A model of optimal voluntary muscular control.
FitzHugh, R
1977-07-19
In the absence of detailed knowledge of how the CNS controls a muscle through its motor fibers, a reasonable hypothesis is that of optimal control. This hypothesis is studied using a simplified mathematical model of a single muscle, based on A.V. Hill's equations, with series elastic element omitted, and with the motor signal represented by a single input variable. Two cost functions were used. The first was total energy expended by the muscle (work plus heat). If the load is a constant force, with no inertia, Hill's optimal velocity of shortening results. If the load includes a mass, analysis by optimal control theory shows that the motor signal to the muscle consists of three phases: (1) maximal stimulation to accelerate the mass to the optimal velocity as quickly as possible, (2) an intermediate level of stimulation to hold the velocity at its optimal value, once reached, and (3) zero stimulation, to permit the mass to slow down, as quickly as possible, to zero velocity at the specified distance shortened. If the latter distance is too small, or the mass too large, the optimal velocity is not reached, and phase (2) is absent. For lengthening, there is no optimal velocity; there are only two phases, zero stimulation followed by maximal stimulation. The second cost function was total time. The optimal control for shortening consists of only phases (1) and (3) above, and is identical to the minimal energy control whenever phase (2) is absent from the latter. Generalization of this model to include viscous loads and a series elastic element are discussed.
Simplified ejector model for control and optimization
International Nuclear Information System (INIS)
Zhu Yinhai; Cai Wenjian; Wen Changyun; Li Yanzhong
2008-01-01
In this paper, a simple yet effective ejector model for a real time control and optimization of an ejector system is proposed. Firstly, a fundamental model for calculation of ejector entrainment ratio at critical working conditions is derived by one-dimensional analysis and the shock circle model. Then, based on thermodynamic principles and the lumped parameter method, the fundamental ejector model is simplified to result in a hybrid ejector model. The model is very simple, which only requires two or three parameters and measurement of two variables to determine the ejector performance. Furthermore, the procedures for on line identification of the model parameters using linear and non-linear least squares methods are also presented. Compared with existing ejector models, the solution of the proposed model is much easier without coupled equations and iterative computations. Finally, the effectiveness of the proposed model is validated by published experimental data. Results show that the model is accurate and robust and gives a better match to the real performances of ejectors over the entire operating range than the existing models. This model is expected to have wide applications in real time control and optimization of ejector systems
Optimization control of LNG regasification plant using Model Predictive Control
Wahid, A.; Adicandra, F. F.
2018-03-01
Optimization of liquified natural gas (LNG) regasification plant is important to minimize costs, especially operational costs. Therefore, it is important to choose optimum LNG regasification plant design and maintaining the optimum operating conditions through the implementation of model predictive control (MPC). Optimal tuning parameter for MPC such as P (prediction horizon), M (control of the horizon) and T (sampling time) are achieved by using fine-tuning method. The optimal criterion for design is the minimum amount of energy used and for control is integral of square error (ISE). As a result, the optimum design is scheme 2 which is developed by Devold with an energy savings of 40%. To maintain the optimum conditions, required MPC with P, M and T as follows: tank storage pressure: 90, 2, 1; product pressure: 95, 2, 1; temperature vaporizer: 65, 2, 2; and temperature heater: 35, 6, 5, with ISE value at set point tracking respectively 0.99, 1792.78, 34.89 and 7.54, or improvement of control performance respectively 4.6%, 63.5%, 3.1% and 58.2% compared to PI controller performance. The energy savings that MPC controllers can make when there is a disturbance in temperature rise 1°C of sea water is 0.02 MW.
Optimal treatment interruptions control of TB transmission model
Nainggolan, Jonner; Suparwati, Titik; Kawuwung, Westy B.
2018-03-01
A tuberculosis model which incorporates treatment interruptions of infectives is established. Optimal control of individuals infected with active TB is given in the model. It is obtained that the control reproduction numbers is smaller than the reproduction number, this means treatment controls could optimize the decrease in the spread of active TB. For this model, controls on treatment of infection individuals to reduce the actively infected individual populations, by application the Pontryagins Maximum Principle for optimal control. The result further emphasized the importance of controlling disease relapse in reducing the number of actively infected and treatment interruptions individuals with tuberculosis.
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
. The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used......Water supply systems consist of a number of pumping stations, which deliver water to the customers via pipeline networks and elevated reservoirs. A huge amount of drinking water is lost before it reaches to end-users due to the leakage in pipe networks. A cost effective solution to reduce leakage...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...
Neutron density optimal control of A-1 reactor analoque model
International Nuclear Information System (INIS)
Grof, V.
