contemporary methods of Markov chain Monte Carlo. A straightforward implementation can be achieved in the software package WinBUGS. I provide an analysis of a simple model with constant parameter detection and survival probability parameters. A second example is based on data from a 7-year study of European dippers, in which a model with year and individual effects is fitted.
Directory of Open Access Journals (Sweden)
Nacer Tabib
2016-01-01
Full Text Available This paper proposes a new framework based on Binary Decision Diagrams (BDD for the graph distribution problem in the context of explicit model checking. The BDD are yet used to represent the state space for a symbolic verification model checking. Thus, we took advantage of high compression ratio of BDD to encode not only the state space, but also the place where each state will be put. So, a fitness function that allows a good balance load of states over the nodes of an homogeneous network is used. Furthermore, a detailed explanation of how to calculate the inter-site edges between different nodes based on the adapted data structure is presented.
A state space approach for the eigenvalue problem of marine risers
Alfosail, Feras
2017-10-05
A numerical state-space approach is proposed to examine the natural frequencies and critical buckling limits of marine risers. A large axial tension in the riser model causes numerical limitations. These limitations are overcome by using the modified Gram–Schmidt orthonormalization process as an intermediate step during the numerical integration process with the fourth-order Runge–Kutta scheme. The obtained results are validated against those obtained with other numerical methods, such as the finite-element, Galerkin, and power-series methods, and are found to be in good agreement. The state-space approach is shown to be computationally more efficient than the other methods. Also, we investigate the effect of a high applied tension, a high apparent weight, and higher-order modes on the accuracy of the numerical scheme. We demonstrate that, by applying the orthonormalization process, the stability and convergence of the approach are significantly improved.
DEFF Research Database (Denmark)
Poulsen, Tjalfe; Møldrup, Per; Nielsen, Don
2003-01-01
field were used. Multiple regression and ARIMA models yielded similar prediction accuracy, whereas state-space models generally gave significantly higher accuracy. State-space modeling suggested K-S at a given location could be predicted using nearby values of K-S, k(a100) and air-filled porosity......Estimates of soil hydraulic conductivity (K) and air permeability (k(a)) at given soil-water potentials are often used as reference points in constitutive models for K and k(a) as functions of moisture content and are, therefore, a prerequisite for predicting migration of water, air, and dissolved...... and gaseous chemicals in the vadose zone. In this study, three modeling approaches were used to identify the dependence of saturated hydraulic conductivity (K-S) and air permeability at -100 cm H2O soil-water potential (k(a100)) on soil physical properties in undisturbed soil: (i) Multiple regression, (ii...
STATE SPACE MODELING AND SIMULATION OF SENSORLESS PERMANENT MAGNET BLDC MOTOR
Directory of Open Access Journals (Sweden)
N. MURUGANANTHAM
2010-10-01
Full Text Available Brushless DC (BLDC motor simulation can be simply implemented with the required control scheme using specialized simulink built-in tools and block sets such as simpower systems toolbox. But it requires powerful processor requirements, large random access memory and long simulation time. To overcome these drawbacks this paper presents a state space modeling, simulation and control of permanent magnet brushless DC motor. By reading the instantaneous position of the rotor as an output, different variables of the motor can be controlled without the need of any external sensors or position detection techniques. Simulink is utilized with the assistance of MATLAB to give a very flexible and reliable simulation. With state space model representation, the motor performance can be analyzed for variation of motor parameters.
Modeling and Simulation of DC Power Electronics Systems Using Harmonic State Space (HSS) Method
DEFF Research Database (Denmark)
Kwon, Jun Bum; Wang, Xiongfei; Bak, Claus Leth
2015-01-01
For the efficiency and simplicity of electric systems, the dc based power electronics systems are widely used in variety applications such as electric vehicles, ships, aircrafts and also in homes. In these systems, there could be a number of dynamic interactions between loads and other dc...... based on the state-space averaging and generalized averaging, these also have limitations to show the same results as with the non-linear time domain simulations. This paper presents a modeling and simulation method for a large dc power electronic system by using Harmonic State Space (HSS) modeling....... Through this method, the required computation time and CPU memory for large dc power electronics systems can be reduced. Besides, the achieved results show the same results as with the non-linear time domain simulation, but with the faster simulation time which is beneficial in a large network....
A Stochastic and State Space Model for Tumour Growth and Applications
Directory of Open Access Journals (Sweden)
Wai-Yuan Tan
2009-01-01
Full Text Available We develop a state space model documenting Gompertz behaviour of tumour growth. The state space model consists of two sub-models: a stochastic system model that is an extension of the deterministic model proposed by Gyllenberg and Webb (1991, and an observation model that is a statistical model based on data for the total number of tumour cells over time. In the stochastic system model we derive through stochastic equations the probability distributions of the numbers of different types of tumour cells. Combining with the statistic model, we use these distribution results to develop a generalized Bayesian method and a Gibbs sampling procedure to estimate the unknown parameters and to predict the state variables (number of tumour cells. We apply these models and methods to real data and to computer simulated data to illustrate the usefulness of the models, the methods, and the procedures.
Particle Filtering for Large Dimensional State Spaces with Multimodal Observation Likelihoods
Vaswani, Namrata
2008-01-01
We study efficient importance sampling techniques for particle filtering (PF) when either (a) the observation likelihood (OL) is frequently multimodal or heavy-tailed, or (b) the state space dimension is large or both. When the OL is multimodal, but the state transition pdf (STP) is narrow enough, the optimal importance density is usually unimodal. Under this assumption, many techniques have been proposed. But when the STP is broad, this assumption does not hold. We study how existing techniques can be generalized to situations where the optimal importance density is multimodal, but is unimodal conditioned on a part of the state vector. Sufficient conditions to test for the unimodality of this conditional posterior are derived. The number of particles, N, to accurately track using a PF increases with state space dimension, thus making any regular PF impractical for large dimensional tracking problems. We propose a solution that partially addresses this problem. An important class of large dimensional problems...
State-space-based harmonic stability assessment of paralleled grid-connected inverters system
DEFF Research Database (Denmark)
Wang, Yanbo; Wang, Xiongfei; Chen, Zhe;
2016-01-01
model of paralleled grid-connected inverters is built. Finally, the state space-based stability analysis approach is developed to explain the harmonic resonance phenomenon. The eigenvalue traces associated with time delay and coupled grid impedance are obtained, which accounts for how the unstable......This paper addresses a state-space-based harmonic stability analysis of paralleled grid-connected inverters system. A small signal model of individual inverter is developed, where LCL filter, the equivalent delay of control system, and current controller are modeled. Then, the overall small signal...... inverter produces the harmonic resonance and leads to the instability of whole paralleled system. The proposed approach reveals the contributions of the grid impedance as well as the coupled effect on other grid-connected inverters under different grid conditions. Simulation and experimental results...
Analytic State Space Model for an Unsteady Finite-Span Wing
Izraelevitz, Jacob; Zhu, Qiang; Triantafyllou, Michael
2015-11-01
Real-time control of unsteady flows, such as force control in flapping wings, requires simple wake models that easily translate into robust control designs. We analytically derive a state-space model for the unsteady trailing vortex system behind a finite aspect-ratio flapping wing. Contrary to prior models, the downwash and lift distributions over the span can be arbitrary, including tip effects. The wake vorticity is assumed to be a fully unsteady distribution, with the exception of quasi-steady (no rollup) geometry. Each discretization along the span has one to four states to represent the local unsteady wake-induced downwash, lift, and circulation. The model supports independently time-varying velocity, heave, and twist along the span. We validate this state-space model through comparison with existing analytic solutions for elliptic wings and an unsteady inviscid panel method.
Addressing challenges in single species assessments via a simple state-space assessment model
DEFF Research Database (Denmark)
Nielsen, Anders
Single-species and age-structured fish stock assessments still remains the main tool for managing fish stocks. A simple state-space assessment model is presented as an alternative to (semi) deterministic procedures and the full parametric statistical catch at age models. It offers a solution...... of state-space assessment models is that they tend to be more conservative (react slower to changes) than the alternatives. A solution to this criticism is offered by introducing a mixture distribution for the transitions steps. The model presented is used for several commercially important stocks...... to some of the key challenges of these models. Compared to the deterministic procedures it solves a list of problems originating from falsely assuming that age classified catches are known without errors and allows quantification of uncertainties of estimated quantities of interest. Compared to full...
State-space-split method for some generalized Fokker-Planck-Kolmogorov equations in high dimensions.
Er, Guo-Kang; Iu, Vai Pan
2012-06-01
The state-space-split method for solving the Fokker-Planck-Kolmogorov equations in high dimensions is extended to solving the generalized Fokker-Planck-Kolmogorov equations in high dimensions for stochastic dynamical systems with a polynomial type of nonlinearity and excited by Poissonian white noise. The probabilistic solution of the motion of the stretched Euler-Bernoulli beam with cubic nonlinearity and excited by uniformly distributed Poissonian white noise is analyzed with the presented solution procedure. The numerical analysis shows that the results obtained with the state-space-split method together with the exponential polynomial closure method are close to those obtained with the Monte Carlo simulation when the relative value of the basic system relaxation time and the mean arrival time of the Poissonian impulse is in some limited range.
H_2-Optimal Decentralized Control over Posets: A State-Space Solution for State-Feedback
Shah, Parikshit
2011-01-01
We develop a complete state-space solution to H_2-optimal decentralized control of poset-causal systems with state-feedback. Our solution is based on the exploitation of a key separability property of the problem, that enables an efficient computation of the optimal controller by solving a small number of uncoupled standard Riccati equations. Our approach gives important insight into the structure of optimal controllers, such as controller degree bounds that depend on the structure of the poset. A novel element in our state-space characterization of the controller is a remarkable pair of transfer functions, that belong to the incidence algebra of the poset, are inverses of each other, and are intimately related to prediction of the state along the different paths on the poset. The results are illustrated by a numerical example.
DEFF Research Database (Denmark)
Wang, Yanbo; Wang, Xiongfei; Blaabjerg, Frede
2017-01-01
This paper presents a harmonic instability analysis method using state-space modeling and participation analysis in the inverter-fed ac power systems. A full-order state-space model for the droop-controlled Distributed Generation (DG) inverter is built first, including the time delay of the digit...
Ruess, Jakob
2015-12-28
Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space.
Identification of a Class of Non-linear State Space Models using RPE Techniques
DEFF Research Database (Denmark)
Zhou, Wei-Wu; Blanke, Mogens
1989-01-01
The RPE (recursive prediction error) method in state-space form is developed in the nonlinear systems and extended to include the exact form of a nonlinearity, thus enabling structure preservation for certain classes of nonlinear systems. Both the discrete and the continuous-discrete versions...... of the algorithm in an innovations model are investigated, and a nonlinear simulation example shows a quite convincing performance of the filter as combined parameter and state estimator...
Bayesian state space models for dynamic genetic network construction across multiple tissues.
Liang, Yulan; Kelemen, Arpad
2016-08-01
Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.
Ruess, Jakob
2015-12-01
Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space.
Operator analogue of the Krein-Milman theorem in the generalized state spaces
Institute of Scientific and Technical Information of China (English)
吴畏
2001-01-01
We discuss the Krein-Milman-type problems in the C-convexity theory for the generalized state space SCn(A) of C-algebra A. The main results are that every BW-compact, C-convex subset of SCn(A) possesses a C-extreme point and every BW-compact, C-convex subset of SCn(A) is the C -convex hull of its C -extreme points.
MCMC for non-linear state space models using ensembles of latent sequences
2013-01-01
Non-linear state space models are a widely-used class of models for biological, economic, and physical processes. Fitting these models to observed data is a difficult inference problem that has no straightforward solution. We take a Bayesian approach to the inference of unknown parameters of a non-linear state model; this, in turn, requires the availability of efficient Markov Chain Monte Carlo (MCMC) sampling methods for the latent (hidden) variables and model parameters. Using the ensemble ...
The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications
Andreasen, Martin Møller; Fernández-Villaverde, Jesús; Juan F Rubio-Ramírez
2013-01-01
This paper studies the pruned state-space system for higher-order approximations to the solutions of DSGE models. For second- and third-order approximations, we derive the statistical properties of this system and provide closed-form expressions for first and second unconditional moments and impulse response functions. Thus, our analysis introduces GMM estimation for DSGE models approximated up to third-order and provides the foundation for indirect inference and SMM when simulation is requir...
Hierarchical approximate policy iteration with binary-tree state space decomposition.
Xu, Xin; Liu, Chunming; Yang, Simon X; Hu, Dewen
2011-12-01
In recent years, approximate policy iteration (API) has attracted increasing attention in reinforcement learning (RL), e.g., least-squares policy iteration (LSPI) and its kernelized version, the kernel-based LSPI algorithm. However, it remains difficult for API algorithms to obtain near-optimal policies for Markov decision processes (MDPs) with large or continuous state spaces. To address this problem, this paper presents a hierarchical API (HAPI) method with binary-tree state space decomposition for RL in a class of absorbing MDPs, which can be formulated as time-optimal learning control tasks. In the proposed method, after collecting samples adaptively in the state space of the original MDP, a learning-based decomposition strategy of sample sets was designed to implement the binary-tree state space decomposition process. Then, API algorithms were used on the sample subsets to approximate local optimal policies of sub-MDPs. The original MDP was decomposed into a binary-tree structure of absorbing sub-MDPs, constructed during the learning process, thus, local near-optimal policies were approximated by API algorithms with reduced complexity and higher precision. Furthermore, because of the improved quality of local policies, the combined global policy performed better than the near-optimal policy obtained by a single API algorithm in the original MDP. Three learning control problems, including path-tracking control of a real mobile robot, were studied to evaluate the performance of the HAPI method. With the same setting for basis function selection and sample collection, the proposed HAPI obtained better near-optimal policies than previous API methods such as LSPI and KLSPI.
Exploiting Stabilizers and Parallelism in State Space Generation with the Symmetry Method
DEFF Research Database (Denmark)
Lorentsen, Louise; Kristensen, Lars Michael
2001-01-01
The symmetry method is a main reduction paradigm for alleviating the state explosion problem. For large symmetry groups deciding whether two states are symmetric becomes time expensive due to the apparent high time complexity of the orbit problem. The contribution of this paper is to alleviate th...... the negative impact of the orbit problem by the specification of canonical representatives for equivalence classes of states in Coloured Petri Nets, and by giving algorithms exploiting stabilizers and parallelism for computing the condensed state space....
State-space prediction of spring discharge in a karst catchment in southwest China
Li, Zhenwei; Xu, Xianli; Liu, Meixian; Li, Xuezhang; Zhang, Rongfei; Wang, Kelin; Xu, Chaohao
2017-06-01
Southwest China represents one of the largest continuous karst regions in the world. It is estimated that around 1.7 million people are heavily dependent on water derived from karst springs in southwest China. However, there is a limited amount of water supply in this region. Moreover, there is not enough information on temporal patterns of spring discharge in the area. In this context, it is essential to accurately predict spring discharge, as well as understand karst hydrological processes in a thorough manner, so that water shortages in this area could be predicted and managed efficiently. The objectives of this study were to determine the primary factors that govern spring discharge patterns and to develop a state-space model to predict spring discharge. Spring discharge, precipitation (PT), relative humidity (RD), water temperature (WD), and electrical conductivity (EC) were the variables analyzed in the present work, and they were monitored at two different locations (referred to as karst springs A and B, respectively, in this paper) in a karst catchment area in southwest China from May to November 2015. Results showed that a state-space model using any combinations of variables outperformed a classical linear regression, a back-propagation artificial neural network model, and a least square support vector machine in modeling spring discharge time series for karst spring A. The best state-space model was obtained by using PT and RD, which accounted for 99.9% of the total variation in spring discharge. This model was then applied to an independent data set obtained from karst spring B, and it provided accurate spring discharge estimates. Therefore, state-space modeling was a useful tool for predicting spring discharge in karst regions in southwest China, and this modeling procedure may help researchers to obtain accurate results in other karst regions.
The Physics of Imaging with Remote Sensors : Photon State Space & Radiative Transfer
Davis, Anthony B.
2012-01-01
Standard (mono-pixel/steady-source) retrieval methodology is reaching its fundamental limit with access to multi-angle/multi-spectral photo- polarimetry. Next... Two emerging new classes of retrieval algorithm worth nurturing: multi-pixel time-domain Wave-radiometry transition regimes, and more... Cross-fertilization with bio-medical imaging. Physics-based remote sensing: - What is "photon state space?" - What is "radiative transfer?" - Is "the end" in sight? Two wide-open frontiers! center dot Examples (with variations.
2000-01-01
We propose in this paper two methods to compute Markovian bounds for monotone functions of a discrete time homogeneous Markov chain evolving in a totally ordered state space. The main interest of such methods is to propose algorithms to simplify analysis of transient characteristics such as the output process of a queue, or sojourn time in a subset of states. Construction of bounds are based on two kinds of results: well-known results on stochastic comparison between Markov cha...
The Physics of Imaging with Remote Sensors : Photon State Space & Radiative Transfer
Davis, Anthony B.
2012-01-01
Standard (mono-pixel/steady-source) retrieval methodology is reaching its fundamental limit with access to multi-angle/multi-spectral photo- polarimetry. Next... Two emerging new classes of retrieval algorithm worth nurturing: multi-pixel time-domain Wave-radiometry transition regimes, and more... Cross-fertilization with bio-medical imaging. Physics-based remote sensing: - What is "photon state space?" - What is "radiative transfer?" - Is "the end" in sight? Two wide-open frontiers! center dot Examples (with variations.
Institute of Scientific and Technical Information of China (English)
席建中; 韩成春
2012-01-01
研制开发在核心技术上具有自主知识产权的自整定式液压震动波能量吸收装置,利用对油缸活塞的结构设计和液压油路控制增加液体流动的行程,通过液体流动阻尼和压力降,以此吸收振动能量,达到减震的目的.从而实现机械设备、特别是以振动方式工作的机械设备,例如振动压路机、振动挖掘机等工作点之外的机件、主机等进行减震、消震.该能量吸收装置结构简单,吸收震动源效果好,将复杂不断变化的震动能使用独立机构吸收,并巧妙的利用波的传递特性自调整抵消,避免了震动能对震源的冲击.%Researching and developing the core technology with independent intellectual property rights of the hydraulic shock waves from the self-tuning energy absorption equipment, making use of the structure design of hydraulic cylinder piston and control of the flow of fluid increase trip of hydraulic oil, through the liquid flow damping and the pressure drop, to absorbs vibration energy, and to achieve the purpose of shock.So as to realize the mechanical equipment,especially the mechanical equipment working with vibration mode ,such as vibratory roller, vibration excavators which damping and absorbing shock using machine parts outside of working points. This energy absorption equipment is simple in structure,good in absorption of shock source , using independent agencies to absorb complex changing vibration,and the clever use of wave transmission characteristics of self-tuning offset avoids the impact vibrational energy to vibration source.
Modal contribution and state space order selection in operational modal analysis
Cara, F. Javier; Juan, Jesús; Alarcón, Enrique; Reynders, Edwin; De Roeck, Guido
2013-07-01
The estimation of modal parameters of a structure from ambient measurements has attracted the attention of many researchers in the last years. The procedure is now well established and the use of state space models, stochastic system identification methods and stabilization diagrams allows to identify the modes of the structure. In this paper the contribution of each identified mode to the measured vibration is discussed. This modal contribution is computed using the Kalman filter and it is an indicator of the importance of the modes. Also the variation of the modal contribution with the order of the model is studied. This analysis suggests selecting the order for the state space model as the order that includes the modes with higher contribution. The order obtained using this method is compared to those obtained using other well known methods, like Akaike criteria for time series or the singular values of the weighted projection matrix in the Stochastic Subspace Identification method. Finally, both simulated and measured vibration data are used to show the practicability of the derived technique. Finally, it is important to remark that the method can be used with any identification method working in the state space model.
Cara, Javier
2016-05-01
Modal parameters comprise natural frequencies, damping ratios, modal vectors and modal masses. In a theoretic framework, these parameters are the basis for the solution of vibration problems using the theory of modal superposition. In practice, they can be computed from input-output vibration data: the usual procedure is to estimate a mathematical model from the data and then to compute the modal parameters from the estimated model. The most popular models for input-output data are based on the frequency response function, but in recent years the state space model in the time domain has become popular among researchers and practitioners of modal analysis with experimental data. In this work, the equations to compute the modal parameters from the state space model when input and output data are available (like in combined experimental-operational modal analysis) are derived in detail using invariants of the state space model: the equations needed to compute natural frequencies, damping ratios and modal vectors are well known in the operational modal analysis framework, but the equation needed to compute the modal masses has not generated much interest in technical literature. These equations are applied to both a numerical simulation and an experimental study in the last part of the work.
Density-dependent state-space model for population-abundance data with unequal time intervals.
Dennis, Brian; Ponciano, José Miguel
2014-08-01
The Gompertz state-space (GSS) model is a stochastic model for analyzing time-series observations of population abundances. The GSS model combines density dependence, environmental process noise, and observation error toward estimating quantities of interest in biological monitoring and population viability analysis. However, existing methods for estimating the model parameters apply only to population data with equal time intervals between observations. In the present paper, we extend the GSS model to data with unequal time intervals, by embedding it within a state-space version of the Ornstein-Uhlenbeck process, a continuous-time model of an equilibrating stochastic system. Maximum likelihood and restricted maximum likelihood calculations for the Ornstein-Uhlenbeck state-space model involve only numerical maximization of an explicit multivariate normal likelihood, and so the extension allows for easy bootstrapping, yielding confidence intervals for model parameters, statistical hypothesis testing of density dependence, and selection among sub-models using information criteria. Ecologists and managers previously drawn to models lacking density dependence or observation error because such models accommodated unequal time intervals (for example, due to missing data) now have an alternative analysis framework incorporating density dependence, process noise, and observation error.
State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices.
Hong, Keum-Shik; Nguyen, Hoang-Dung
2014-06-01
THE PAPER PRESENTS STATE SPACE MODELS OF THE HEMODYNAMIC RESPONSE (HR) OF FNIRS TO AN IMPULSE STIMULUS IN THREE BRAIN REGIONS: motor cortex (MC), somatosensory cortex (SC), and visual cortex (VC). Nineteen healthy subjects were examined. For each cortex, three impulse HRs experimentally obtained were averaged. The averaged signal was converted to a state space equation by using the subspace method. The activation peak and the undershoot peak of the oxy-hemoglobin (HbO) in MC are noticeably higher than those in SC and VC. The time-to-peaks of the HbO in three brain regions are almost the same (about 6.76 76 ± 0.2 s). The time to undershoot peak in VC is the largest among three. The HbO decreases in the early stage (~0.46 s) in MC and VC, but it is not so in SC. These findings were well described with the developed state space equations. Another advantage of the proposed method is its easy applicability in generating the expected HR to arbitrary stimuli in an online (or real-time) imaging. Experimental results are demonstrated.
State-space models of head-related transfer functions for virtual auditory scene synthesis.
Adams, Norman H; Wakefield, Gregory H
2009-06-01
This study investigates the use of reduced-order state-space models of collections of head-related transfer functions (HRTFs). Recent head-phone applications have motivated interest in binaural displays that can render multiple simultaneous virtual sound sources, acoustic reflections, and source and listener motion. In the present study, a multi-direction framework is considered that can render such phenomena by filtering source signals with a collection of HRTFs rather than individual HRTFs. The collection of HRTFs is implemented in the state-space, and approximation techniques are applied to construct low-order approximants that are indiscriminable from full-order HRTFs. Two experiments are described in which five observers are asked to discriminate between state-space and full-order renderings. Depending on the stimulus conditions and discrimination task, order thresholds of 7
A new look at state-space models for neural data.
Paninski, Liam; Ahmadian, Yashar; Ferreira, Daniel Gil; Koyama, Shinsuke; Rahnama Rad, Kamiar; Vidne, Michael; Vogelstein, Joshua; Wu, Wei
2010-08-01
State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely on certain approximations which are not always accurate. Here we review direct optimization methods that avoid these approximations, but that nonetheless retain the computational efficiency of the approximate methods. We discuss a variety of examples, applying these direct optimization techniques to problems in spike train smoothing, stimulus decoding, parameter estimation, and inference of synaptic properties. Along the way, we point out connections to some related standard statistical methods, including spline smoothing and isotonic regression. Finally, we note that the computational methods reviewed here do not in fact depend on the state-space setting at all; instead, the key property we are exploiting involves the bandedness of certain matrices. We close by discussing some applications of this more general point of view, including Markov chain Monte Carlo methods for neural decoding and efficient estimation of spatially-varying firing rates.
A one-step-ahead pseudo-DIC for comparison of Bayesian state-space models.
Millar, R B; McKechnie, S
2014-12-01
In the context of state-space modeling, conventional usage of the deviance information criterion (DIC) evaluates the ability of the model to predict an observation at time t given the underlying state at time t. Motivated by the failure of conventional DIC to clearly choose between competing multivariate nonlinear Bayesian state-space models for coho salmon population dynamics, and the computational challenge of alternatives, this work proposes a one-step-ahead DIC, DICp, where prediction is conditional on the state at the previous time point. Simulations revealed that DICp worked well for choosing between state-space models with different process or observation equations. In contrast, conventional DIC could be grossly misleading, with a strong preference for the wrong model. This can be explained by its failure to account for inflated estimates of process error arising from the model mis-specification. DICp is not based on a true conditional likelihood, but is shown to have interpretation as a pseudo-DIC in which the compensatory behavior of the inflated process errors is eliminated. It can be easily calculated using the DIC monitors within popular BUGS software when the process and observation equations are conjugate. The improved performance of DICp is demonstrated by application to the multi-stage modeling of coho salmon abundance in Lobster Creek, Oregon. © 2014, The International Biometric Society.
Sun, Xiaodian; Jin, Li; Xiong, Momiao
2008-01-01
It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.
Inference and Decoding of Motor Cortex Low-Dimensional Dynamics via Latent State-Space Models.
Aghagolzadeh, Mehdi; Truccolo, Wilson
2016-02-01
Motor cortex neuronal ensemble spiking activity exhibits strong low-dimensional collective dynamics (i.e., coordinated modes of activity) during behavior. Here, we demonstrate that these low-dimensional dynamics, revealed by unsupervised latent state-space models, can provide as accurate or better reconstruction of movement kinematics as direct decoding from the entire recorded ensemble. Ensembles of single neurons were recorded with triple microelectrode arrays (MEAs) implanted in ventral and dorsal premotor (PMv, PMd) and primary motor (M1) cortices while nonhuman primates performed 3-D reach-to-grasp actions. Low-dimensional dynamics were estimated via various types of latent state-space models including, for example, Poisson linear dynamic system (PLDS) models. Decoding from low-dimensional dynamics was implemented via point process and Kalman filters coupled in series. We also examined decoding based on a predictive subsampling of the recorded population. In this case, a supervised greedy procedure selected neuronal subsets that optimized decoding performance. When comparing decoding based on predictive subsampling and latent state-space models, the size of the neuronal subset was set to the same number of latent state dimensions. Overall, our findings suggest that information about naturalistic reach kinematics present in the recorded population is preserved in the inferred low-dimensional motor cortex dynamics. Furthermore, decoding based on unsupervised PLDS models may also outperform previous approaches based on direct decoding from the recorded population or on predictive subsampling.
Density dependent state space model for population abundance data with unequal time intervals
Dennis, Brian; Ponciano, José Miguel
2014-01-01
The Gompertz state-space (GSS) model is a stochastic model for analyzing time series observations of population abundances. The GSS model combines density dependence, environmental process noise, and observation error toward estimating quantities of interest in biological monitoring and population viability analysis. However, existing methods for estimating the model parameters apply only to population data with equal time intervals between observations. In the present paper, we extend the GSS model to data with unequal time intervals, by embedding it within a state-space version of the Ornstein-Uhlenbeck process, a continuous-time model of an equilibrating stochastic system. Maximum likelihood and restricted maximum likelihood calculations for the Ornstein-Uhlenbeck state-space model involve only numerical maximization of an explicit multivariate normal likelihood, and so the extension allows for easy bootstrapping, yielding confidence intervals for model parameters, statistical hypothesis testing of density dependence, and selection among sub-models using information criteria. Ecologists and managers previously drawn to models lacking density dependence or observation error because such models accommodated unequal time intervals (for example, due to missing data) now have an alternative analysis framework incorporating density dependence, process noise and observation error. PMID:25230459
Institute of Scientific and Technical Information of China (English)
田兆青; 来新民; 林忠钦
2004-01-01
Dimensional quality is one of the most critical challenges in industries, which uses the multistage manufacturing process (MMP) such as assembly and machining for automotive and aerospace industries. According to investigations, fixture faults accounted for 72% of all the dimensional faults. Previous studies focused on only one fault or multiple faults occurred in one station or one fault in multiple stations, but these cases rarely appear in the real manufacturing. This paper presents a method for diagnosis of multiple fixture faults in the multi-station manufacturing process. The proposed method is based on the state space model of the MMP processes, which carries the information of the fixture layout geometry and sensor position. To identify the root cause, three continuous steps were used: a) development of the state space model and the construction of the statistics variables on offline mode, b) measurement of the coordinate measuring machines data on online mode and calculation of the statistics variables, and c) diagnostic algorithm for identifying the root cause. The presented paper integrates the state space model of the manufacturing processes and hypothesis test considering the impact of the measure noises. A case study verifies the proposed method.
On the State Space Geometry of the Kuramoto--Sivashinsky Flow in a Periodic Domain
Cvitanovic, Predrag; Davidchack, Ruslan L.; Siminos, Evangelos
2010-01-01
The continuous and discrete symmetries of the Kuramoto-Sivashinsky system restricted to a spatially periodic domain play a prominent role in shaping the invariant sets of its chaotic dynamics. The continuous spatial translation symmetry leads to relative equilibrium (traveling wave) and relative periodic orbit (modulated traveling wave) solutions. The discrete symmetries lead to existence of equilibrium and periodic orbit solutions, induce decomposition of state space into invariant subspaces, and enforce certain structurally stable heteroclinic connections between equilibria. We show, for the example of a particular small-cell Kuramoto-Sivashinsky system, how the geometry of its dynamical state space is organized by a rigid cage built by heteroclinic connections between equilibria, and demonstrate the preponderance of unstable relative periodic orbits and their likely role as the skeleton underpinning spatiotemporal turbulence in systems with continuous symmetries. We also offer novel visualizations of the high-dimensional Kuramoto-Sivashinsky state space flow through projections onto low-dimensional, PDE representation-independent, dynamically invariant intrinsic coordinate frames, as well as in terms of the physical, symmetry invariant energy transfer rates.
The phase response and state space of slow wave contractions in the small intestine.
Parsons, Sean P; Huizinga, Jan D
2017-09-01
What is the central question of this study? What are the dynamical rules governing interstitial cell of Cajal (ICC)-generated slow wave contractions in the small intestine, as reflected in their phase response curve and state space? What is the main finding and its importance? The phase response curve has a region of phase advance surrounding a phase delay peak. This pattern is important in generating a stable synchrony within the ICC network and is related to the state space of the ICC; in particular, the phase delay peak corresponds to the unstable equilibrium point that threads the ICC's limit cycle. Interstitial cells of Cajal (ICCs) generate electrical oscillations in the gut. Synchronization of the ICC population is required for generation of coherent electrical waves ('slow waves') that cause muscular contraction and thereby move gut content. The phase response curve (PRC) is an experimental measure of the dynamical rules governing a population of oscillators that determine their synchrony and gives an experimental window onto the state space of the oscillator, its dynamical landscape. We measured the PRC of slow wave contractions in the mouse small intestine by the novel combination of diameter mapping and single pulse electrical field stimulation. Phase change (τ) was measured as a function of old phase (ϕ) and distance from the stimulation electrode (d). Plots of τ(ϕ, d) showed an arrowhead-shaped region of phase advance enclosing at its base a phase delay peak. The phase change mirrored the perturbed pattern of contraction waves in response to a pulse. The (ϕ, d) plane is the surface of a displacement tube extending from the limit cycle through state space. To visualize the state space vector field on this tube, latent phase (ϕlat ) was calculated from τ. At the transition from advance to delay, isochrons made boomerang turns before tightening and winding around the phase delay peak corresponding to the unstable equilibrium point that threads the
Directory of Open Access Journals (Sweden)
Yap Hoon
2017-02-01
Full Text Available In this paper, a refined reference current generation algorithm based on instantaneous power (pq theory is proposed, for operation of an indirect current controlled (ICC three-level neutral-point diode clamped (NPC inverter-based shunt active power filter (SAPF under non-sinusoidal source voltage conditions. SAPF is recognized as one of the most effective solutions to current harmonics due to its flexibility in dealing with various power system conditions. As for its controller, pq theory has widely been applied to generate the desired reference current due to its simple implementation features. However, the conventional dependency on self-tuning filter (STF in generating reference current has significantly limited mitigation performance of SAPF. Besides, the conventional STF-based pq theory algorithm is still considered to possess needless features which increase computational complexity. Furthermore, the conventional algorithm is mostly designed to suit operation of direct current controlled (DCC SAPF which is incapable of handling switching ripples problems, thereby leading to inefficient mitigation performance. Therefore, three main improvements are performed which include replacement of STF with mathematical-based fundamental real power identifier, removal of redundant features, and generation of sinusoidal reference current. To validate effectiveness and feasibility of the proposed algorithm, simulation work in MATLAB-Simulink and laboratory test utilizing a TMS320F28335 digital signal processor (DSP are performed. Both simulation and experimental findings demonstrate superiority of the proposed algorithm over the conventional algorithm.
恒线速无卡旋切机变速进给自整定PID控制%Self-tuning PID Control of Variable Speed Feed for Log-core Veneer Lathe
Institute of Scientific and Technical Information of China (English)
杨素珍
2012-01-01
In current variable speed feed system of log-core veneer lathe, the uncertainty and interference can not be compensated by using open-loop control, the parameters are hard to determined and the controller is complicate by using closed-loop control. To deal with these problems, a composite controller combining feed forward and PID control with online parameters self-tuning based on single neural network is proposed. The structure of the controller is simple, the system interference is eliminated effectively, its stabilization time is short and its accuracy is high, the effectiveness of this controller is proved by the simulation result.%针对当前无卡旋切机的变速进给系统存在开环控制下无法补偿系统不确定性和干扰，闭环控制下控制器复杂且控制参数不易确定，提出一种结合前馈和基于单神经元网络在线参数自整定PID控制的复合控制器，该控制器结构简单，有效消除系统干扰，且收敛迅速，控制精度高，仿真结果表明该控制器的有效性。
Nadeem, Khurram; Moore, Jeffrey E; Zhang, Ying; Chipman, Hugh
2016-07-01
Stochastic versions of Gompertz, Ricker, and various other dynamics models play a fundamental role in quantifying strength of density dependence and studying long-term dynamics of wildlife populations. These models are frequently estimated using time series of abundance estimates that are inevitably subject to observation error and missing data. This issue can be addressed with a state-space modeling framework that jointly estimates the observed data model and the underlying stochastic population dynamics (SPD) model. In cases where abundance data are from multiple locations with a smaller spatial resolution (e.g., from mark-recapture and distance sampling studies), models are conventionally fitted to spatially pooled estimates of yearly abundances. Here, we demonstrate that a spatial version of SPD models can be directly estimated from short time series of spatially referenced distance sampling data in a unified hierarchical state-space modeling framework that also allows for spatial variance (covariance) in population growth. We also show that a full range of likelihood based inference, including estimability diagnostics and model selection, is feasible in this class of models using a data cloning algorithm. We further show through simulation experiments that the hierarchical state-space framework introduced herein efficiently captures the underlying dynamical parameters and spatial abundance distribution. We apply our methodology by analyzing a time series of line-transect distance sampling data for fin whales (Balaenoptera physalus) off the U.S. west coast. Although there were only seven surveys conducted during the study time frame, 1991-2014, our analysis detected presence of strong density regulation and provided reliable estimates of fin whale densities. In summary, we show that the integrative framework developed herein allows ecologists to better infer key population characteristics such as presence of density regulation and spatial variability in a
Bayesian state space models for inferring and predicting temporal gene expression profiles.
Liang, Yulan; Kelemen, Arpad
2007-12-01
Prediction of gene dynamic behavior is a challenging and important problem in genomic research while estimating the temporal correlations and non-stationarity are the keys in this process. Unfortunately, most existing techniques used for the inclusion of the temporal correlations treat the time course as evenly distributed time intervals and use stationary models with time-invariant settings. This is an assumption that is often violated in microarray time course data since the time course expression data are at unequal time points, where the difference in sampling times varies from minutes to days. Furthermore, the unevenly spaced short time courses with sudden changes make the prediction of genetic dynamics difficult. In this paper, we develop two types of Bayesian state space models to tackle this challenge for inferring and predicting the gene expression profiles associated with diseases. In the univariate time-varying Bayesian state space models we treat both the stochastic transition matrix and the observation matrix time-variant with linear setting and point out that this can easily be extended to nonlinear setting. In the multivariate Bayesian state space model we include temporal correlation structures in the covariance matrix estimations. In both models, the unevenly spaced short time courses with unseen time points are treated as hidden state variables. Bayesian approaches with various prior and hyper-prior models with MCMC algorithms are used to estimate the model parameters and hidden variables. We apply our models to multiple tissue polygenetic affymetrix data sets. Results show that the predictions of the genomic dynamic behavior can be well captured by the proposed models. (c) 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Chen, Jinsong; Hubbard, Susan S.; Williams, Kenneth H.; Pride, Steve; Li, Li; Steefel, Carl; Slater, Lee
2009-08-01
We develop a state-space Bayesian framework to combine time-lapse geophysical data with other types of information for quantitative estimation of biogeochemical parameters during bioremediation. We consider characteristics of end products of biogeochemical transformations as state vectors, which evolve under constraints of local environments through evolution equations, and consider time-lapse geophysical data as available observations, which could be linked to the state vectors through petrophysical models. We estimate the state vectors and their associated unknown parameters over time using Markov chain Monte Carlo sampling methods. To demonstrate the use of the state-space approach, we apply it to complex resistivity data collected during laboratory column biostimulation experiments that were poised to precipitate iron and zinc sulfides during sulfate reduction. We develop a petrophysical model based on sphere-shaped cells to link the sulfide precipitate properties to the time-lapse geophysical attributes and estimate volume fraction of the sulfide precipitates, fraction of the dispersed, sulfide-encrusted cells, mean radius of the aggregated clusters, and permeability over the course of the experiments. Results of the case study suggest that the developed state-space approach permits the use of geophysical data sets for providing quantitative estimates of end-product characteristics and hydrological feedbacks associated with biogeochemical transformations. Although tested here on laboratory column experiment data sets, the developed framework provides the foundation needed for quantitative field-scale estimation of biogeochemical parameters over space and time using direct, but often sparse wellbore data with indirect, but more spatially extensive geophysical data sets.
Recursive prediction error methods for online estimation in nonlinear state-space models
Directory of Open Access Journals (Sweden)
Dag Ljungquist
1994-04-01
Full Text Available Several recursive algorithms for online, combined state and parameter estimation in nonlinear state-space models are discussed in this paper. Well-known algorithms such as the extended Kalman filter and alternative formulations of the recursive prediction error method are included, as well as a new method based on a line-search strategy. A comparison of the algorithms illustrates that they are very similar although the differences can be important for the online tracking capabilities and robustness. Simulation experiments on a simple nonlinear process show that the performance under certain conditions can be improved by including a line-search strategy.
Equilibrium points of the tilted perfect fluid Bianchi VIh state space
Apostolopoulos, Pantelis S.