1975-01-01
Two applications are described of the optimal control of a reactor analog model. Both cases consider the control of neutron density. Control loops containing the on-line controlled process, the reactor of the first Czechoslovak nuclear power plant A-1, are simulated on an analog computer. Two versions of the optimal control algorithm are derived using modern control theory (Pontryagin's maximum principle, the calculus of variations, and Kalman's estimation theory), the minimum time performance index, and the quadratic performance index. The results of the optimal control analysis are compared with the A-1 reactor conventional control. (author)
An optimal control model of crop thinning in viticulture
Schamel Guenter H.; Schubert Stefan F.
2016-01-01
We develop an economic model of cluster thinning in viticulture to control for grape quantity harvested and grape quality, applying a simple optimal control model with the aim to raise grape quality and related economic profits. The model maximizes vineyard owner profits and allows to discuss two relevant scenarios using a phase diagram analysis: (1) when the initial grape quantity is sufficiently small, thinning grapes will not be optimal and (2) when the initial grape quantity is high enoug...
Desiccant wheel thermal performance modeling for indoor humidity optimal control
International Nuclear Information System (INIS)
Wang, Nan; Zhang, Jiangfeng; Xia, Xiaohua
2013-01-01
Highlights: • An optimal humidity control model is formulated to control the indoor humidity. • MPC strategy is used to implement the optimal operation solution. • Practical applications of the MPC strategy is illustrated by the case study. - Abstract: Thermal comfort is an important concern in the energy efficiency improvement of commercial buildings. Thermal comfort research focuses mostly on temperature control, but humidity control is an important aspect to maintain indoor comfort too. In this paper, an optimal humidity control model (OHCM) is presented. Model predictive control (MPC) strategy is applied to implement the optimal operation of the desiccant wheel during working hours of a commercial building. The OHCM is revised to apply the MPC strategy. A case is studied to illustrate the practical applications of the MPC strategy
Time dependent optimal switching controls in online selling models
Energy Technology Data Exchange (ETDEWEB)
Bradonjic, Milan [Los Alamos National Laboratory; Cohen, Albert [MICHIGAN STATE UNIV
2010-01-01
We present a method to incorporate dishonesty in online selling via a stochastic optimal control problem. In our framework, the seller wishes to maximize her average wealth level W at a fixed time T of her choosing. The corresponding Hamilton-Jacobi-Bellmann (HJB) equation is analyzed for a basic case. For more general models, the admissible control set is restricted to a jump process that switches between extreme values. We propose a new approach, where the optimal control problem is reduced to a multivariable optimization problem.
Optimal Model-Based Control in HVAC Systems
DEFF Research Database (Denmark)
Komareji, Mohammad; Stoustrup, Jakob; Rasmussen, Henrik
2008-01-01
is developed. Then the optimal control structure is designed and implemented. The HVAC system is splitted into two subsystems. By selecting the right set-points and appropriate cost functions for each subsystem controller the optimal control strategy is respected to gaurantee the minimum thermal and electrical......This paper presents optimal model-based control of a heating, ventilating, and air-conditioning (HVAC) system. This HVAC system is made of two heat exchangers: an air-to-air heat exchanger (a rotary wheel heat recovery) and a water-to- air heat exchanger. First dynamic model of the HVAC system...... energy consumption. Finally, the controller is applied to control the mentioned HVAC system and the results show that the expected goals are fulfilled....
Model-based dynamic control and optimization of gas networks
Energy Technology Data Exchange (ETDEWEB)
Hofsten, Kai
2001-07-01
This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum
Optimal control of information epidemics modeled as Maki Thompson rumors
Kandhway, Kundan; Kuri, Joy
2014-12-01
We model the spread of information in a homogeneously mixed population using the Maki Thompson rumor model. We formulate an optimal control problem, from the perspective of single campaigner, to maximize the spread of information when the campaign budget is fixed. Control signals, such as advertising in the mass media, attempt to convert ignorants and stiflers into spreaders. We show the existence of a solution to the optimal control problem when the campaigning incurs non-linear costs under the isoperimetric budget constraint. The solution employs Pontryagin's Minimum Principle and a modified version of forward backward sweep technique for numerical computation to accommodate the isoperimetric budget constraint. The techniques developed in this paper are general and can be applied to similar optimal control problems in other areas. We have allowed the spreading rate of the information epidemic to vary over the campaign duration to model practical situations when the interest level of the population in the subject of the campaign changes with time. The shape of the optimal control signal is studied for different model parameters and spreading rate profiles. We have also studied the variation of the optimal campaigning costs with respect to various model parameters. Results indicate that, for some model parameters, significant improvements can be achieved by the optimal strategy compared to the static control strategy. The static strategy respects the same budget constraint as the optimal strategy and has a constant value throughout the campaign horizon. This work finds application in election and social awareness campaigns, product advertising, movie promotion and crowdfunding campaigns.