2005-05-01
We present the full set of evolution equations for the spatially homogeneous cosmologies of type VIh filled with a tilted perfect fluid and we provide the corresponding equilibrium points of the resulting dynamical state space. It is found that only when the group parameter satisfies h > -1 a self-similar solution exists. In particular we show that for h > -{1/9} there exists a self-similar equilibrium point provided that γ ∈ ({2(3+sqrt{-h})/5+3sqrt{-h}},{3/2}) whereas for h VIh.
Testing for Level Shifts in Fractionally Integrated Processes: a State Space Approach
DEFF Research Database (Denmark)
Monache, Davide Delle; Grassi, Stefano; Santucci de Magistris, Paolo
Short memory models contaminated by level shifts have similar long-memory features as fractionally integrated processes. This makes hard to verify whether the true data generating process is a pure fractionally integrated process when employing standard estimation methods based...... on the autocorrelation function or the periodogram. In this paper, we propose a robust testing procedure, based on an encompassing parametric specification that allows to disentangle the level shifts from the fractionally integrated component. The estimation is carried out on the basis of a state-space methodology...
Markov chain Monte Carlo methods for state-space models with point process observations.
Yuan, Ke; Girolami, Mark; Niranjan, Mahesan
2012-06-01
This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations. We quantified the efficiencies of these MCMC methods on synthetic data, and our results suggest that the Reimannian manifold Hamiltonian Monte Carlo method offers the best performance. We further compared such a method with a previously tested variational Bayes method on two experimental data sets. Results indicate similar performance on the large data sets and superior performance on small ones. The work offers an extensive suite of MCMC algorithms evaluated on an important class of models for physiological signal analysis.
On observation distributions for state space models of population survey data.
Knape, Jonas; Jonzén, Niclas; Sköld, Martin
2011-11-01
1. State space models are starting to replace more simple time series models in analyses of temporal dynamics of populations that are not perfectly censused. By simultaneously modelling both the dynamics and the observations, consistent estimates of population dynamical parameters may be obtained. For many data sets, the distribution of observation errors is unknown and error models typically chosen in an ad-hoc manner. 2. To investigate the influence of the choice of observation error on inferences, we analyse the dynamics of a replicated time series of red kangaroo surveys using a state space model with linear state dynamics. Surveys were performed through aerial counts and Poisson, overdispersed Poisson, normal and log-normal distributions may all be adequate for modelling observation errors for the data. We fit each of these to the data and compare them using AIC. 3. The state space models were fitted with maximum likelihood methods using a recent importance sampling technique that relies on the Kalman filter. The method relaxes the assumption of Gaussian observation errors required by the basic Kalman filter. Matlab code for fitting linear state space models with Poisson observations is provided. 4. The ability of AIC to identify the correct observation model was investigated in a small simulation study. For the parameter values used in the study, without replicated observations, the correct observation distribution could sometimes be identified but model selection was prone to misclassification. On the other hand, when observations were replicated, the correct distribution could typically be identified. 5. Our results illustrate that inferences may differ markedly depending on the observation distributions used, suggesting that choosing an adequate observation model can be critical. Model selection and simulations show that for the models and parameter values in this study, a suitable observation model can typically be identified if observations are
Input Harmonic Analysis on the Slim DC-Link Drive Using Harmonic State Space Model
DEFF Research Database (Denmark)
Yang, Feng; Kwon, Jun Bum; Wang, Xiongfei
2017-01-01
the shortcomings of the present harmonic analysis methods, such as the time-domain simulation, or the Fourier analysis, this paper proposes a Harmonic State Space model to study the harmonics performance for this type of drive. In this study, this model is utilized to describe the behavior of the harmonic...... variation according to the switching instant, the harmonics at the steady-state condition, as well as the coupling between the multiple harmonic impedances. By using this model, the impaction on the harmonics performance by the film capacitor and the grid inductance is derived. Simulation and experimental...
Directory of Open Access Journals (Sweden)
Mohammad Shahzad
2016-05-01
Full Text Available This study deals with the control of chaotic dynamics of tumor cells, healthy host cells, and effector immune cells in a chaotic Three Dimensional Cancer Model (TDCM by State Space Exact Linearization (SSEL technique based on Lie algebra. A non-linear feedback control law is designed which induces a coordinate transformation thereby changing the original chaotic TDCM system into a controlled one linear system. Numerical simulation has been carried using Mathematica that witness the robustness of the technique implemented on the chosen chaotic system.
Design and performance evaluation of a state-space based AQM
Ariba, Yassine; Gouaisbaut, Frédéric; 10.1109/CTRQ.2008.15
2009-01-01
Recent research has shown the link between congestion control in communication networks and feedback control system. In this paper, the design of an active queue management (AQM) which can be viewed as a controller, is considered. Based on a state space representation of a linearized fluid flow model of TCP, the AQM design is converted to a state feedback synthesis problem for time delay systems. Finally, an example extracted from the literature and simulations via a network simulator NS (under cross traffic conditions) support our study.
Research of united model of knowledge discovery state space and its application
Institute of Scientific and Technical Information of China (English)
You Fucheng; Song Wei; Yang Bingru
2005-01-01
There are both associations and differences between structured and unstructured data mining. How to unite them together to be a united theoretical framework and to guide the research of knowledge discovery and data mining has become an urgent problem to be solved. On the base of analysis and study of existing research results, the united model of knowledge discovery state space (UMKDSS) is presented, and the structured data mining and the complex type data mining are associated together. UMKDSS can provide theoretical guidance for complex type data mining. An application example of UMKDSS is given at last.
Forecasting the Global Mean Sea Level, a Continuous-Time State-Space Approach
DEFF Research Database (Denmark)
Boldrini, Lorenzo
In this paper we propose a continuous-time, Gaussian, linear, state-space system to model the relation between global mean sea level (GMSL) and the global mean temperature (GMT), with the aim of making long-term projections for the GMSL. We provide a justification for the model specification based......) and the temperature reconstruction from Hansen et al. (2010). We compare the forecasting performance of the proposed specification to the procedures developed in Rahmstorf (2007b) and Vermeer and Rahmstorf (2009). Finally, we compute projections for the sea-level rise conditional on the 21st century SRES temperature...
State-space approach to vibration of gold nano-beam induced by ramp type heating
Institute of Scientific and Technical Information of China (English)
Hamdy M Youssef; Khaled A Elsibai
2010-01-01
In the nanoscale beam, two effects become domineering. One is the non-Fourier effect in heat conduction and the other is the coupling effect between temperature and strain rate. In the present study, a generalized solution for the generalized thermoelastic vibration of gold nano-beam resonator induced by ramp type heating is developed. The solution takes into account the above two effects. State-space and Laplace transform methods are used to determine the lateral vibration, the temperature, the displacement, the stress and the strain energy of the beam. The effects of the relaxation time and the ramping time parameters have been studied.
You Pretty Little Flocker: Exploring the Aesthetic State Space of Creative Ecosystems.
Eldridge, Alice
2015-01-01
Artificial life models constitute a rich compendium of tools for the generative arts; complex, self-organizing, emergent behaviors have great interactive and generative potential. But how can we go beyond simply visualizing scientific simulations and manipulate these models for use in design and creative art contexts? You Pretty Little Flocker is a proof-of-concept study in expanding and exploring the aesthetic state space of a model for generative design. A modified version of Reynolds' flocking algorithm (1987) is described in which the space of possible images is extended and navigable in a way that at once provides user control and maintains generative autonomy.
State-space geometry, non-extremal black holes and Kaluza-Klein monopoles
Bellucci, Stefano
2012-01-01
We examine the statistical nature of the charged anticharged non-extremal black holes in string theory. From the perspective of the intrinsic Riemannian Geometry, the first principle of the statistical mechanics shows that the stability properties of general nonextremal nonlarge charged black brane solutions are divulged from the positivity of the corresponding principle minors of the space-state metric tensor. Under the addition of the Kaluza-Klein monopoles, a novel aspect of the Gaussian fluctuations demonstrates that the canonical fluctuations can be ascertained without any approximation. We offer the state-space geometric implication for the most general non-extremal black brane configurations in string theory.
A Beddoes-Leishman type dynamic stall model in state-space and indicial formulations
DEFF Research Database (Denmark)
Hansen, M.H.; Gaunaa, Mac; Aagaard Madsen, Helge
2004-01-01
This report contains a description of a Beddoes-Leishman type dynamic stall model in both a state-space and an indicial function formulation. The model predicts the unsteady aerodynamic forces and moment on an airfoil section undergoing arbitrary motionin heave, lead-lag, and pitch. The model...... features, such as overshoot of the lift, in the stall region. The linearized model is shown to give identicalresults to the full model for small amplitude oscillations. Furthermore, it is shown that the response of finite thichkness airfoils can be reproduced to a high accuracy by the use of specific...
A SAS/IML program using the Kalman filter for estimating state space models.
Gu, Fei; Yung, Yiu-Fai
2013-03-01
To help disseminate the knowledge and software implementation of a state space model (SSM), this article provides a SAS/IML (SAS Institute, 2010) program for estimating the parameters of general linear Gaussian SSMs using the Kalman filter algorithm. In order to use this program, the user should have SAS installed on a computer and have a valid license for SAS/IML. Since the code is completely open, it is expected that this program can be used not only by applied researchers, but also by quantitative methodologists who are interested in improving their methods and promoting SSM as a research instrument.
Bizon, Nicu; Mahdavi Tabatabaei, Naser
2014-01-01
This book explains and analyzes the dynamic performance of linear and nonlinear systems, particularly for Power Systems including Hybrid Power Sources. Offers a detailed description of system stability using state space energy conservation principle, and more.
Bayesian State-Space Modelling on High-Performance Hardware Using LibBi
Directory of Open Access Journals (Sweden)
Lawrence M. Murray
2015-10-01
Full Text Available LibBi is a software package for state space modelling and Bayesian inference on modern computer hardware, including multi-core central processing units, many-core graphics processing units, and distributed-memory clusters of such devices. The software parses a domain-specific language for model specification, then optimizes, generates, compiles and runs code for the given model, inference method and hardware platform. In presenting the software, this work serves as an introduction to state space models and the specialized methods developed for Bayesian inference with them. The focus is on sequential Monte Carlo (SMC methods such as the particle filter for state estimation, and the particle Markov chain Monte Carlo and SMC2 methods for parameter estimation. All are well-suited to current computer hardware. Two examples are given and developed throughout, one a linear three-element windkessel model of the human arterial system, the other a nonlinear Lorenz '96 model. These are specified in the prescribed modelling language, and LibBi demonstrated by performing inference with them. Empirical results are presented, including a performance comparison of the software with different hardware configurations.
Reliability Analysis of a 3-Machine Power Station Using State Space Approach
Directory of Open Access Journals (Sweden)
WasiuAkande Ahmed
2014-07-01
Full Text Available With the advent of high-integrity fault-tolerant systems, the ability to account for repairs of partially failed (but still operational systems become increasingly important. This paper presents a systemic method of determining the reliability of a 3-machine electric power station, taking into consideration the failure rates and repair rates of the individual component (machine that make up the system. A state-space transition process for a 3-machine with 23 states was developed and consequently, steady state equations were generated based on Markov mathematical modeling of the power station. Important reliability components were deduced from this analysis. This research simulation was achieved with codes written in Excel® -VBA programming environment. System reliability using state space approach proofs to be a viable and efficient technique of reliability prediction as it is able to predict the state of the system under consideration. For the purpose of neatness and easy entry of data, Graphic User Interface (GUI was designed.
A state space representation of VAR models with sparse learning for dynamic gene networks.
Kojima, Kaname; Yamaguchi, Rui; Imoto, Seiya; Yamauchi, Mai; Nagasaki, Masao; Yoshida, Ryo; Shimamura, Teppei; Ueno, Kazuko; Higuchi, Tomoyuki; Gotoh, Noriko; Miyano, Satoru
2010-01-01
We propose a state space representation of vector autoregressive model and its sparse learning based on L1 regularization to achieve efficient estimation of dynamic gene networks based on time course microarray data. The proposed method can overcome drawbacks of the vector autoregressive model and state space model; the assumption of equal time interval and lack of separation ability of observation and systems noises in the former method and the assumption of modularity of network structure in the latter method. However, in a simple implementation the proposed model requires the calculation of large inverse matrices in a large number of times during parameter estimation process based on EM algorithm. This limits the applicability of the proposed method to a relatively small gene set. We thus introduce a new calculation technique for EM algorithm that does not require the calculation of inverse matrices. The proposed method is applied to time course microarray data of lung cells treated by stimulating EGF receptors and dosing an anticancer drug, Gefitinib. By comparing the estimated network with the control network estimated using non-treated lung cells, perturbed genes by the anticancer drug could be found, whose up- and down-stream genes in the estimated networks may be related to side effects of the anticancer drug.
An Investigation of State-Space Model Fidelity for SSME Data
Martin, Rodney Alexander
2008-01-01
In previous studies, a variety of unsupervised anomaly detection techniques for anomaly detection were applied to SSME (Space Shuttle Main Engine) data. The observed results indicated that the identification of certain anomalies were specific to the algorithmic method under consideration. This is the reason why one of the follow-on goals of these previous investigations was to build an architecture to support the best capabilities of all algorithms. We appeal to that goal here by investigating a cascade, serial architecture for the best performing and most suitable candidates from previous studies. As a precursor to a formal ROC (Receiver Operating Characteristic) curve analysis for validation of resulting anomaly detection algorithms, our primary focus here is to investigate the model fidelity as measured by variants of the AIC (Akaike Information Criterion) for state-space based models. We show that placing constraints on a state-space model during or after the training of the model introduces a modest level of suboptimality. Furthermore, we compare the fidelity of all candidate models including those embodying the cascade, serial architecture. We make recommendations on the most suitable candidates for application to subsequent anomaly detection studies as measured by AIC-based criteria.
Population dynamics of an Arctiid caterpillar-tachinid parasitoid system using state-space models.
Karban, Richard; de Valpine, Perry
2010-05-01
1. Population dynamics of insect host-parasitoid systems are important in many natural and managed ecosystems and have inspired much ecological theory. However, ecologists have a limited knowledge about the relative strengths of species interactions, abiotic effects and density dependence in natural host-parasitoid dynamics. Statistical time-series analyses would be more informative by incorporating multiple factors, measurement error and noisy dynamics. 2. We use a novel maximum likelihood and model-selection analysis of a state-space model for host-parasitoid dynamics to examine 21 years of annual census data for woolly bear caterpillars (Platyprepia virginalis) and their locally host-specific tachinid parasitoids (Thelaira americana). 3. Caterpillar densities varied by three orders of magnitude and were driven by density dependence and precipitation from the previous March but not detectably by parasitoids, despite variable and sometimes high (>50%) parasitism. 4. Fly fluctuations, as estimated from per cent parasitism, were affected by density dependence and precipitation from the previous July. There was marginal evidence that host abundance drives fly fluctuations as a generic linear effect but no evidence for classical Nicholson-Bailey coupling. 5. The state-space model analysis includes new methods for likelihood calculation and allows a balanced consideration of effect magnitude and statistical significance in a nonlinear model with multiple alternative explanatory variables.
Cointegration between trends and their estimators in state space models and CVAR models
DEFF Research Database (Denmark)
Johansen, Søren; Tabor, Morten Nyboe
In a linear state space model, y_{t+1}=BT_{t}+eps_{t+1}, we investigate if the unobserved trend, T_{t}, cointegrates with the extracted trend E_{t}T_{t}, and with the estimated trend E^_{t}T_{t}, in the sense that the spreads T_{t}-E_{t}T_{t} and E_{t}T_{t}-E^_{t}T_{t} are stationary. We find...... that this result holds for BT_{t}-BE_{t}T_{t} and BE_{t}T_{t}-B^E^_{t}T_{t}. For the trends T_{t} and E^_{t}T_{t}, however, this type cointegration depends on the identification of B and T_{t}. The same results are found, if the observations, y_{t}, from the state space model are analysed using a cointegrated...... vector autoregressive model, where the trend is defined as the common trend. Finally we investigate cointegration between trends and their estimators based on the two models, and find the same results. We illustrate with two examples and confirm the results by a small simulation study....
Modeling State Space Search Technique for a Real World Adversarial Problem Solving
Directory of Open Access Journals (Sweden)
Kester O. Omoregie
2015-02-01
Full Text Available In problem solving, there is a search for the appropriate solution. A state space is a problem domain consisting of the start state, the goal state and the operations that will necessitate the various moves from the start state to the goal state. Each move operation takes one away from the start state and closer to the goal state. In this work we have attempted implementing this concept in adversarial problem solving, which is a more complex problem space. We noted that real world adversarial problems vary in their types and complexities, and therefore solving an adversarial problem would depend on the nature of the adversarial problem itself. Specifically, we examined a real world case, "the prisoner's dilemma" which is a critical, mutually independent, decision making adversarial problem. We combined the idea of the Thagard's Theory of Explanatory Coherence (TEC with Bayes' theorem of conditional probability to construct the model of an opponent that includes the opponent's model of the agent. A further conversion of the model into a series of state space structures led us into the use of breadth-first search strategy to arrive at our decision goal.
Rate control system algorithm developed in state space for models with parameter uncertainties
Directory of Open Access Journals (Sweden)
Adilson Jesus Teixeira
2011-09-01
Full Text Available Researching in weightlessness above the atmosphere needs a payload to carry the experiments. To achieve the weightlessness, the payload uses a rate control system (RCS in order to reduce the centripetal acceleration within the payload. The rate control system normally has actuators that supply a constant force when they are turned on. The development of an algorithm control for this rate control system will be based on the minimum-time problem method in the state space to overcome the payload and actuators dynamics uncertainties of the parameters. This control algorithm uses the initial conditions of optimal trajectories to create intermediate points or to adjust existing points of a switching function. It associated with inequality constraint will form a decision function to turn on or off the actuators. This decision function, for linear time-invariant systems in state space, needs only to test the payload state variables instead of spent effort in solving differential equations and it will be tuned in real time to the payload dynamic. It will be shown, through simulations, the results obtained for some cases of parameters uncertainties that the rate control system algorithm reduced the payload centripetal acceleration below μg level and keep this way with no limit cycle.
Bayesian State-Space Modelling on High-Performance Hardware Using LibBi
Directory of Open Access Journals (Sweden)
Lawrence M. Murray
2015-10-01
Full Text Available LibBi is a software package for state space modelling and Bayesian inference on modern computer hardware, including multi-core central processing units, many-core graphics processing units, and distributed-memory clusters of such devices. The software parses a domain-specific language for model specification, then optimizes, generates, compiles and runs code for the given model, inference method and hardware platform. In presenting the software, this work serves as an introduction to state space models and the specialized methods developed for Bayesian inference with them. The focus is on sequential Monte Carlo (SMC methods such as the particle filter for state estimation, and the particle Markov chain Monte Carlo and SMC2 methods for parameter estimation. All are well-suited to current computer hardware. Two examples are given and developed throughout, one a linear three-element windkessel model of the human arterial system, the other a nonlinear Lorenz '96 model. These are specified in the prescribed modelling language, and LibBi demonstrated by performing inference with them. Empirical results are presented, including a performance comparison of the software with different hardware configurations.
Liu, Peipei; Sohn, Hoon; Park, Byeongjin
2015-06-01
Damage often causes a structural system to exhibit severe nonlinear behaviors, and the resulting nonlinear features are often much more sensitive to the damage than their linear counterparts. This study develops a laser nonlinear wave modulation spectroscopy (LNWMS) so that certain types of damage can be detected without any sensor placement. The proposed LNWMS utilizes a pulse laser to generate ultrasonic waves and a laser vibrometer for ultrasonic measurement. Under the broadband excitation of the pulse laser, a nonlinear source generates modulations at various frequency values due to interactions among various input frequency components. State space attractors are reconstructed from the ultrasonic responses measured by LNWMS, and a damage feature called Bhattacharyya distance (BD) is computed from the state space attractors to quantify the degree of damage-induced nonlinearity. By computing the BD values over the entire target surface using laser scanning, damage can be localized and visualized without relying on the baseline data obtained from the pristine condition of a target structure. The proposed technique has been successfully used for visualizing fatigue crack in an aluminum plate and delamination and debonding in a glass fiber reinforced polymer wind turbine blade.
State space orderings for Gauss-Seidel in Markov chains revisited
Energy Technology Data Exchange (ETDEWEB)
Dayar, T. [Bilkent Univ., Ankara (Turkey)
1996-12-31
Symmetric state space orderings of a Markov chain may be used to reduce the magnitude of the subdominant eigenvalue of the (Gauss-Seidel) iteration matrix. Orderings that maximize the elemental mass or the number of nonzero elements in the dominant term of the Gauss-Seidel splitting (that is, the term approximating the coefficient matrix) do not necessarily converge faster. An ordering of a Markov chain that satisfies Property-R is semi-convergent. On the other hand, there are semi-convergent symmetric state space orderings that do not satisfy Property-R. For a given ordering, a simple approach for checking Property-R is shown. An algorithm that orders the states of a Markov chain so as to increase the likelihood of satisfying Property-R is presented. The computational complexity of the ordering algorithm is less than that of a single Gauss-Seidel iteration (for sparse matrices). In doing all this, the aim is to gain an insight for faster converging orderings. Results from a variety of applications improve the confidence in the algorithm.
Directory of Open Access Journals (Sweden)
Esfandiar, H.
2013-05-01
Full Text Available In this paper, based on the VoigtKelvin constitutive model, nonlinear dynamic modelling and state space representation of a viscoelastic beam acting as a flexible robotic manipulator is investigated. Complete nonlinear dynamic modelling of a viscoelastic beam without premature linearisation of dynamic equations is developed. The adopted method is capable of reproducing nonlinear dynamic effects, such as beam stiffening due to centrifugal and Coriolis forces induced by rotation of the joints. Structural damping effects on the models dynamic behaviour are also shown. A reliable model for a viscoelastic beam is subsequently presented. The governing equations of motion are derived using Hamiltons principle, and using the finite difference method, nonlinear partial differential equations are reduced to ordinary differential equations. For the purpose of flexible manipulator control, the standard form of state space equations for the viscoelastic link and the actuator is obtained. Simulation results indicate substantial improvements in dynamic behaviour, and a parameter sensitivity study is carried out to investigate the effect of structural damping on the vibration amplitude.
Ensemble Kalman Filtering with a Divided State-Space Strategy for Coupled Data Assimilation Problems
Luo, Xiaodong
2014-12-01
This study considers the data assimilation problem in coupled systems, which consists of two components (subsystems) interacting with each other through certain coupling terms. A straightforward way to tackle the assimilation problem in such systems is to concatenate the states of the subsystems into one augmented state vector, so that a standard ensemble Kalman filter (EnKF) can be directly applied. This work presents a divided state-space estimation strategy, in which data assimilation is carried out with respect to each individual subsystem, involving quantities from the subsystem itself and correlated quantities from other coupled subsystems. On top of the divided state-space estimation strategy, the authors also consider the possibility of running the subsystems separately. Combining these two ideas, a few variants of the EnKF are derived. The introduction of these variants is mainly inspired by the current status and challenges in coupled data assimilation problems and thus might be of interest from a practical point of view. Numerical experiments with a multiscale Lorenz 96 model are conducted to evaluate the performance of these variants against that of the conventional EnKF. In addition, specific for coupled data assimilation problems, two prototypes of extensions of the presented methods are also developed in order to achieve a trade-offbetween efficiency and accuracy.
Jonsen, Ian D; Myers, Ransom A; James, Michael C
2006-09-01
1. Biological and statistical complexity are features common to most ecological data that hinder our ability to extract meaningful patterns using conventional tools. Recent work on implementing modern statistical methods for analysis of such ecological data has focused primarily on population dynamics but other types of data, such as animal movement pathways obtained from satellite telemetry, can also benefit from the application of modern statistical tools. 2. We develop a robust hierarchical state-space approach for analysis of multiple satellite telemetry pathways obtained via the Argos system. State-space models are time-series methods that allow unobserved states and biological parameters to be estimated from data observed with error. We show that the approach can reveal important patterns in complex, noisy data where conventional methods cannot. 3. Using the largest Atlantic satellite telemetry data set for critically endangered leatherback turtles, we show that the diel pattern in travel rates of these turtles changes over different phases of their migratory cycle. While foraging in northern waters the turtles show similar travel rates during day and night, but on their southward migration to tropical waters travel rates are markedly faster during the day. These patterns are generally consistent with diving data, and may be related to changes in foraging behaviour. Interestingly, individuals that migrate southward to breed generally show higher daytime travel rates than individuals that migrate southward in a non-breeding year. 4. Our approach is extremely flexible and can be applied to many ecological analyses that use complex, sequential data.
Hwang, Chyi; Guo, Tong-Yi; Shieh, Leang-San
1991-01-01
A canonical state-space realization based on the multipoint Jordan continued-fraction expansion (CFE) is presented for single-input-single-output (SISO) systems. The similarity transformation matrix which relates the new canonical form to the phase-variable canonical form is also derived. The presented canonical state-space representation is particularly attractive for the application of SISO system theory in which a reduced-dimensional time-domain model is necessary.
永磁伺服电机模糊 PID 自整定 SVPWM 控制研究%Fuzzy PID self-tuning SVPWM control research of PMSM
Institute of Scientific and Technical Information of China (English)
马立新; 范洪成; 黄阳龙
2016-01-01
针对永磁同步电机自身的非线性、强耦合性以及时变性特点，以及传统P ID控制策略不能跟随系统参数的变化而自动做出整定等问题。通过对模糊理论分析，本文提出了一种简单实用的永磁同步电机控制策略，即模糊PID自整定SVPWM控制方式。采取SVPWM 的方式产生三相电流驱动电机，通过模糊逻辑语句建立了模糊控制规则，并实现与 PID 控制参数相结合，实现实时改变电机控制参数功能，并利用 MATLAB工具建立了模糊 PID 自整定SVPWM闭环矢量控制系统仿真模型。仿真结果表明：系统转速实现无超调，响应速度和扰动恢复时间与传统PID控制方式相比缩短了一半。该方法提高了永磁交流伺服系统的控制精度，具有良好的动静态性能，在工程应用上提供了一种简单、易实现的控制方法。%Aiming at the nonlinear ,strong coupling and time-varying characteristics of permanent magnet synchronous motor and the traditional PID control strategy can't follow the change of system parameters and automatically make the corresponding setting .Through the analysis of fuzzy control ,this paper puts forward a simple and practical control strategy of permanent magnet synchronous motor ,namely fuzzy PID self-tuning SVPWM control .Adopt the method of SVPWM produce three phase current drive motor , the fuzzy control rule was established based on fuzzy logic statements ,and combined with PID control parameters ,real-time change motor control parameters of the function ,and using Matlab tools to establish the fuzzy self-tuning PID SVPWM closed-loop vector control system simulation model . The simulation results show that the system speed to realize no overshoot ,response speed and disturbance recovery time shortened by half compared with the traditional PID control method .The method to improve the control precision of the permanent magnet ac servo system ,has a good dynamic and static
Institute of Scientific and Technical Information of China (English)
杜恩利; 何礼高; 李旭; 马彦林
2011-01-01
A significant problem with diode neutral point clamped three-level inverter is the fluctuation in the neutral-point voltage.The causes of the neutral point potential unbalance based on space vector pulse width modulation are analyzed. And then a strategy based on parameters self-tuning fuzzy logic control is presented which could solve the problems of some commonly used control strategies. For time-variable nonlinear system,this algorithm has better robustness and adaptability.Also there are small static and dynamic error in controlling of the neutral point voltage.The effectiveness of the proposed control approach is verified by simulation and experimental results.%中点电位平衡对二极管箝位三电平逆变器而言非常重要.在此深入分析了空间矢量调制中引起中点电位偏移的原因,提出了基于参数自整定模糊控制的中点平衡控制策略.该算法对三电平逆变器中点电位这一非线性时变对象有较好的适应性和鲁棒性,其静态及动态误差都能控制在较低范围,达到中点电位平衡的效果.仿真和实验结果证明了该算法的正确性和有效性.
Institute of Scientific and Technical Information of China (English)
郝少杰; 方康玲
2011-01-01
工业温度控制系统具有非线性、时变性和滞后性等特性,严重影响温度控制的快速性和准确性,为了解决常规PID参数调节在温度控制中适应性差,调节效果不理想的问题,这里采用了模糊PID参数自整定控制方法,用模糊控制规则对PID参数进行修改,利用Matlab的Simulink仿真工具箱做了常规PID与模糊PID的仿真对比试验.仿真结果表明,模糊PID参数自整定控制效果在超调量和调节时间上都小于常规PID,提高系统快速性和准确性,改善了温度系统动态性能.%The industry temperature control system has features of nonlinear, time-varying and hysteretic, which seriously affected the speed and accuracy of the temperature control. In order to solve the less adaptable regulation of conventional PID parameter adjustment in the temperature control, a parameter self-tuning control method of fuzzy PID is adopted. The method uses fuzzy control rules to modify the PID parameters and utilizes the Simulink simulation toolbox of Matlab to make a contrast test between the conventional PID and fuzzy PID. Simulation results show that the control effect of parameter selftuning of fuzzy PID is better than conventional PID in the overshoot and settling time, and the system speed, accuracy and dynamic performance are improved.
Beatty, William; Jay, Chadwick V.; Fischbach, Anthony S.
2016-01-01
State-space models offer researchers an objective approach to modeling complex animal location data sets, and state-space model behavior classifications are often assumed to have a link to animal behavior. In this study, we evaluated the behavioral classification accuracy of a Bayesian state-space model in Pacific walruses using Argos satellite tags with sensors to detect animal behavior in real time. We fit a two-state discrete-time continuous-space Bayesian state-space model to data from 306 Pacific walruses tagged in the Chukchi Sea. We matched predicted locations and behaviors from the state-space model (resident, transient behavior) to true animal behavior (foraging, swimming, hauled out) and evaluated classification accuracy with kappa statistics (κ) and root mean square error (RMSE). In addition, we compared biased random bridge utilization distributions generated with resident behavior locations to true foraging behavior locations to evaluate differences in space use patterns. Results indicated that the two-state model fairly classified true animal behavior (0.06 ≤ κ ≤ 0.26, 0.49 ≤ RMSE ≤ 0.59). Kernel overlap metrics indicated utilization distributions generated with resident behavior locations were generally smaller than utilization distributions generated with true foraging behavior locations. Consequently, we encourage researchers to carefully examine parameters and priors associated with behaviors in state-space models, and reconcile these parameters with the study species and its expected behaviors.
DEFF Research Database (Denmark)
Bach, Christian; Christensen, Bent Jesper
We include simultaneously both realized volatility measures based on high-frequency asset returns and implied volatilities backed out of individual traded at the money option prices in a state space approach to the analysis of true underlying volatility. We model integrated volatility as a latent...... fi…rst order Markov process and show that our model is closely related to the CEV and Barndorff-Nielsen & Shephard (2001) models for local volatility. We show that if measurement noise in the observable volatility proxies is not accounted for, then the estimated autoregressive parameter in the latent...... process is downward biased. Implied volatility performs better than any of the alternative realized measures when forecasting future integrated volatility. The results are largely similar across the stock market (S&P 500), bond market (30-year U.S. T-bond), and foreign currency exchange market ($/£ )....
Modeling fuzzy state space of reheater system for simulation and analysis
Munirah, W. M. Wan; Ahmad, T.; Ashaari, A.; Abdullah, M. Adib
2014-07-01
Reheater is one of the important heat exchange components in a high capacity power plant of a boiler system. The aim of this study is to improve heat transfer of a reheater system. The method is to maximize steam production and at the same time, keeping variables within constraints. Fuzzy arithmetic is a powerful tool used to solve engineering problems with uncertain parameters. Therefore, in order to determine heat transfer efficiency, the state space of reheater is simulated using fuzzy arithmetic by taking into account the uncertainties in the reheater system. The uncertain model parameters and the model inputs are represented by fuzzy numbers with their shape derived from quasi-Gaussian function. Finally, this paper discusses how the mathematical model can be manipulated in order to produce maximum heat transfer with least loss of energy. Furthermore, the improvement of the reheater efficiency and the quantification of the heat supplied parameters are presented in this paper.
Projective Limits of State Spaces: Quantum Field Theory without a Vacuum
Lanéry, Suzanne
2016-01-01
Instead of formulating the states of a Quantum Field Theory (QFT) as density matrices over a single large Hilbert space, it has been proposed by Kijowski [Kijowski, 1977] to construct them as consistent families of partial density matrices, the latter being defined over small 'building block' Hilbert spaces. In this picture, each small Hilbert space can be physically interpreted as extracting from the full theory specific degrees of freedom. This allows to reduce the quantization of a classical field theory to the quantization of finite-dimensional sub-systems, thus sidestepping some of the common ambiguities (specifically, the issues revolving around the choice of a 'vacuum state'), while obtaining robust and well-controlled quantum states spaces. The present letter provides a self-contained introduction to this formalism, detailing its motivations as well as its relations to other approaches to QFT (such as conventional Fock-like Hilbert spaces, path-integral quantization, and the algebraic formulation). At...
An optical flow-based state-space model of the vocal folds
DEFF Research Database (Denmark)
Granados, Alba; Brunskog, Jonas
2017-01-01
. A linear and Gaussian nonstationary state-space model is proposed and thoroughly discussed. The evolution model is based on a self-sustained three-dimensional finite element model of the vocal folds, and the observation model involves a dense optical flow algorithm. The results show that the method is able......High-speed movies of the vocal fold vibration are valuable data to reveal vocal fold features for voice pathology diagnosis. This work presents a suitable Bayesian model and a purely theoretical discussion for further development of a framework for continuum biomechanical features estimation...... to capture different deformation patterns between the computed optical flow and the finite element deformation, controlled by the choice of the model tissue parameters....
Maximum efficiency of state-space models of nanoscale energy conversion devices.
Einax, Mario; Nitzan, Abraham
2016-07-07
The performance of nano-scale energy conversion devices is studied in the framework of state-space models where a device is described by a graph comprising states and transitions between them represented by nodes and links, respectively. Particular segments of this network represent input (driving) and output processes whose properly chosen flux ratio provides the energy conversion efficiency. Simple cyclical graphs yield Carnot efficiency for the maximum conversion yield. We give general proof that opening a link that separate between the two driving segments always leads to reduced efficiency. We illustrate these general result with simple models of a thermoelectric nanodevice and an organic photovoltaic cell. In the latter an intersecting link of the above type corresponds to non-radiative carriers recombination and the reduced maximum efficiency is manifested as a smaller open-circuit voltage.
Representing time-varying cyclic dynamics using multiple-subject state-space models.
Chow, Sy-Miin; Hamaker, Ellen L; Fujita, Frank; Boker, Steven M
2009-11-01
Over the last few decades, researchers have become increasingly aware of the need to consider intraindividual variability in the form of cyclic processes. In this paper, we review two contemporary cyclic state-space models: Young and colleagues' dynamic harmonic regression model and Harvey and colleagues' stochastic cycle model. We further derive the analytic equivalence between the two models, discuss their unique strengths and propose multiple-subject extensions. Using data from a study on human postural dynamics and a daily affect study, we demonstrate the use of these models to represent within-person non-stationarities in cyclic dynamics and interindividual differences therein. The use of diagnostic tools for evaluating model fit is also illustrated.
Online variational inference for state-space models with point-process observations.
Mangion, Andrew Zammit; Yuan, Ke; Kadirkamanathan, Visakan; Niranjan, Mahesan; Sanguinetti, Guido
2011-08-01
We present a variational Bayesian (VB) approach for the state and parameter inference of a state-space model with point-process observations, a physiologically plausible model for signal processing of spike data. We also give the derivation of a variational smoother, as well as an efficient online filtering algorithm, which can also be used to track changes in physiological parameters. The methods are assessed on simulated data, and results are compared to expectation-maximization, as well as Monte Carlo estimation techniques, in order to evaluate the accuracy of the proposed approach. The VB filter is further assessed on a data set of taste-response neural cells, showing that the proposed approach can effectively capture dynamical changes in neural responses in real time.
State space modeling of reactor core in a pressurized water reactor
Ashaari, A.; Ahmad, T.; Shamsuddin, Mustaffa; M, Wan Munirah W.; Abdullah, M. Adib
2014-07-01
The power control system of a nuclear reactor is the key system that ensures a safe operation for a nuclear power plant. However, a mathematical model of a nuclear power plant is in the form of nonlinear process and time dependent that give very hard to be described. One of the important components of a Pressurized Water Reactor is the Reactor core. The aim of this study is to analyze the performance of power produced from a reactor core using temperature of the moderator as an input. Mathematical representation of the state space model of the reactor core control system is presented and analyzed in this paper. The data and parameters are taken from a real time VVER-type Pressurized Water Reactor and will be verified using Matlab and Simulink. Based on the simulation conducted, the results show that the temperature of the moderator plays an important role in determining the power of reactor core.
Directory of Open Access Journals (Sweden)
Emran Tohidi
2013-01-01
Full Text Available The idea of approximation by monomials together with the collocation technique over a uniform mesh for solving state-space analysis and optimal control problems (OCPs has been proposed in this paper. After imposing the Pontryagins maximum principle to the main OCPs, the problems reduce to a linear or nonlinear boundary value problem. In the linear case we propose a monomial collocation matrix approach, while in the nonlinear case, the general collocation method has been applied. We also show the efficiency of the operational matrices of differentiation with respect to the operational matrices of integration in our numerical examples. These matrices of integration are related to the Bessel, Walsh, Triangular, Laguerre, and Hermite functions.
Torus breakdown in the symmetry-reduced state space of the Kuramoto-Sivashinsky system
Budanur, Nazmi Burak
2015-01-01
Systems such as fluid flows in channels and pipes or the complex Ginzburg-Landau system, defined over periodic domains, exhibit both continuous symmetries, translational and rotational, as well as discrete symmetries under spatial reflections or complex conjugation. The simplest, and very common symmetry of this type is the equivariance of the defining equations under the orthogonal group O(2). We formulate a novel symmetry-reduction scheme for such systems by combining the method of slices with invariant polynomial methods, and show how it works by applying it to the Kuramoto-Sivashinsky system in one spatial dimension. As an example, we track a relative periodic orbit through a sequence of bifurcation to the onset of chaos. Within the symmetry-reduced state space we are able to compute and visualize the unstable manifolds of relative periodic orbits, their torus bifurcations, a transition to chaos via torus breakdown, and heteroclinic connections between various relative periodic orbits. It would be very ha...
Maximum efficiency of state-space models of nanoscale energy conversion devices
Einax, Mario; Nitzan, Abraham
2016-07-01
The performance of nano-scale energy conversion devices is studied in the framework of state-space models where a device is described by a graph comprising states and transitions between them represented by nodes and links, respectively. Particular segments of this network represent input (driving) and output processes whose properly chosen flux ratio provides the energy conversion efficiency. Simple cyclical graphs yield Carnot efficiency for the maximum conversion yield. We give general proof that opening a link that separate between the two driving segments always leads to reduced efficiency. We illustrate these general result with simple models of a thermoelectric nanodevice and an organic photovoltaic cell. In the latter an intersecting link of the above type corresponds to non-radiative carriers recombination and the reduced maximum efficiency is manifested as a smaller open-circuit voltage.
Two Temperature Magneto-Thermoelasticity with Initial Stress: State Space Formulation
Directory of Open Access Journals (Sweden)
Sunita Deswal
2013-01-01
Full Text Available Magneto-thermoelastic interactions in an initially stressed isotropic homogeneous elastic half-space with two temperatures are studied using mathematical methods under the purview of the L-S model of linear theory of generalized thermoelasticity. The formalism deals with the state space approach with the purpose of counteracting the difficulties of handling the displacement potential functions. Of specific concern here is the propagation of waves owing to ramp type increase in temperature and load. The medium is considered to be permeated by a uniform magnetic field. The expressions for different field parameters such as displacement, temperature, strain, and stress in the physical domain are obtained by applying a numerical inversion technique. Results of some earlier workers have been deduced from the present formulation. Numerical work is also performed for a suitable material with the aim of illustrating the results.