An optimal control model of crop thinning in viticulture
Directory of Open Access Journals (Sweden)
Schamel Guenter H.
2016-01-01
Full Text Available We develop an economic model of cluster thinning in viticulture to control for grape quantity harvested and grape quality, applying a simple optimal control model with the aim to raise grape quality and related economic profits. The model maximizes vineyard owner profits and allows to discuss two relevant scenarios using a phase diagram analysis: (1 when the initial grape quantity is sufficiently small, thinning grapes will not be optimal and (2 when the initial grape quantity is high enough, it is optimal to thin grapes from the beginning of the relevant planning horizon and to reduce the quantity over time until the stock of grapes arrives at its optimum. Depending on the model's parameters, the “stopping time” for thinning grapes is reached sooner or later. After the stopping time, grape quantity evolves solely according to natural decay. The results relate to observed dynamics in viticulture and for other horticultural crops.
Real-Time Optimization for Economic Model Predictive Control
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Edlund, Kristian; Frison, Gianluca
2012-01-01
In this paper, we develop an efficient homogeneous and self-dual interior-point method for the linear programs arising in economic model predictive control. To exploit structure in the optimization problems, the algorithm employs a highly specialized Riccati iteration procedure. Simulations show...
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.
Optimal control in a model of malaria with differential susceptibility
Hincapié, Doracelly; Ospina, Juan
2014-06-01
A malaria model with differential susceptibility is analyzed using the optimal control technique. In the model the human population is classified as susceptible, infected and recovered. Susceptibility is assumed dependent on genetic, physiological, or social characteristics that vary between individuals. The model is described by a system of differential equations that relate the human and vector populations, so that the infection is transmitted to humans by vectors, and the infection is transmitted to vectors by humans. The model considered is analyzed using the optimal control method when the control consists in using of insecticide-treated nets and educational campaigns; and the optimality criterion is to minimize the number of infected humans, while keeping the cost as low as is possible. One first goal is to determine the effects of differential susceptibility in the proposed control mechanism; and the second goal is to determine the algebraic form of the basic reproductive number of the model. All computations are performed using computer algebra, specifically Maple. It is claimed that the analytical results obtained are important for the design and implementation of control measures for malaria. It is suggested some future investigations such as the application of the method to other vector-borne diseases such as dengue or yellow fever; and also it is suggested the possible application of free software of computer algebra like Maxima.
Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO
Directory of Open Access Journals (Sweden)
Adel Taieb
2017-01-01
Full Text Available This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO Fuzzy Optimal Model Predictive Control (FOMPC using the Adaptive Particle Swarm Optimization (APSO algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR and Tank system, where the proposed approach provides better performances compared with other methods.
A model for HIV/AIDS pandemic with optimal control
Sule, Amiru; Abdullah, Farah Aini
2015-05-01
Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) is pandemic. It has affected nearly 60 million people since the detection of the disease in 1981 to date. In this paper basic deterministic HIV/AIDS model with mass action incidence function are developed. Stability analysis is carried out. And the disease free equilibrium of the basic model was found to be locally asymptotically stable whenever the threshold parameter (RO) value is less than one, and unstable otherwise. The model is extended by introducing two optimal control strategies namely, CD4 counts and treatment for the infective using optimal control theory. Numerical simulation was carried out in order to illustrate the analytic results.
Optimal Control of Drug Therapy in a Hepatitis B Model
Directory of Open Access Journals (Sweden)
Jonathan E. Forde
2016-08-01
Full Text Available Combination antiviral drug therapy improves the survival rates of patients chronically infected with hepatitis B virus by controlling viral replication and enhancing immune responses. Some of these drugs have side effects that make them unsuitable for long-term administration. To address the trade-off between the positive and negative effects of the combination therapy, we investigated an optimal control problem for a delay differential equation model of immune responses to hepatitis virus B infection. Our optimal control problem investigates the interplay between virological and immunomodulatory effects of therapy, the control of viremia and the administration of the minimal dosage over a short period of time. Our numerical results show that the high drug levels that induce immune modulation rather than suppression of virological factors are essential for the clearance of hepatitis B virus.
Correlations in state space can cause sub-optimal adaptation of optimal feedback control models
Aprasoff, Jonathan; Donchin, Opher
2011-01-01
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 feedb...
Developments in model-based optimization and control distributed control and industrial applications
Grancharova, Alexandra; Pereira, Fernando
2015-01-01
This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and desi...