Harmonic Interaction Analysis in Grid Connected Converter using Harmonic State Space (HSS) Modeling
DEFF Research Database (Denmark)
Kwon, Jun Bum; Wang, Xiongfei; Bak, Claus Leth
2015-01-01
An increasing number of power electronics based Distributed Generation (DG) systems and loads generate coupled harmonic as well as non-characteristic harmonic with each other. Several methods like impedance based analysis, which is derived from conventional small signal- and average-model, are in...... behavior interaction and dynamic transfer procedure. Frequency domain as well as time domain simulation results are represented by means of HSS modeling to verify the theoretical analysis. Experimental results are also included to validate the method....... during the modeling process. This paper investigates grid connected converter by means of Harmonic State Space (HSS) small signal model, which is modeled from Linear Time varying Periodically (LTP) system. Further, a grid connected converter harmonic matrix is investigated to analyze the harmonic...
State space modeling of reactor core in a pressurized water reactor
Energy Technology Data Exchange (ETDEWEB)
Ashaari, A.; Ahmad, T.; M, Wan Munirah W. [Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor (Malaysia); Shamsuddin, Mustaffa [Institute of Ibnu Sina, Universiti Teknologi Malaysia, 81310 Skudai, Johor (Malaysia); Abdullah, M. Adib [Swinburne University of Technology, Faculty of Engineering, Computing and Science, Jalan Simpang Tiga, 93350 Kuching, Sarawak (Malaysia)
2014-07-10
The power control system of a nuclear reactor is the key system that ensures a safe operation for a nuclear power plant. However, a mathematical model of a nuclear power plant is in the form of nonlinear process and time dependent that give very hard to be described. One of the important components of a Pressurized Water Reactor is the Reactor core. The aim of this study is to analyze the performance of power produced from a reactor core using temperature of the moderator as an input. Mathematical representation of the state space model of the reactor core control system is presented and analyzed in this paper. The data and parameters are taken from a real time VVER-type Pressurized Water Reactor and will be verified using Matlab and Simulink. Based on the simulation conducted, the results show that the temperature of the moderator plays an important role in determining the power of reactor core.
Discrete Simulation of Flexible Plate Structure Using State-Space Formulation
Institute of Scientific and Technical Information of China (English)
S. Md. Salleh; M. O. Tokhi
2008-01-01
This paper presents the development of dynamic simulation of a flexible plate structure with various boundary conditions. A flexible square plate is considered. A finite-difference method is used to discretise the governing partial differential equation formulation describing its dynamic behaviour. The model thus developed has been validated against characteristic parameters of the plate. The model thus developed is further formulated using discrete state-space representation, to allow easy and fast implementation for simulating the dynamic behaviour of the plate with various boundary conditions. The simulation algorithm thus developed is validated, and accurate results with representation of the first five modes of vibration of the plate have been achieved. The algorithm thus developed is used in subsequent research work as a platform for development and verification of suitable control strategies for vibration suppression of flexible plate structures.
State-space model identification and feedback control of unsteady aerodynamic forces
Brunton, Steven L; Rowley, Clarence W
2014-01-01
Unsteady aerodynamic models are necessary to accurately simulate forces and develop feedback controllers for wings in agile motion; however, these models are often high dimensional or incompatible with modern control techniques. Recently, reduced-order unsteady aerodynamic models have been developed for a pitching and plunging airfoil by linearizing the discretized Navier-Stokes equation with lift-force output. In this work, we extend these reduced-order models to include multiple inputs (pitch, plunge, and surge) and explicit parameterization by the pitch-axis location, inspired by Theodorsen's model. Next, we investigate the na\\"{\\i}ve application of system identification techniques to input--output data and the resulting pitfalls, such as unstable or inaccurate models. Finally, robust feedback controllers are constructed based on these low-dimensional state-space models for simulations of a rigid flat plate at Reynolds number 100. Various controllers are implemented for models linearized at base angles of ...
PySSM: A Python Module for Bayesian Inference of Linear Gaussian State Space Models
Directory of Open Access Journals (Sweden)
Christopher Strickland
2014-04-01
Full Text Available PySSM is a Python package that has been developed for the analysis of time series using linear Gaussian state space models. PySSM is easy to use; models can be set up quickly and efficiently and a variety of different settings are available to the user. It also takes advantage of scientific libraries NumPy and SciPy and other high level features of the Python language. PySSM is also used as a platform for interfacing between optimized and parallelized Fortran routines. These Fortran routines heavily utilize basic linear algebra and linear algebra Package functions for maximum performance. PySSM contains classes for filtering, classical smoothing as well as simulation smoothing.
Institute of Scientific and Technical Information of China (English)
袁保红; 孙秀冬; 姜永远; 周忠祥; 姚凤凤; 李焱
2002-01-01
We have proven theoretically that there are sublinear, linear and superlinear relations between the response ratesand total incident intensity for different cases of traps in photorefractive polymer materials. These relations wereobserved in inorganic photorefractive crystals many years ago. Also, the steady-state space-charge field is a functionof the total incident intensity, which has also been found in inorganic photorefractive crystals. We have measured therelations of the steady-state diffraction efficiency and the response rate with respect to the total incident intensity in thephotorefractive composite consisting of the polymer (N-vinylcarbazole) (PVK) doped with 4,4'-n-pentylcyanobiphenyl(5CB) and C60. The results obtained show that the composite belongs to the case of low trap density.
A Machine-Checked Proof of A State-Space Construction Algorithm
Catano, Nestor; Siminiceanu, Radu I.
2010-01-01
This paper presents the correctness proof of Saturation, an algorithm for generating state spaces of concurrent systems, implemented in the SMART tool. Unlike the Breadth First Search exploration algorithm, which is easy to understand and formalise, Saturation is a complex algorithm, employing a mutually-recursive pair of procedures that compute a series of non-trivial, nested local fixed points, corresponding to a chaotic fixed point strategy. A pencil-and-paper proof of Saturation exists, but a machine checked proof had never been attempted. The key element of the proof is the characterisation theorem of saturated nodes in decision diagrams, stating that a saturated node represents a set of states encoding a local fixed-point with respect to firing all events affecting only the node s level and levels below. For our purpose, we have employed the Prototype Verification System (PVS) for formalising the Saturation algorithm, its data structures, and for conducting the proofs.
Active absorption of acoustic wave using state-space control approach
Wu, Zhen; Varadan, Vijay K.; Varadan, Vasundara V.; Lee, Kwang Y.
1994-05-01
This paper presents a computer modeling and simulation of an active sound absorbing system with an optimal state-feedback controller. First, a state-space model is developed to describe one-dimensional sound reflection and transmission in the time domain. In the model derivation, the difficulty of discretizing the wave equation in an unbounded region is overcome by combining the finite-difference and analytical solutions. Numerical simulation of the open- loop model response is performed, which shows a good agreement with the well known frequency domain solutions. Second, a state-feedback controller including a linear quadratic regulator and a Kalman filter type state-estimator is designed using the optimal control theory. Numerical simulation of the closed-loop model response of an active sound control system containing two sensors and one actuator is presented. It is shown that a broadband attenuation of more than 30 dB over 2 octaves has been reached.
Choosing the observational likelihood in state-space stock assessment models
DEFF Research Database (Denmark)
Albertsen, Christoffer Moesgaard; Nielsen, Anders; Thygesen, Uffe Høgsbro
2016-01-01
Data used in stock assessment models result from combinations of biological, ecological, fishery, and sampling processes. Since different types of errors propagate through these processes it can be difficult to identify a particular family of distributions for modelling errors on observations...... a priori. By implementing several observational likelihoods, modelling both numbers- and proportions-at-age, in an age based state-space stock assessment model, we compare the model fit for each choice of likelihood along with the implications for spawning stock biomass and average fishing mortality. We...... propose using AIC intervals based on fitting the full observational model for comparing different observational likelihoods. Using data from four stocks, we show that the model fit is improved by modelling the correlation of observations within years. However, the best choice of observational likelihood...
Institute of Scientific and Technical Information of China (English)
ZHOU Jie; TANG Aiping; FENG Hailin
2016-01-01
The statistical inference for generalized mixed-effects state space models (MESSM) are investigated when the random effects are unknown.Two filtering algorithms are designed both of which are based on mixture Kalman filter.These algorithms are particularly useful when the longitudinal measurements are sparse.The authors also propose a globally convergent algorithm for parameter estimation of MESSM which can be used to locate the initial value of parameters for local while more efficient algorithms.Simulation examples are carried out which validate the efficacy of the proposed approaches.A data set from the clinical trial is investigated and a smaller mean square error is achieved compared to the existing results in literatures.
Approximation of a class of Markov-modulated Poisson processes with a large state space
Energy Technology Data Exchange (ETDEWEB)
Sitaraman, H.
1989-01-01
Many queueing systems have an arrival process that can be modeled by a Markov-modulated Poisson process. The Markov-modulated Poisson process (MMPP) is a doubly stochastic Poisson process in which the arrival rate varies according to a finite state irreducible Markov process. In many applications of MMPPs, the point process is constructed by superpositions or similar constructions, which lead to modulating Markov processes with a large state space. Since this limits the feasibility of numerical computations, a useful problem is to approximate an MMPP represented by a large Markov process by one with fewer states. The author focuses his attention in particular, to approximating a simple but useful special case of the MMPP, namely the Birth and Death Modulated Poisson process. In the validation stage, the quality of the approximation is examined in relation to the MMPP/G/1 queue.
Zhang, Yu-Lin
This paper states the application of state-space method to the analysis of the dynamic characteristics of a variable thrust liquid propellant rocket engine and presents a set of state equations for describing the dynamic process of the engine. An efficient numerical method for solving these system equations is developed. The theoretical solutions agree well with the experimental data. The analysis leads to the following conclusion: the set coefficient of the pulse width, the working frequency of the solenoid valves and the deviation of the critical working points of these valves are important parameters for determining the dynamic response time and the control precision of this engine. The methods developed in this paper may be used effectively in the analysis of dynamic characteristics of variable thrust liquid propellant rocket engines.
State-Space GMDH Neural Networks for Actuator Robust Fault Diagnosis
Directory of Open Access Journals (Sweden)
MRUGALSKI, M.
2012-08-01
Full Text Available Most fault diagnosis methods focus on the fault detection of the system or sensors and do not take into account the problem of the fault detection and isolation of the actuators, which are an important part of the contemporary industrial systems. To solve such a problem, the system outputs and inputs estimator based on a dynamic Group Method of Data Handling neural network in the state-space representation is proposed. In particular, the methodology of the adaptive thresholds calculation for system inputs and outputs is presented. The approach is based on the application of the Unscented Kalman Filter and Unknown Input Filter is presented. This result enables performing robust fault detection and isolation of the actuators. The final part of the paper presents an application study, which confirms the effectiveness of the proposed approach.
DEFF Research Database (Denmark)
Kwon, Jun Bum; Wang, Xiongfei; Blaabjerg, Frede
2016-01-01
For the efficiency and simplicity of electric systems, the dc power electronic systems are widely used in a variety of applications such as electric vehicles, ships, aircraft and also in homes. In these systems, there could be a number of dynamic interactions and frequency coupling between network...... with different switching frequency or harmonics from ac-dc converters makes that harmonics and frequency coupling are both problems of ac system and challenges of dc system. This paper presents a modeling and simulation method for a large dc power electronic system by using Harmonic State Space (HSS) modeling...... and loads and other converters. Hence, time-domain simulations are usually required to consider such a complex system behavior. However, simulations in the time-domain may increase the calculation time and the utilization of computer memory. Furthermore, frequency coupling driven by multiple converters...
Institute of Scientific and Technical Information of China (English)
Liu Huafeng; You Hongshun; Shi Pengcheng
2007-01-01
Quantitative estimation of radioactivity map has important clinical implications for better diagnosis and understanding of cancers. Although attenuation map and activity map are usually treated sequentially, they can obviously benefit a great deal when the transmission data is missing. In this paper, we propose a novel scheme of simultaneously solving for attenuation map and activity distribution from emission sinograms. Our strategy combines the measurement model of PET, and the attenuation parameters are treated as random variables with known prior statistics. After the conversion to state space representation, the extended Kalman filtering procedures are adopted to linearize the equations and to provide the joint estimates in an approximate optimal sense. Experiments have been performed on both synthetic data to illustrate its abilities and benefits.
Choosing the observational likelihood in state-space stock assessment models
DEFF Research Database (Denmark)
Albertsen, Christoffer Moesgaard; Nielsen, Anders; Thygesen, Uffe Høgsbro
2017-01-01
propose using AIC intervals based on fitting the full observational model for comparing different observational likelihoods. Using data from four stocks, we show that the model fit is improved by modelling the correlation of observations within years. However, the best choice of observational likelihood......Data used in stock assessment models result from combinations of biological, ecological, fishery, and sampling processes. Since different types of errors propagate through these processes it can be difficult to identify a particular family of distributions for modelling errors on observations...... a priori. By implementing several observational likelihoods, modelling both numbers- and proportions-at-age, in an age based state-space stock assessment model, we compare the model fit for each choice of likelihood along with the implications for spawning stock biomass and average fishing mortality. We...
DEFF Research Database (Denmark)
Poulsen, T.G.; Christophersen, Mette; Moldrup, P.
2003-01-01
were applied: (I) State-space analysis was used to identify relations between gas flux and short-term (hourly) variations in atmospheric pressure. (II) A numerical gas transport model was fitted to the data and used to quantify short-term impacts of variations in atmospheric pressure, volumetric soil......-water content, soil gas permeability, soil gas diffusion coefficients, and biological CH4 degradation rate upon landfill gas concentration and fluxes in the soil. Fluxes and concentrations were found to be most sensitive to variations in volumetric soil water content, atmospheric pressure variations and gas...... permeability whereas variations in CH4 oxidation rate and molecular coefficients had less influence. Fluxes appeared to be most sensitive to atmospheric pressure at intermediate distances from the landfill edge. Also overall CH4 fluxes out of the soil over longer periods (years) were largest during periods...
Testing for causality in reconstructed state spaces by an optimized mixed prediction method
Krakovská, Anna; Hanzely, Filip
2016-11-01
In this study, a method of causality detection was designed to reveal coupling between dynamical systems represented by time series. The method is based on the predictions in reconstructed state spaces. The results of the proposed method were compared with outcomes of two other methods, the Granger VAR test of causality and the convergent cross-mapping. We used two types of test data. The first test example is a unidirectional connection of chaotic systems of Rössler and Lorenz type. The second one, the fishery model, is an example of two correlated observables without a causal relationship. The results showed that the proposed method of optimized mixed prediction was able to reveal the presence and the direction of coupling and distinguish causality from mere correlation as well.
Stochastic State Space Modelling of Nonlinear systems - With application to Marine Ecosystems
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg
to conflict with the concept of mass balances. One of the central conclusions of the thesis is that the stochastic formulations should be an integral part of the model formulation. As discrete-time stochastic processes are simpler to handle numerically than continuous-time stochastic processes, I start......This thesis deals with stochastic dynamical systems in discrete and continuous time. Traditionally dynamical systems in continuous time are modelled using Ordinary Differential Equations (ODEs). Even the most complex system of ODEs will not be able to capture every detail of a complex system like...... a natural ecosystem, and hence residual variation between the model and observations will always remain. In stochastic state-space models the residual variation is separated into observation and system noise and a main theme of the thesis is a proper description of the system noise. Additive Gaussian noise...
Long, Christopher J; Temereanca, Simona; Desai, Neil U; Hämäläinen, Matti S; Brown, Emery N; 10.1214/11-AOAS483
2011-01-01
Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the head is a challenging problem in functional neuroimaging. The number of potential sources within the brain exceeds by an order of magnitude the number of recording sites. As a consequence, the estimates for the magnitude and location of the neural sources will be ill-conditioned because of the underdetermined nature of the problem. One well-known technique designed to address this imbalance is the minimum norm estimator (MNE). This approach imposes an $L^2$ regularization constraint that serves to stabilize and condition the source parameter estimates. However, these classes of regularizer are static in time and do not consider the temporal constraints inherent to the biophysics of the MEG experiment. In this paper we propose a dynamic state-space model that accounts for both spatial and temporal correlations within and across candida...
State-space modeling of population sizes and trends in Nihoa Finch and Millerbird
Gorresen, P. Marcos; Brinck, Kevin W.; Camp, Richard J.; Farmer, Chris; Plentovich, Sheldon M.; Banko, Paul C.
2016-01-01
Both of the 2 passerines endemic to Nihoa Island, Hawai‘i, USA—the Nihoa Millerbird (Acrocephalus familiaris kingi) and Nihoa Finch (Telespiza ultima)—are listed as endangered by federal and state agencies. Their abundances have been estimated by irregularly implemented fixed-width strip-transect sampling from 1967 to 2012, from which area-based extrapolation of the raw counts produced highly variable abundance estimates for both species. To evaluate an alternative survey method and improve abundance estimates, we conducted variable-distance point-transect sampling between 2010 and 2014. We compared our results to those obtained from strip-transect samples. In addition, we applied state-space models to derive improved estimates of population size and trends from the legacy time series of strip-transect counts. Both species were fairly evenly distributed across Nihoa and occurred in all or nearly all available habitat. Population trends for Nihoa Millerbird were inconclusive because of high within-year variance. Trends for Nihoa Finch were positive, particularly since the early 1990s. Distance-based analysis of point-transect counts produced mean estimates of abundance similar to those from strip-transects but was generally more precise. However, both survey methods produced biologically unrealistic variability between years. State-space modeling of the long-term time series of abundances obtained from strip-transect counts effectively reduced uncertainty in both within- and between-year estimates of population size, and allowed short-term changes in abundance trajectories to be smoothed into a long-term trend.
Fast and Stable Signal Deconvolution via Compressible State-Space Models.
Kazemipour, Abbas; Liu, Ji; Solarana, Krystyna; Nagode, Daniel; Kanold, Patrick; Wu, Min; Babadi, Behtash
2017-04-13
Common biological measurements are in the form of noisy convolutions of signals of interest with possibly unknown and transient blurring kernels. Examples include EEG and calcium imaging data. Thus, signal deconvolution of these measurements is crucial in understanding the underlying biological processes. The objective of this paper is to develop fast and stable solutions for signal deconvolution from noisy, blurred and undersampled data, where the signals are in the form of discrete events distributed in time and space. We introduce compressible state-space models as a framework to model and estimate such discrete events. These state-space models admit abrupt changes in the states and have a convergent transition matrix, and are coupled with compressive linear measurements. We consider a dynamic compressive sensing optimization problem and develop a fast solution, using two nested Expectation Maximization algorithms, to jointly estimate the states as well as their transition matrices. Under suitable sparsity assumptions on the dynamics, we prove optimal stability guarantees for the recovery of the states and present a method for the identification of the underlying discrete events with precise confidence bounds. We present simulation studies as well as application to calcium deconvolution and sleep spindle detection, which verify our theoretical results and show significant improvement over existing techniques. Our results show that by explicitly modeling the dynamics of the underlying signals, it is possible to construct signal deconvolution solutions that are scalable, statistically robust, and achieve high temporal resolution. Our proposed methodology provides a framework for modeling and deconvolution of noisy, blurred, and undersampled measurements in a fast and stable fashion, with potential application to a wide range of biological data.
Dysconnection topography in schizophrenia revealed with state-space analysis of EEG.
Directory of Open Access Journals (Sweden)
Mahdi Jalili
Full Text Available BACKGROUND: The dysconnection hypothesis has been proposed to account for pathophysiological mechanisms underlying schizophrenia. Widespread structural changes suggesting abnormal connectivity in schizophrenia have been imaged. A functional counterpart of the structural maps would be the EEG synchronization maps. However, due to the limits of currently used bivariate methods, functional correlates of dysconnection are limited to the isolated measurements of synchronization between preselected pairs of EEG signals. METHODS/RESULTS: To reveal a whole-head synchronization topography in schizophrenia, we applied a new method of multivariate synchronization analysis called S-estimator to the resting dense-array (128 channels EEG obtained from 14 patients and 14 controls. This method determines synchronization from the embedding dimension in a state-space domain based on the theoretical consequence of the cooperative behavior of simultaneous time series-the shrinking of the state-space embedding dimension. The S-estimator imaging revealed a specific synchronization landscape in schizophrenia patients. Its main features included bilaterally increased synchronization over temporal brain regions and decreased synchronization over the postcentral/parietal region neighboring the midline. The synchronization topography was stable over the course of several months and correlated with the severity of schizophrenia symptoms. In particular, direct correlations linked positive, negative, and general psychopathological symptoms to the hyper-synchronized temporal clusters over both hemispheres. Along with these correlations, general psychopathological symptoms inversely correlated within the hypo-synchronized postcentral midline region. While being similar to the structural maps of cortical changes in schizophrenia, the S-maps go beyond the topography limits, demonstrating a novel aspect of the abnormalities of functional cooperation: namely, regionally reduced or
State-space based analysis and forecasting of macroscopic road safety trends in Greece.
Antoniou, Constantinos; Yannis, George
2013-11-01
In this paper, macroscopic road safety trends in Greece are analyzed using state-space models and data for 52 years (1960-2011). Seemingly unrelated time series equations (SUTSE) models are developed first, followed by richer latent risk time-series (LRT) models. As reliable estimates of vehicle-kilometers are not available for Greece, the number of vehicles in circulation is used as a proxy to the exposure. Alternative considered models are presented and discussed, including diagnostics for the assessment of their model quality and recommendations for further enrichment of this model. Important interventions were incorporated in the models developed (1986 financial crisis, 1991 old-car exchange scheme, 1996 new road fatality definition) and found statistically significant. Furthermore, the forecasting results using data up to 2008 were compared with final actual data (2009-2011) indicating that the models perform properly, even in unusual situations, like the current strong financial crisis in Greece. Forecasting results up to 2020 are also presented and compared with the forecasts of a model that explicitly considers the currently on-going recession. Modeling the recession, and assuming that it will end by 2013, results in more reasonable estimates of risk and vehicle-kilometers for the 2020 horizon. This research demonstrates the benefits of using advanced state-space modeling techniques for modeling macroscopic road safety trends, such as allowing the explicit modeling of interventions. The challenges associated with the application of such state-of-the-art models for macroscopic phenomena, such as traffic fatalities in a region or country, are also highlighted. Furthermore, it is demonstrated that it is possible to apply such complex models using the relatively short time-series that are available in macroscopic road safety analysis.
Model Reduction of Hybrid Systems
DEFF Research Database (Denmark)
Shaker, Hamid Reza
systems are derived in this thesis. The results are used for output feedback control of switched nonlinear systems. Model reduction of piecewise affine systems is also studied in this thesis. The proposed method is based on the reduction of linear subsystems inside the polytopes. The methods which......High-Technological solutions of today are characterized by complex dynamical models. A lot of these models have inherent hybrid/switching structure. Hybrid/switched systems are powerful models for distributed embedded systems design where discrete controls are applied to continuous processes...... of hybrid systems, designing controllers and implementations is very high so that the use of these models is limited in applications where the size of the state space is large. To cope with complexity, model reduction is a powerful technique. This thesis presents methods for model reduction and stability...
Application of Self-tuning Models to Air Handling Units for Fault Detection%用参数自整定模型在线检测空气处理机组故障
Institute of Scientific and Technical Information of China (English)
王海涛; 陈友明; 陈永康; 秦建英
2012-01-01
Building management control systems (BMCS) are widely employed in modern buildings. The huge amount of data available on central stations and outstations provide rich information for fault diagnosis of HVAC systems. An online fault diagnosis method for variable air volume air handling units was presented using self-tuning HVAC component models. The model parameters are tuned online by using a genetic algorithm (GA) which minimizes the error between measured and estimated performance data, so high modeling accuracy is assured. If the error between measured and estimated performance data exceeds preset thresholds, it means the occurrence of faults or abnormalities in the air handling unit system. The statistical method of selecting thresholds also is presented. The fault detection method was tested and validated using data collected from real HVAC systems. The results of validation show that the fault detection method can be integrated in BMCS systems to detect faults in air handling unit systems efficiently.%大型现代建筑大都安装了能源管理与控制系统(EMCS),EMCS系统储存的大量监控数据为空调系统的在线故障检测与诊断提供了方便.提出了一种利用参数自整定空调部件模型在线检测变风量空气处理机组故障的方法.利用遗传算法优化模型参数使模型预测数据与实测值数据的残差最小,因此空调部件模型有较高的预测精度.若模型预测数据与实测数据的残差超出了预先设定的阈值,就意味着变风量空气处理机组可能存在故障.针对在实际应用时确定故障检测阈值的困难,给出了用统计方法确定阈值的方法.故障检测方法在真实建筑中进行了应用和验证,结果表明该故障检测方法可以结合EMCS系统准确有效的检测变风量空气处理机组故障.
Directory of Open Access Journals (Sweden)
Brdyś Mietek A.
2016-03-01
Full Text Available The paper considers the forecasting of the euro/Polish złoty (EUR/PLN spot exchange rate by applying state space wavelet network and econometric forecast combination models. Both prediction methods are applied to produce one-trading-day-ahead forecasts of the EUR/PLN exchange rate. The paper presents the general state space wavelet network and forecast combination models as well as their underlying principles. The state space wavelet network model is, in contrast to econometric forecast combinations, a non-parametric prediction technique which does not make any distributional assumptions regarding the underlying input variables. Both methods can be used as forecasting tools in portfolio investment management, asset valuation, IT security and integrated business risk intelligence in volatile market conditions.
Dynamic Baysesian state-space model with a neural network for an online river flow prediction
Ham, Jonghwa; Hong, Yoon-Seok
2013-04-01
The usefulness of artificial neural networks in complex hydrological modeling has been demonstrated by successful applications. Several different types of neural network have been used for the hydrological modeling task but the multi-layer perceptron (MLP) neural network (also known as the feed-forward neural network) has enjoyed a predominant position because of its simplicity and its ability to provide good approximations. In many hydrological applications of MLP neural networks, the gradient descent-based batch learning algorithm such as back-propagation, quasi-Newton, Levenburg-Marquardt, and conjugate gradient algorithms has been used to optimize the cost function (usually by minimizing the error function in the prediction) by updating the parameters and structure in a neural network defined using a set of input-output training examples. Hydrological systems are highly with time-varying inputs and outputs, and are characterized by data that arrive sequentially. The gradient descent-based batch learning approaches that are implemented in MLP neural networks have significant disadvantages for online dynamic hydrological modeling because they could not update the model structure and parameter when a new set of hydrological measurement data becomes available. In addition, a large amount of training data is always required off-line with a long model training time. In this work, a dynamic nonlinear Bayesian state-space model with a multi-layer perceptron (MLP) neural network via a sequential Monte Carlo (SMC) learning algorithm is proposed for an online dynamic hydrological modeling. This proposed new method of modeling is herein known as MLP-SMC. The sequential Monte Carlo learning algorithm in the MLP-SMC is designed to evolve and adapt the weight of a MLP neural network sequentially in time on the arrival of each new item of hydrological data. The weight of a MLP neural network is treated as the unknown dynamic state variable in the dynamic Bayesian state-space
Relative resolution: A hybrid formalism for fluid mixtures
Chaimovich, Aviel; Peter, Christine; Kremer, Kurt
2015-12-01
We show here that molecular resolution is inherently hybrid in terms of relative separation. While nearest neighbors are characterized by a fine-grained (geometrically detailed) model, other neighbors are characterized by a coarse-grained (isotropically simplified) model. We notably present an analytical expression for relating the two models via energy conservation. This hybrid framework is correspondingly capable of retrieving the structural and thermal behavior of various multi-component and multi-phase fluids across state space.
Approximate Bayesian Computation by Subset Simulation using hierarchical state-space models
Vakilzadeh, Majid K.; Huang, Yong; Beck, James L.; Abrahamsson, Thomas
2017-02-01
A new multi-level Markov Chain Monte Carlo algorithm for Approximate Bayesian Computation, ABC-SubSim, has recently appeared that exploits the Subset Simulation method for efficient rare-event simulation. ABC-SubSim adaptively creates a nested decreasing sequence of data-approximating regions in the output space that correspond to increasingly closer approximations of the observed output vector in this output space. At each level, multiple samples of the model parameter vector are generated by a component-wise Metropolis algorithm so that the predicted output corresponding to each parameter value falls in the current data-approximating region. Theoretically, if continued to the limit, the sequence of data-approximating regions would converge on to the observed output vector and the approximate posterior distributions, which are conditional on the data-approximation region, would become exact, but this is not practically feasible. In this paper we study the performance of the ABC-SubSim algorithm for Bayesian updating of the parameters of dynamical systems using a general hierarchical state-space model. We note that the ABC methodology gives an approximate posterior distribution that actually corresponds to an exact posterior where a uniformly distributed combined measurement and modeling error is added. We also note that ABC algorithms have a problem with learning the uncertain error variances in a stochastic state-space model and so we treat them as nuisance parameters and analytically integrate them out of the posterior distribution. In addition, the statistical efficiency of the original ABC-SubSim algorithm is improved by developing a novel strategy to regulate the proposal variance for the component-wise Metropolis algorithm at each level. We demonstrate that Self-regulated ABC-SubSim is well suited for Bayesian system identification by first applying it successfully to model updating of a two degree-of-freedom linear structure for three cases: globally
Henke, D.; Schubert, A.; Small, D.; Meier, E.; Lüthi, M. P.; Vieli, A.
2014-12-01
A new method for glacier surface velocity (GSV) estimates is proposed here which combines ground- and space-based measurements with hidden state space modeling (HSSM). Examples of such a fusion of physical models with remote sensing (RS) observations were described in (Henke & Meier, Hidden State Space Models for Improved Remote Sensing Applications, ITISE 2014, p. 1242-1255) and are currently adapted for GSV estimation. GSV can be estimated using in situ measurements, RS methods or numerical simulations based on ice-flow models. In situ measurements ensure high accuracy but limited coverage and time consuming field work, while RS methods offer regular observations with high spatial coverage generally not possible with in situ methods. In particular, spaceborne Synthetic Aperture Radar (SAR) can obtain useful images independent of daytime and cloud cover. A ground portable radar interferometer (GPRI) is useful for investigating a particular area in more detail than is possible from space, but provides local coverage only. Several processing methods for deriving GSV from radar sensors have been established, including interferometry and offset tracking (Schubert et al, Glacier surface velocity estimation using repeat TerraSAR-X images. ISPRS Journal of P&RS, p. 49-62, 2013). On the other hand, it is also possible to derive glacier parameters from numerical ice-flow modeling alone. Given a well-parameterized model, GSV can in theory be derived and propagated continuously in time. However, uncertainties in the glacier flow dynamics and model errors increase with excessive propagation. All of these methods have been studied independently, but attempts to combine them have only rarely been made. The HSSM we propose recursively estimates the GSV based on 1) a process model making use of temporal and spatial interdependencies between adjacent states, and 2) observations (RS and optional in situ). The in situ and GPRI images currently being processed were acquired in the
Pan, Shuokai; Elliott, Stephen J; Teal, Paul D; Lineton, Ben
2015-06-01
Nonlinear models of the cochlea are best implemented in the time domain, but their computational demands usually limit the duration of the simulations that can reasonably be performed. This letter presents a modified state space method and its application to an example nonlinear one-dimensional transmission-line cochlear model. The sparsity pattern of the individual matrices for this alternative formulation allows the use of significantly faster numerical algorithms. Combined with a more efficient implementation of the saturating nonlinearity, the computational speed of this modified state space method is more than 40 times faster than that of the original formulation.
State-space approach for the analysis of soil water content and temperature in a sugarcane crop
Directory of Open Access Journals (Sweden)
Dourado-Neto Durval
1999-01-01
Full Text Available The state-space approach is used to describe surface soil water content and temperature behaviour, in a field experiment in which sugarcane is submitted to different management practices. The treatments consisted of harvest trash mulching, bare soil, and burned trash, all three in a ratoon crop, after first cane harvest. One transect of 84 points was sampled, meter by meter, covering all treatments and borders. The state-space approach is described in detail and the results show that soil water contents measured along the transect could successfully be estimated from water content and temperature observations made at the first neighbour.
Evaluating a fish monitoring protocol using state-space hierarchical models
Russell, Robin E.; Schmetterling, David A.; Guy, Chris S.; Shepard, Bradley B.; McFarland, Robert; Skaar, Donald
2012-01-01
Using data collected from three river reaches in Montana, we evaluated our ability to detect population trends and predict fish future fish abundance. Data were collected as part of a long-term monitoring program conducted by Montana Fish, Wildlife and Parks to primarily estimate rainbow (Oncorhynchus mykiss) and brown trout (Salmo trutta) abundance in numerous rivers across Montana. We used a hierarchical Bayesian mark-recapture model to estimate fish abundance over time in each of the three river reaches. We then fit a state-space Gompertz model to estimate current trends and future fish populations. Density dependent effects were detected in 1 of the 6 fish populations. Predictions of future fish populations displayed wide credible intervals. Our simulations indicated that given the observed variation in the abundance estimates, the probability of detecting a 30% decline in fish populations over a five-year period was less than 50%. We recommend a monitoring program that is closely tied to management objectives and reflects the precision necessary to make informed management decisions.
Auger-Méthé, Marie; Field, Chris; Albertsen, Christoffer M; Derocher, Andrew E; Lewis, Mark A; Jonsen, Ian D; Mills Flemming, Joanna
2016-05-25
State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.
State-space models of mental processes from fMRI.
Janoos, Firdaus; Singh, Shantanu; Machiraju, Raghu; Wells, William M; Mórocz, Istvan A
2011-01-01
In addition to functional localization and integration, the problem of determining whether the data encode some information about the mental state of the subject, and if so, how this information is represented has become an important research agenda in functional neuroimaging. Multivariate classifiers, commonly used for brain state decoding, are restricted to simple experimental paradigms with a fixed number of alternatives and are limited in their representation of the temporal dimension of the task. Moreover, they learn a mapping from the data to experimental conditions and therefore do not explain the intrinsic patterns in the data. In this paper, we present a data-driven approach to building a spatio-temporal representation of mental processes using a state-space formalism, without reference to experimental conditions. Efficient Monte Carlo algorithms for estimating the parameters of the model along with a method for model-size selection are developed. The advantages of such a model in determining the mental-state of the subject over pattern classifiers are demonstrated using an fMRI study of mental arithmetic.
Niemi, Jarad; West, Mike
2010-06-01
We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the non-linearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters. We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.
Estimation of cortical connectivity from EEG using state-space models.
Cheung, Bing Leung Patrick; Riedner, Brady Alexander; Tononi, Giulio; Van Veen, Barry D
2010-09-01
A state-space formulation is introduced for estimating multivariate autoregressive (MVAR) models of cortical connectivity from noisy, scalp-recorded EEG. A state equation represents the MVAR model of cortical dynamics, while an observation equation describes the physics relating the cortical signals to the measured EEG and the presence of spatially correlated noise. We assume that the cortical signals originate from known regions of cortex, but the spatial distribution of activity within each region is unknown. An expectation-maximization algorithm is developed to directly estimate the MVAR model parameters, the spatial activity distribution components, and the spatial covariance matrix of the noise from the measured EEG. Simulation and analysis demonstrate that this integrated approach is less sensitive to noise than two-stage approaches in which the cortical signals are first estimated from EEG measurements, and next, an MVAR model is fit to the estimated cortical signals. The method is further demonstrated by estimating conditional Granger causality using EEG data collected while subjects passively watch a movie.
Hooker, Giles; Ellner, Stephen P; Roditi, Laura De Vargas; Earn, David J D
2011-07-06
Parameter estimation for infectious disease models is important for basic understanding (e.g. to identify major transmission pathways), for forecasting emerging epidemics, and for designing control measures. Differential equation models are often used, but statistical inference for differential equations suffers from numerical challenges and poor agreement between observational data and deterministic models. Accounting for these departures via stochastic model terms requires full specification of the probabilistic dynamics, and computationally demanding estimation methods. Here, we demonstrate the utility of an alternative approach, generalized profiling, which provides robustness to violations of a deterministic model without needing to specify a complete probabilistic model. We introduce novel means for estimating the robustness parameters and for statistical inference in this framework. The methods are applied to a model for pre-vaccination measles incidence in Ontario, and we demonstrate the statistical validity of our inference through extensive simulation. The results confirm that school term versus summer drives seasonality of transmission, but we find no effects of short school breaks and the estimated basic reproductive ratio (0) greatly exceeds previous estimates. The approach applies naturally to any system for which candidate differential equations are available, and avoids many challenges that have limited Monte Carlo inference for state-space models.
Advancing brain-machine interfaces: moving beyond linear state space models.
Rouse, Adam G; Schieber, Marc H
2015-01-01
Advances in recent years have dramatically improved output control by Brain-Machine Interfaces (BMIs). Such devices nevertheless remain robotic and limited in their movements compared to normal human motor performance. Most current BMIs rely on transforming recorded neural activity to a linear state space composed of a set number of fixed degrees of freedom. Here we consider a variety of ways in which BMI design might be advanced further by applying non-linear dynamics observed in normal motor behavior. We consider (i) the dynamic range and precision of natural movements, (ii) differences between cortical activity and actual body movement, (iii) kinematic and muscular synergies, and (iv) the implications of large neuronal populations. We advance the hypothesis that a given population of recorded neurons may transmit more useful information than can be captured by a single, linear model across all movement phases and contexts. We argue that incorporating these various non-linear characteristics will be an important next step in advancing BMIs to more closely match natural motor performance.
Frailty in state-space models: application to actuarial senescence in the Dipper.
Marzolin, Gilbert; Charmantier, Anne; Gimenez, Olivier
2011-03-01
Senescence, a decrease in life history traits with age, is a within-individual process. The lack of suitable methods to deal with individual heterogeneity has long impeded progress in exploring senescence in wild populations. Analyses of survival senescence are additionally complicated by the often neglected issue of imperfect detectability. To deal with both these issues, we developed state-space models to analyze capture-mark-recapture data while accounting for individual heterogeneity by incorporating random effects. We illustrated our approach by applying it to 29 years of data on breeding females in a Dipper (Cinclus cinclus) population. We highlighted patterns of age-related variation in annual survival by statistical comparisons of piecewise linear, quadratic, Gompertz, and Weibull survival models. The Gompertz model was ranked first in our set. It provided strong evidence for actuarial senescence with an onset of senescence estimated at about 2.3 years. The probability for this model to involve a frailty was 0.15, and the probability to involve an individual latent effect in detection was about 0.4. The estimated mean age at first reproduction was 1.2 years. The general case model described here in detail should encourage the reanalysis of actuarial senescence in cases where imperfect detection or individual heterogeneity is suspected.
A state space based approach to localizing single molecules from multi-emitter images.
Vahid, Milad R; Chao, Jerry; Ward, E Sally; Ober, Raimund J
2017-01-28
Single molecule super-resolution microscopy is a powerful tool that enables imaging at sub-diffraction-limit resolution. In this technique, subsets of stochastically photoactivated fluorophores are imaged over a sequence of frames and accurately localized, and the estimated locations are used to construct a high-resolution image of the cellular structures labeled by the fluorophores. Available localization methods typically first determine the regions of the image that contain emitting fluorophores through a process referred to as detection. Then, the locations of the fluorophores are estimated accurately in an estimation step. We propose a novel localization method which combines the detection and estimation steps. The method models the given image as the frequency response of a multi-order system obtained with a balanced state space realization algorithm based on the singular value decomposition of a Hankel matrix, and determines the locations of intensity peaks in the image as the pole locations of the resulting system. The locations of the most significant peaks correspond to the locations of single molecules in the original image. Although the accuracy of the location estimates is reasonably good, we demonstrate that, by using the estimates as the initial conditions for a maximum likelihood estimator, refined estimates can be obtained that have a standard deviation close to the Cramér-Rao lower bound-based limit of accuracy. We validate our method using both simulated and experimental multi-emitter images.
Inferring gene regulatory networks via nonlinear state-space models and exploiting sparsity.
Noor, Amina; Serpedin, Erchin; Nounou, Mohamed; Nounou, Hazem N
2012-01-01
This paper considers the problem of learning the structure of gene regulatory networks from gene expression time series data. A more realistic scenario when the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter-based state estimation algorithm is considered instead of the contemporary linear approximation-based approaches. The parameters characterizing the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then subjected to a LASSO-based least squares regression operation which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with the extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) employing the Mean Square Error (MSE) as the fidelity criterion in recovering the parameters of gene regulatory networks from synthetic data and real biological data. Extensive computer simulations illustrate that the proposed particle filter-based network inference algorithm outperforms EKF and UKF, and therefore, it can serve as a natural framework for modeling gene regulatory networks with nonlinear and sparse structure.
SiGN-SSM: open source parallel software for estimating gene networks with state space models.
Tamada, Yoshinori; Yamaguchi, Rui; Imoto, Seiya; Hirose, Osamu; Yoshida, Ryo; Nagasaki, Masao; Miyano, Satoru
2011-04-15
SiGN-SSM is an open-source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by a statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles. SiGN-SSM is distributed under GNU Affero General Public Licence (GNU AGPL) version 3 and can be downloaded at http://sign.hgc.jp/signssm/. The pre-compiled binaries for some architectures are available in addition to the source code. The pre-installed binaries are also available on the Human Genome Center supercomputer system. The online manual and the supplementary information of SiGN-SSM is available on our web site. tamada@ims.u-tokyo.ac.jp.
Møller, Jan Kloppenborg; Bergmann, Kirsten Riber; Christiansen, Lasse Engbo; Madsen, Henrik
2012-07-21
In the present study, bacterial growth in a rich media is analysed in a Stochastic Differential Equation (SDE) framework. It is demonstrated that the SDE formulation and smoothened state estimates provide a systematic framework for data driven model improvements, using random walk hidden states. Bacterial growth is limited by the available substrate and the inclusion of diffusion must obey this natural restriction. By inclusion of a modified logistic diffusion term it is possible to introduce a diffusion term flexible enough to capture both the growth phase and the stationary phase, while concentration is restricted to the natural state space (substrate and bacteria non-negative). The case considered is the growth of Salmonella and Enterococcus in a rich media. It is found that a hidden state is necessary to capture the lag phase of growth, and that a flexible logistic diffusion term is needed to capture the random behaviour of the growth model. Further, it is concluded that the Monod effect is not needed to capture the dynamics of bacterial growth in the data presented.
An Analysis of North Pacific Subsurface Temperatures Using State-Space Techniques
Bessey, Cindy
2012-01-01
North Pacific subsurface temperature data from the Simple Ocean Data Assimilation model at 10m, 50m, 75m, 100m and 150m depths, are analyzed using a combination of state-space decomposition and subspace identification techniques to examine the spatial structure of thermal variability within the upper water column. We identify four common trends from our analysis that display the major broad-scale patterns in the North Pacific over a 47 year period (1958-2004): (1) a basin-wide near-surface warming trend that identifies the mid 1980's as a change point from a cooling to a warming trend; (2) a contrasting cooling in the central basin and warming along the coast of North America that began in the early 1970's; (3) a cooling along the transition zone and the west coast of North America that becomes dominant around 1998; (4) and contrasting differences in the subarctic and subtropical gyres displaying differences in processes at each depth. We also provide a detailed analysis of the temperature variability at four...
A receptor state space model of the insulin signalling system in glucose transport.
Gray, Catheryn W; Coster, Adelle C F
2015-12-01
Insulin is a potent peptide hormone that regulates glucose levels in the blood. Insulin-sensitive cells respond to insulin stimulation with the translocation of glucose transporter 4 (GLUT4) to the plasma membrane (PM), enabling the clearance of glucose from the blood. Defects in this process can give rise to insulin resistance and ultimately diabetes. One widely cited model of insulin signalling leading to glucose transport is that of Sedaghat et al. (2002) Am. J. Physiol. Endocrinol. Metab. 283, E1084-E1101. Consisting of 20 deterministic ordinary differential equations (ODEs), it is the most comprehensive model of insulin signalling to date. However, the model possesses some major limitations, including the non-conservation of key components. In the current work, we detail mathematical and sensitivity analyses of the Sedaghat model. Based on the results of these analyses, we propose a reduced state space model of the insulin receptor subsystem. This reduced model maintains the input-output relation of the original model but is computationally more efficient, analytically tractable and resolves some of the limitations of the Sedaghat model.
Contaminant ingress into multizone buildings: An analytical state-space approach
Parker, Simon
2013-08-13
The ingress of exterior contaminants into buildings is often assessed by treating the building interior as a single well-mixed space. Multizone modelling provides an alternative way of representing buildings that can estimate concentration time series in different internal locations. A state-space approach is adopted to represent the concentration dynamics within multizone buildings. Analysis based on this approach is used to demonstrate that the exposure in every interior location is limited to the exterior exposure in the absence of removal mechanisms. Estimates are also developed for the short term maximum concentration and exposure in a multizone building in response to a step-change in concentration. These have considerable potential for practical use. The analytical development is demonstrated using a simple two-zone building with an inner zone and a range of existing multizone models of residential buildings. Quantitative measures are provided of the standard deviation of concentration and exposure within a range of residential multizone buildings. Ratios of the maximum short term concentrations and exposures to single zone building estimates are also provided for the same buildings. © 2013 Tsinghua University Press and Springer-Verlag Berlin Heidelberg.
Using State Space Methods to Reveal Dynamical Associations Between Cortisol and Depression.
Toonen, Roelof B; Wardenaar, Klaas J; van Ockenburg, Sonja L; Bos, Elisabeth H; de Jonge, Peter
2016-01-01
Despite extensive research, the link between etiological factors and depression remains poorly understood. This may in part be due to a focus on strictly linear definitions of causality, derived at the group level. However, etiological relations in depression are likely to be dynamical, nonlinear and potentially unquantifiable with traditional statistics. Therefore the aim of this study was to evaluate the use of the convergent cross-mapping (CCM) method in investigating possible nonlinear relationships between supposed etiological factors and depressive symptomatology. Time series data from six healthy individuals were used to model the relationship between 24-h urinary free cortisol and negative affect using CCM and dewdrop embeddings. CCM is a nonlinear measure of causality, based on state space reconstruction with lagged coordinate embeddings. The results showed that nonlinear dynamical relationships between cortisol and negative affect may be present within participants, as demonstrated by a positive cross-map convergence from negative affect to cortisol. However, analyses also showed that noise and influential points had considerable impact on the results. Convergent crossmapping can be used to reveal possible nonlinear dynamical relationships between etiological factors and psychopathology that may remain undetected with traditional linear causality measures.
Energy Technology Data Exchange (ETDEWEB)
Sahmani, S.; Ansari, R. [University of Guilan, Rasht (Iran, Islamic Republic of)
2011-09-15
Buckling analysis of nanobeams is investigated using nonlocal continuum beam models of the different classical beam theories namely as Euler-Bernoulli beam theory (EBT), Timoshenko beam theory (TBT), and Levinson beam theory (LBT). To this end, Eringen's equations of nonlocal elasticity are incorporated into the classical beam theories for buckling of nanobeams with rectangular cross-section. In contrast to the classical theories, the nonlocal elastic beam models developed here have the capability to predict critical buckling loads that allowing for the inclusion of size effects. The values of critical buckling loads corresponding to four commonly used boundary conditions are obtained using state-space method. The results are presented for different geometric parameters, boundary conditions, and values of nonlocal parameter to show the effects of each of them in detail. Then the results are fitted with those of molecular dynamics simulations through a nonlinear least square fitting procedure to find the appropriate values of nonlocal parameter for the buckling analysis of nanobeams relevant to each type of nonlocal beam model and boundary conditions analysis.
Lee-Carter state space modeling: Application to the Malaysia mortality data
Zakiyatussariroh, W. H. Wan; Said, Z. Mohammad; Norazan, M. R.
2014-06-01
This article presents an approach that formalizes the Lee-Carter (LC) model as a state space model. Maximum likelihood through Expectation-Maximum (EM) algorithm was used to estimate the model. The methodology is applied to Malaysia's total population mortality data. Malaysia's mortality data was modeled based on age specific death rates (ASDR) data from 1971-2009. The fitted ASDR are compared to the actual observed values. However, results from the comparison of the fitted and actual values between LC-SS model and the original LC model shows that the fitted values from the LC-SS model and original LC model are quite close. In addition, there is not much difference between the value of root mean squared error (RMSE) and Akaike information criteria (AIC) from both models. The LC-SS model estimated for this study can be extended for forecasting ASDR in Malaysia. Then, accuracy of the LC-SS compared to the original LC can be further examined by verifying the forecasting power using out-of-sample comparison.
Tomicic, Alemka; Martínez, Claudio; Pérez, J Carola; Hollenstein, Tom; Angulo, Salvador; Gerstmann, Adam; Barroux, Isabelle; Krause, Mariane
2015-01-01
This study seeks to provide evidence of the dynamics associated with the configurations of discourse-voice regulatory strategies in patient-therapist interactions in relevant episodes within psychotherapeutic sessions. Its central assumption is that discourses manifest themselves differently in terms of their prosodic characteristics according to their regulatory functions in a system of interactions. The association between discourse and vocal quality in patients and therapists was analyzed in a sample of 153 relevant episodes taken from 164 sessions of five psychotherapies using the state space grid (SSG) method, a graphical tool based on the dynamic systems theory (DST). The results showed eight recurrent and stable discourse-voice regulatory strategies of the patients and three of the therapists. Also, four specific groups of these discourse-voice strategies were identified. The latter were interpreted as regulatory configurations, that is to say, as emergent self-organized groups of discourse-voice regulatory strategies constituting specific interactional systems. Both regulatory strategies and their configurations differed between two types of relevant episodes: Change Episodes and Rupture Episodes. As a whole, these results support the assumption that speaking and listening, as dimensions of the interaction that takes place during therapeutic conversation, occur at different levels. The study not only shows that these dimensions are dependent on each other, but also that they function as a complex and dynamic whole in therapeutic dialog, generating relational offers which allow the patient and the therapist to regulate each other and shape the psychotherapeutic process that characterizes each type of relevant episode.
Entangled Bloch spheres: Bloch matrix and two-qubit state space
Gamel, Omar
2016-06-01
We represent a two-qubit density matrix in the basis of Pauli matrix tensor products, with the coefficients constituting a Bloch matrix, analogous to the single qubit Bloch vector. We find the quantum state positivity requirements on the Bloch matrix components, leading to three important inequalities, allowing us to parametrize and visualize the two-qubit state space. Applying the singular value decomposition naturally separates the degrees of freedom to local and nonlocal, and simplifies the positivity inequalities. It also allows us to geometrically represent a state as two entangled Bloch spheres with superimposed correlation axes. It is shown that unitary transformations, local or nonlocal, have simple interpretations as axis rotations or mixing of certain degrees of freedom. The nonlocal unitary invariants of the state are then derived in terms of local unitary invariants. The positive partial transpose criterion for entanglement is generalized, and interpreted as a reflection, or a change of a single sign. The formalism is used to characterize maximally entangled states, and generalize two qubit isotropic and Werner states.
Robust maximum likelihood estimation for stochastic state space model with observation outliers
AlMutawa, J.
2016-08-01
The objective of this paper is to develop a robust maximum likelihood estimation (MLE) for the stochastic state space model via the expectation maximisation algorithm to cope with observation outliers. Two types of outliers and their influence are studied in this paper: namely,the additive outlier (AO) and innovative outlier (IO). Due to the sensitivity of the MLE to AO and IO, we propose two techniques for robustifying the MLE: the weighted maximum likelihood estimation (WMLE) and the trimmed maximum likelihood estimation (TMLE). The WMLE is easy to implement with weights estimated from the data; however, it is still sensitive to IO and a patch of AO outliers. On the other hand, the TMLE is reduced to a combinatorial optimisation problem and hard to implement but it is efficient to both types of outliers presented here. To overcome the difficulty, we apply the parallel randomised algorithm that has a low computational cost. A Monte Carlo simulation result shows the efficiency of the proposed algorithms. An earlier version of this paper was presented at the 8th Asian Control Conference, Kaohsiung, Taiwan, 2011.
Fast Kalman-like filtering for large-dimensional linear and Gaussian state-space models
Ait-El-Fquih, Boujemaa
2015-08-13
This paper considers the filtering problem for linear and Gaussian state-space models with large dimensions, a setup in which the optimal Kalman Filter (KF) might not be applicable owing to the excessive cost of manipulating huge covariance matrices. Among the most popular alternatives that enable cheaper and reasonable computation is the Ensemble KF (EnKF), a Monte Carlo-based approximation. In this paper, we consider a class of a posteriori distributions with diagonal covariance matrices and propose fast approximate deterministic-based algorithms based on the Variational Bayesian (VB) approach. More specifically, we derive two iterative KF-like algorithms that differ in the way they operate between two successive filtering estimates; one involves a smoothing estimate and the other involves a prediction estimate. Despite its iterative nature, the prediction-based algorithm provides a computational cost that is, on the one hand, independent of the number of iterations in the limit of very large state dimensions, and on the other hand, always much smaller than the cost of the EnKF. The cost of the smoothing-based algorithm depends on the number of iterations that may, in some situations, make this algorithm slower than the EnKF. The performances of the proposed filters are studied and compared to those of the KF and EnKF through a numerical example.
Identification of the parameters of a DC motor state space model
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Momir Ranislav Stanković
2013-06-01
Full Text Available A method for the identification of the DC state space model parameters based on the minimization of the error function using the least squares method is described in this paper. The algorithm is practically applied in the laboratory environment on an industrial DC motor. The verification of the results was performed by comparing the characteristic signals of real and modeled systems. The results show that the quality of the identification is satisfactory. Introduction The identification of system parameters is the first step in the analysis and synthesis of control systems. Identification Quality strongly impacts on the results of all other computations. In the theory of automatic control, many methods of identification are developed. Which method will be applied depends on the characteristics of the system. In this paper, we described an identification algorithm based on the least squares method. A practical test of this algorithm of estimation is done on a DC motor. parameter estimation with the least squares method A DC motor is a second-order system described with two differential equations: one which describes electrical and one which describes mechanical parts of the motor. The idea is to analyse the motor as two first-order systems. The main signals are responses of two first order sub-systems on appropriate inputs. Using a discrete state-space model of the motor and applying the least square method on the recorded signals, we get straightforward equations for the computation of all the necessary parameters: Rr, Lr , Je , Fe , Kme and Kem (Eykhoff, Wilsoons, 1974. Experimental results The practical application was realized in the laboratory where a DC middle-power motor was used as a control object. It is coupled with a DC generator which serves as a load. Generation of the input signals and measure of the responses were performed with the acquisition system based on the appropriate acquisition card and the MATLAB
Determinants of road traffic safety: New evidence from Australia using state-space analysis.
Nghiem, Son; Commandeur, Jacques J F; Connelly, Luke B
2016-09-01
This paper examines the determinants of road traffic crash fatalities in Queensland for the period 1958-2007 using a state-space time-series model. In particular, we investigate the effects of policies that aimed to reduce drink-driving on traffic fatalities, as well as indicators of the economic environment that may affect exposure to traffic, and hence affect the number of accidents and fatalities. The results show that the introduction of a random breath testing program in 1988 was associated with a 11.3% reduction in traffic fatalities; its expansion in 1998 was associated with a 26.2% reduction in traffic fatalities; and the effect of the "Safe4life" program, which was introduced in 2004, was a 14.3% reduction in traffic fatalities. Reductions in economic activity are also associated with reductions in road fatalities: we estimate that a one percent increase in the unemployment rate is associated with a 0.2% reduction in traffic fatalities.
Adaptive internal state space construction method for reinforcement learning of a real-world agent.
Samejima, K; Omori, T
1999-10-01
One of the difficulties encountered in the application of the reinforcement learning to real-world problems is the construction of a discrete state space from a continuous sensory input signal. In the absence of a priori knowledge about the task, a straightforward approach to this problem is to discretize the input space into a grid, and to use a lookup table. However, this method suffers from the curse of dimensionality. Some studies use continuous function approximators such as neural networks instead of lookup tables. However, when global basis functions such as sigmoid functions are used, convergence cannot be guaranteed. To overcome this problem, we propose a method in which local basis functions are incrementally assigned depending on the task requirement. Initially, only one basis function is allocated over the entire space. The basis function is divided according to the statistical property of locally weighted temporal difference error (TD error) of the value function. We applied this method to an autonomous robot collision avoidance problem, and evaluated the validity of the algorithm in simulation. The proposed algorithm, which we call adaptive basis division (ABD) algorithm, achieved the task using a smaller number of basis functions than the conventional methods. Moreover, we applied the method to a goal-directed navigation problem of a real mobile robot. The action strategy was learned using a database of sensor data, and it was then used for navigation of a real machine. The robot reached the goal using a smaller number of internal states than with the conventional methods.
Forecasting seasonal influenza with a state-space SIR model1
Osthus, Dave; Hickmann, Kyle S.; Caragea, Petruţa C.; Higdon, Dave; Del Valle, Sara Y.
2017-01-01
Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the Centers for Disease Control and Prevention’s influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts. Despite the relatively rich surveillance data and the recurrent nature of seasonal influenza, forecasting the timing and intensity of seasonal influenza in the U.S. remains challenging because the form of the disease transmission process is uncertain, the disease dynamics are only partially observed, and the public health observations are noisy. Fitting a probabilistic state-space model motivated by a deterministic mathematical model [a susceptible-infectious-recovered (SIR) model] is a promising approach for forecasting seasonal influenza while simultaneously accounting for multiple sources of uncertainty. A significant finding of this work is the importance of thoughtfully specifying the prior, as results critically depend on its specification. Our conditionally specified prior allows us to exploit known relationships between latent SIR initial conditions and parameters and functions of surveillance data. We demonstrate advantages of our approach relative to alternatives via a forecasting comparison using several forecast accuracy metrics. PMID:28979611
Fast fitting of non-Gaussian state-space models to animal movement data via Template Model Builder
DEFF Research Database (Denmark)
Albertsen, Christoffer Moesgaard; Whoriskey, Kim; Yurkowski, David
2015-01-01
State-space models (SSM) are often used for analyzing complex ecological processes that are not observed directly, such as marine animal movement. When outliers are present in the measurements, special care is needed in the analysis to obtain reliable location and process estimates. Here we...
Using Innovative Outliers to Detect Discrete Shifts in Dynamics in Group-Based State-Space Models
Chow, Sy-Miin; Hamaker, Ellen L.; Allaire, Jason C.
2009-01-01
Outliers are typically regarded as data anomalies that should be discarded. However, dynamic or "innovative" outliers can be appropriately utilized to capture unusual but substantively meaningful shifts in a system's dynamics. We extend De Jong and Penzer's 1998 approach for representing outliers in single-subject state-space models to a…
Ayupov, Sh A
2011-01-01
In the present article we prove a fixed point theorem for reflections of compact convex sets and give a new characterization of state space of JB-algebras among compact convex sets. Namely they are exactly those compact convex sets which are strongly spectral and symmetric.
Song, Hairong; Ferrer, Emilio
2009-01-01
This article presents a state-space modeling (SSM) technique for fitting process factor analysis models directly to raw data. The Kalman smoother via the expectation-maximization algorithm to obtain maximum likelihood parameter estimates is used. To examine the finite sample properties of the estimates in SSM when common factors are involved, a…
Bergboer, N.H; Verdult, V.; Verhaegen, M.H.G.
2002-01-01
We present a numerically efficient implementation of the nonlinear least squares and maximum likelihood identification of multivariable linear time-invariant (LTI) state-space models. This implementation is based on a local parameterization of the system and a gradient search in the resulting parame
Modulation depth estimation and variable selection in state-space models for neural interfaces.
Malik, Wasim Q; Hochberg, Leigh R; Donoghue, John P; Brown, Emery N
2015-02-01
Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems.
Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.
Aprasoff, Jonathan; Donchin, Opher
2012-04-01
Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.
Computational state space models for activity and intention recognition. A feasibility study.
Krüger, Frank; Nyolt, Martin; Yordanova, Kristina; Hein, Albert; Kirste, Thomas
2014-01-01
Computational state space models (CSSMs) enable the knowledge-based construction of Bayesian filters for recognizing intentions and reconstructing activities of human protagonists in application domains such as smart environments, assisted living, or security. Computational, i. e., algorithmic, representations allow the construction of increasingly complex human behaviour models. However, the symbolic models used in CSSMs potentially suffer from combinatorial explosion, rendering inference intractable outside of the limited experimental settings investigated in present research. The objective of this study was to obtain data on the feasibility of CSSM-based inference in domains of realistic complexity. A typical instrumental activity of daily living was used as a trial scenario. As primary sensor modality, wearable inertial measurement units were employed. The results achievable by CSSM methods were evaluated by comparison with those obtained from established training-based methods (hidden Markov models, HMMs) using Wilcoxon signed rank tests. The influence of modeling factors on CSSM performance was analyzed via repeated measures analysis of variance. The symbolic domain model was found to have more than 10(8) states, exceeding the complexity of models considered in previous research by at least three orders of magnitude. Nevertheless, if factors and procedures governing the inference process were suitably chosen, CSSMs outperformed HMMs. Specifically, inference methods used in previous studies (particle filters) were found to perform substantially inferior in comparison to a marginal filtering procedure. Our results suggest that the combinatorial explosion caused by rich CSSM models does not inevitably lead to intractable inference or inferior performance. This means that the potential benefits of CSSM models (knowledge-based model construction, model reusability, reduced need for training data) are available without performance penalty. However, our results also
Particle MCMC algorithms and architectures for accelerating inference in state-space models.
Mingas, Grigorios; Bottolo, Leonardo; Bouganis, Christos-Savvas
2017-04-01
Particle Markov Chain Monte Carlo (pMCMC) is a stochastic algorithm designed to generate samples from a probability distribution, when the density of the distribution does not admit a closed form expression. pMCMC is most commonly used to sample from the Bayesian posterior distribution in State-Space Models (SSMs), a class of probabilistic models used in numerous scientific applications. Nevertheless, this task is prohibitive when dealing with complex SSMs with massive data, due to the high computational cost of pMCMC and its poor performance when the posterior exhibits multi-modality. This paper aims to address both issues by: 1) Proposing a novel pMCMC algorithm (denoted ppMCMC), which uses multiple Markov chains (instead of the one used by pMCMC) to improve sampling efficiency for multi-modal posteriors, 2) Introducing custom, parallel hardware architectures, which are tailored for pMCMC and ppMCMC. The architectures are implemented on Field Programmable Gate Arrays (FPGAs), a type of hardware accelerator with massive parallelization capabilities. The new algorithm and the two FPGA architectures are evaluated using a large-scale case study from genetics. Results indicate that ppMCMC achieves 1.96x higher sampling efficiency than pMCMC when using sequential CPU implementations. The FPGA architecture of pMCMC is 12.1x and 10.1x faster than state-of-the-art, parallel CPU and GPU implementations of pMCMC and up to 53x more energy efficient; the FPGA architecture of ppMCMC increases these speedups to 34.9x and 41.8x respectively and is 173x more power efficient, bringing previously intractable SSM-based data analyses within reach.
Directory of Open Access Journals (Sweden)
Jin Hwan Do
2015-10-01
Full Text Available This study compared a parkinsonian neurotoxin 1-methyl-4-phenylpyridinium (MPP+ response in two distinct phenotypes of human neuroblastoma cell lines: neuronal N-type SH-SY5Y cells and flat substrate-adherent S-type SH-EP cells. SH-SY5Y and SH-EP cells shared only 14% of their own MPP+ response genes, and their gene ontology (GO analysis revealed significant endoplasmic reticulum (ER stress by misfolded proteins. Gene modules, which are groups of transcriptionally co-expressed genes with similar biological functions, were identified for SH-SY5Y and SH-EP cells by using time-series microarray data with the state space model (SSM. All modules of SH-SY5Y and SH-EP cells showed strong positive auto-regulation that was often mediated via signal molecules and may cause bi-stability. Interactions in gene levels were calculated by using SSM parameters obtained in the process of module identification. Gene networks that were constructed from the gene interaction matrix showed different hub genes with high node degrees between SH-SY5Y and SH-EP cells. That is, key hub genes of SH-SY5Y cells were DCN, HIST1H2BK, and C5orf40, whereas those of SH-EP cells were MSH6, RBCK1, MTHFD2, ZNF26, CTH, and CARS. These results suggest that inhibition of the mitochondrial complex I by MPP+ might induce different downstream processes that are cell type dependent.
The consciousness state space (CSS – a unifying model for consciousness and self
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Aviva eBerkovich-Ohana
2014-04-01
Full Text Available Every experience, those we are aware of and those we are not, is embedded in a subjective timeline, is tinged with emotion, and inevitably evokes a certain sense of self. Here, we present a phenomenological model for consciousness and selfhood which relates time, awareness, and emotion within one framework. The consciousness state space (CSS model is a theoretical one. It relies on a broad range of literature, hence has high explanatory and integrative strength, and helps in visualizing the relationship between different aspects of experience.Briefly, it is suggested that all phenomenological states fall into two categories of consciousness, core and extended (CC and EC, respectively. CC supports minimal selfhood that is short of temporal extension, its scope being the here and now. EC supports narrative selfhood, which involves personal identity and continuity across time, as well as memory, imagination and conceptual thought. The CSS is a phenomenological space, created by three dimensions: time, awareness and emotion. Each of the three dimensions is shown to have a dual phenomenological composition, falling within CC and EC. The neural spaces supporting each of these dimensions, as well as CC and EC, are laid out based on the neuroscientific literature.The CSS dynamics includes two simultaneous trajectories, one in CC and one in EC, typically antagonistic in normal experiences. However, this characteristic behavior is altered in states in which a person experiences an altered sense of self. Two examples are laid out, flow and meditation. The CSS model creates a broad theoretical framework with explanatory and unificatory power. It constructs a detailed map of the consciousness and selfhood phenomenology, which offers constraints for the science of consciousness. We conclude by outlaying several testable predictions raised by the CSS model.
The consciousness state space (CSS)-a unifying model for consciousness and self.
Berkovich-Ohana, Aviva; Glicksohn, Joseph
2014-01-01
Every experience, those we are aware of and those we are not, is embedded in a subjective timeline, is tinged with emotion, and inevitably evokes a certain sense of self. Here, we present a phenomenological model for consciousness and selfhood which relates time, awareness, and emotion within one framework. The consciousness state space (CSS) model is a theoretical one. It relies on a broad range of literature, hence has high explanatory and integrative strength, and helps in visualizing the relationship between different aspects of experience. Briefly, it is suggested that all phenomenological states fall into two categories of consciousness, core and extended (CC and EC, respectively). CC supports minimal selfhood that is short of temporal extension, its scope being the here and now. EC supports narrative selfhood, which involves personal identity and continuity across time, as well as memory, imagination and conceptual thought. The CSS is a phenomenological space, created by three dimensions: time, awareness and emotion. Each of the three dimensions is shown to have a dual phenomenological composition, falling within CC and EC. The neural spaces supporting each of these dimensions, as well as CC and EC, are laid out based on the neuroscientific literature. The CSS dynamics include two simultaneous trajectories, one in CC and one in EC, typically antagonistic in normal experiences. However, this characteristic behavior is altered in states in which a person experiences an altered sense of self. Two examples are laid out, flow and meditation. The CSS model creates a broad theoretical framework with explanatory and unificatory power. It constructs a detailed map of the consciousness and selfhood phenomenology, which offers constraints for the science of consciousness. We conclude by outlining several testable predictions raised by the CSS model.
Computational state space models for activity and intention recognition. A feasibility study.
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Frank Krüger
Full Text Available BACKGROUND: Computational state space models (CSSMs enable the knowledge-based construction of Bayesian filters for recognizing intentions and reconstructing activities of human protagonists in application domains such as smart environments, assisted living, or security. Computational, i. e., algorithmic, representations allow the construction of increasingly complex human behaviour models. However, the symbolic models used in CSSMs potentially suffer from combinatorial explosion, rendering inference intractable outside of the limited experimental settings investigated in present research. The objective of this study was to obtain data on the feasibility of CSSM-based inference in domains of realistic complexity. METHODS: A typical instrumental activity of daily living was used as a trial scenario. As primary sensor modality, wearable inertial measurement units were employed. The results achievable by CSSM methods were evaluated by comparison with those obtained from established training-based methods (hidden Markov models, HMMs using Wilcoxon signed rank tests. The influence of modeling factors on CSSM performance was analyzed via repeated measures analysis of variance. RESULTS: The symbolic domain model was found to have more than 10(8 states, exceeding the complexity of models considered in previous research by at least three orders of magnitude. Nevertheless, if factors and procedures governing the inference process were suitably chosen, CSSMs outperformed HMMs. Specifically, inference methods used in previous studies (particle filters were found to perform substantially inferior in comparison to a marginal filtering procedure. CONCLUSIONS: Our results suggest that the combinatorial explosion caused by rich CSSM models does not inevitably lead to intractable inference or inferior performance. This means that the potential benefits of CSSM models (knowledge-based model construction, model reusability, reduced need for training data are
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Alemka eTomicic
2015-04-01
Full Text Available This study seeks to provide evidence of the dynamics associated with the configurations of discourse-voice regulatory strategies in patient-therapist interactions in relevant episodes within psychotherapeutic sessions. Its central assumption is that discourses manifest themselves differently in terms of their prosodic characteristics according to their regulatory functions in a system of interactions. The association between discourse and vocal quality in patients and therapists was analyzed in a sample of 153 relevant episodes taken from 164 sessions of five psychotherapies using the State Space Grid (SSG method, a graphical tool based on the Dynamic Systems Theory (DST. The results showed eight recurrent and stable discourse-voice regulatory strategies of the patients and three of the therapists. Also, four specific groups of these discourse-voice strategies were identified. The latter were interpreted as regulatory configurations, that is to say, as emergent self-organized groups of discourse-voice regulatory strategies constituting specific interactional systems. Both regulatory strategies and their configurations differed between two types of relevant episodes: Change Episodes and Rupture Episodes. As a whole, these results support the assumption that speaking and listening, as dimensions of the interaction that takes place during therapeutic conversation, occur at different levels. The study not only shows that these dimensions are dependent on each other, but also that they function as a complex and dynamic whole in therapeutic dialogue, generating relational offers which allow the patient and the therapist to regulate each other and shape the psychotherapeutic process that characterizes each type of relevant episode.
Five degrees of freedom linear state-space representation of electrodynamic thrust bearings
Verdeghem, J. Van; Kluyskens, V.; Dehez, B.
2017-09-01
Electrodynamic bearings can provide stable and contactless levitation of rotors while operating at room temperatures. Depending solely on passive phenomena, specific models have to be developed to study the forces they exert and the resulting rotordynamics. In recent years, models allowing us to describe the axial dynamics of a large range of electrodynamic thrust bearings have been derived. However, these bearings being devised to be integrated into fully magnetic suspensions, the existing models still suffer from restrictions. Indeed, assuming the spin speed as varying slowly, a rigid rotor is characterised by five independent degrees of freedom whereas early models only considered the axial degree. This paper presents a model free of the previous limitations. It consists in a linear state-space representation describing the rotor's complete dynamics by considering the impact of the rotor axial, radial and angular displacements as well as the gyroscopic effects. This set of ten equations depends on twenty parameters whose identification can be easily performed through static finite element simulations or quasi-static experimental measurements. The model stresses the intrinsic decoupling between the axial dynamics and the other degrees of freedom as well as the existence of electrodynamic angular torques restoring the rotor to its nominal position. Finally, a stability analysis performed on the model highlights the presence of two conical whirling modes related to the angular dynamics, namely the nutation and precession motions. The former, whose intrinsic stability depends on the ratio between polar and transverse moments of inertia, can be easily stabilised through external damping whereas the latter, which is stable up to an instability threshold linked to the angular electrodynamic cross-coupling stiffness, is less impacted by that damping.
Viewing hybrid systems as products of control systems and automata
Grossman, R. L.; Larson, R. G.
1992-01-01
The purpose of this note is to show how hybrid systems may be modeled as products of nonlinear control systems and finite state automata. By a hybrid system, we mean a network of consisting of continuous, nonlinear control system connected to discrete, finite state automata. Our point of view is that the automata switches between the control systems, and that this switching is a function of the discrete input symbols or letters that it receives. We show how a nonlinear control system may be viewed as a pair consisting of a bialgebra of operators coding the dynamics, and an algebra of observations coding the state space. We also show that a finite automata has a similar representation. A hybrid system is then modeled by taking suitable products of the bialgebras coding the dynamics and the observation algebras coding the state spaces.
Monteiro, Felipe R.
2016-01-01
The extensive use of digital controllers demands a growing effort to prevent design errors that appear due to finite-word length (FWL) effects. However, there is still a gap, regarding verification tools and methodologies to check implementation aspects of control systems. Thus, the present paper describes an approach, which employs bounded model checking (BMC) techniques, to verify fixed-point digital controllers represented by state-space equations. The experimental results demonstrate the ...
State-space modeling to support management of brucellosis in the Yellowstone bison population
Hobbs, N. Thompson; Geremia, Chris; Treanor, John; Wallen, Rick; White, P.J.; Hooten, Mevin B.; Rhyan, Jack C.
2015-01-01
The bison (Bison bison) of the Yellowstone ecosystem, USA, exemplify the difficulty of conserving large mammals that migrate across the boundaries of conservation areas. Bison are infected with brucellosis (Brucella abortus) and their seasonal movements can expose livestock to infection. Yellowstone National Park has embarked on a program of adaptive management of bison, which requires a model that assimilates data to support management decisions. We constructed a Bayesian state-space model to reveal the influence of brucellosis on the Yellowstone bison population. A frequency-dependent model of brucellosis transmission was superior to a density-dependent model in predicting out-of-sample observations of horizontal transmission probability. A mixture model including both transmission mechanisms converged on frequency dependence. Conditional on the frequency-dependent model, brucellosis median transmission rate was 1.87 yr−1. The median of the posterior distribution of the basic reproductive ratio (R0) was 1.75. Seroprevalence of adult females varied around 60% over two decades, but only 9.6 of 100 adult females were infectious. Brucellosis depressed recruitment; estimated population growth rate λ averaged 1.07 for an infected population and 1.11 for a healthy population. We used five-year forecasting to evaluate the ability of different actions to meet management goals relative to no action. Annually removing 200 seropositive female bison increased by 30-fold the probability of reducing seroprevalence below 40% and increased by a factor of 120 the probability of achieving a 50% reduction in transmission probability relative to no action. Annually vaccinating 200 seronegative animals increased the likelihood of a 50% reduction in transmission probability by fivefold over no action. However, including uncertainty in the ability to implement management by representing stochastic variation in the number of accessible bison dramatically reduced the probability of
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Miyano Satoru
2009-07-01
feed-forward loop type of regulation of heat shock proteins with metabolic genes became less frequent with increasing temperature. This might be the reason for dramatic increase in the expression of heat shock proteins and the number of heat shock response genes at heat shock of 48°C. Conclusion We systemically analysed the thermal adaption mechanism of A. fumigatus by state space model with times series microarray data in terms of transcription regulation structure. We suggest for the first time that heat shock proteins might efficiently regulate metabolic genes using the coherent feed-forward loop type of regulation structure. This type of regulation structure would also be efficient for adjustment to the other stresses requiring rapid change of metabolic mode as well as thermal adaptation.
State-Space Modelling of the Drivers of Movement Behaviour in Sympatric Species.
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F J Pérez-Barbería
Full Text Available Understanding animal movement behaviour is key to furthering our knowledge on intra- and inter-specific competition, group cohesion, energy expenditure, habitat use, the spread of zoonotic diseases or species management. We used a radial basis function surface approximation subject to minimum description length constraint to uncover the state-space dynamical systems from time series data. This approximation allowed us to infer structure from a mathematical model of the movement behaviour of sheep and red deer, and the effect of density, thermal stress and vegetation type. Animal movement was recorded using GPS collars deployed in sheep and deer grazing a large experimental plot in winter and summer. Information on the thermal stress to which animals were exposed was estimated using the power consumption of mechanical heated models and meteorological records of a network of stations in the plot. Thermal stress was higher in deer than in sheep, with less differences between species in summer. Deer travelled more distance than sheep, and both species travelled more in summer than in winter; deer travel distance showed less seasonal differences than sheep. Animal movement was better predicted in deer than in sheep and in winter than in summer; both species showed a swarming behaviour in group cohesion, stronger in deer. At shorter separation distances swarming repulsion was stronger between species than within species. At longer separation distances inter-specific attraction was weaker than intra-specific; there was a positive density-dependent effect on swarming, and stronger in deer than in sheep. There was not clear evidence which species attracted or repelled the other; attraction between deer at long separation distances was stronger when the model accounted for thermal stress, but in general the dynamic movement behaviour was hardly affected by the thermal stress. Vegetation type affected intra-species interactions but had little effect on
State-Space Modelling of the Drivers of Movement Behaviour in Sympatric Species.
Pérez-Barbería, F J; Small, M; Hooper, R J; Aldezabal, A; Soriguer-Escofet, R; Bakken, G S; Gordon, I J
2015-01-01
Understanding animal movement behaviour is key to furthering our knowledge on intra- and inter-specific competition, group cohesion, energy expenditure, habitat use, the spread of zoonotic diseases or species management. We used a radial basis function surface approximation subject to minimum description length constraint to uncover the state-space dynamical systems from time series data. This approximation allowed us to infer structure from a mathematical model of the movement behaviour of sheep and red deer, and the effect of density, thermal stress and vegetation type. Animal movement was recorded using GPS collars deployed in sheep and deer grazing a large experimental plot in winter and summer. Information on the thermal stress to which animals were exposed was estimated using the power consumption of mechanical heated models and meteorological records of a network of stations in the plot. Thermal stress was higher in deer than in sheep, with less differences between species in summer. Deer travelled more distance than sheep, and both species travelled more in summer than in winter; deer travel distance showed less seasonal differences than sheep. Animal movement was better predicted in deer than in sheep and in winter than in summer; both species showed a swarming behaviour in group cohesion, stronger in deer. At shorter separation distances swarming repulsion was stronger between species than within species. At longer separation distances inter-specific attraction was weaker than intra-specific; there was a positive density-dependent effect on swarming, and stronger in deer than in sheep. There was not clear evidence which species attracted or repelled the other; attraction between deer at long separation distances was stronger when the model accounted for thermal stress, but in general the dynamic movement behaviour was hardly affected by the thermal stress. Vegetation type affected intra-species interactions but had little effect on inter
Page, P R
2003-01-01
We review the status of hybrid baryons. The only known way to study hybrids rigorously is via excited adiabatic potentials. Hybrids can be modelled by both the bag and flux-tube models. The low-lying hybrid baryon is N 1/2^+ with a mass of 1.5-1.8 GeV. Hybrid baryons can be produced in the glue-rich processes of diffractive gamma N and pi N production, Psi decays and p pbar annihilation.