Modelling of Rabies Transmission Dynamics Using Optimal Control Analysis
Directory of Open Access Journals (Sweden)
Joshua Kiddy K. Asamoah
2017-01-01
Full Text Available We examine an optimal way of eradicating rabies transmission from dogs into the human population, using preexposure prophylaxis (vaccination and postexposure prophylaxis (treatment due to public education. We obtain the disease-free equilibrium, the endemic equilibrium, the stability, and the sensitivity analysis of the optimal control model. Using the Latin hypercube sampling (LHS, the forward-backward sweep scheme and the fourth-order Range-Kutta numerical method predict that the global alliance for rabies control’s aim of working to eliminate deaths from canine rabies by 2030 is attainable through mass vaccination of susceptible dogs and continuous use of pre- and postexposure prophylaxis in humans.
Dynamics of underactuated multibody systems modeling, control and optimal design
Seifried, Robert
2014-01-01
Underactuated multibody systems are intriguing mechatronic systems, as they possess fewer control inputs than degrees of freedom. Some examples are modern light-weight flexible robots and articulated manipulators with passive joints. This book investigates such underactuated multibody systems from an integrated perspective. This includes all major steps from the modeling of rigid and flexible multibody systems, through nonlinear control theory, to optimal system design. The underlying theories and techniques from these different fields are presented using a self-contained and unified approach and notation system. Subsequently, the book focuses on applications to large multibody systems with multiple degrees of freedom, which require a combination of symbolical and numerical procedures. Finally, an integrated, optimization-based design procedure is proposed, whereby both structural and control design are considered concurrently. Each chapter is supplemented by illustrated examples.
Web malware spread modelling and optimal control strategies
Liu, Wanping; Zhong, Shouming
2017-02-01
The popularity of the Web improves the growth of web threats. Formulating mathematical models for accurate prediction of malicious propagation over networks is of great importance. The aim of this paper is to understand the propagation mechanisms of web malware and the impact of human intervention on the spread of malicious hyperlinks. Considering the characteristics of web malware, a new differential epidemic model which extends the traditional SIR model by adding another delitescent compartment is proposed to address the spreading behavior of malicious links over networks. The spreading threshold of the model system is calculated, and the dynamics of the model is theoretically analyzed. Moreover, the optimal control theory is employed to study malware immunization strategies, aiming to keep the total economic loss of security investment and infection loss as low as possible. The existence and uniqueness of the results concerning the optimality system are confirmed. Finally, numerical simulations show that the spread of malware links can be controlled effectively with proper control strategy of specific parameter choice.
Optimization and Optimal Control
Chinchuluun, Altannar; Enkhbat, Rentsen; Tseveendorj, Ider
2010-01-01
During the last four decades there has been a remarkable development in optimization and optimal control. Due to its wide variety of applications, many scientists and researchers have paid attention to fields of optimization and optimal control. A huge number of new theoretical, algorithmic, and computational results have been observed in the last few years. This book gives the latest advances, and due to the rapid development of these fields, there are no other recent publications on the same topics. Key features: Provides a collection of selected contributions giving a state-of-the-art accou
Multi-model Simulation for Optimal Control of Aeroacoustics.
Energy Technology Data Exchange (ETDEWEB)
Collis, Samuel Scott; Chen, Guoquan
2005-05-01
Flow-generated noise, especially rotorcraft noise has been a serious concern for bothcommercial and military applications. A particular important noise source for rotor-craft is Blade-Vortex-Interaction (BVI)noise, a high amplitude, impulsive sound thatoften dominates other rotorcraft noise sources. Usually BVI noise is caused by theunsteady flow changes around various rotor blades due to interactions with vorticespreviously shed by the blades. A promising approach for reducing the BVI noise isto use on-blade controls, such as suction/blowing, micro-flaps/jets, and smart struc-tures. Because the design and implementation of such experiments to evaluate suchsystems are very expensive, efficient computational tools coupled with optimal con-trol systems are required to explore the relevant physics and evaluate the feasibilityof using various micro-fluidic devices before committing to hardware.In this thesis the research is to formulate and implement efficient computationaltools for the development and study of optimal control and design strategies for com-plex flow and acoustic systems with emphasis on rotorcraft applications, especiallyBVI noise control problem. The main purpose of aeroacoustic computations is todetermine the sound intensity and directivity far away from the noise source. How-ever, the computational cost of using a high-fidelity flow-physics model across thefull domain is usually prohibitive and itmight also be less accurate because of thenumerical diffusion and other problems. Taking advantage of the multi-physics andmulti-scale structure of this aeroacoustic problem, we develop a multi-model, multi-domain (near-field/far-field) method based on a discontinuous Galerkin discretiza-tion. In this approach the coupling of multi-domains and multi-models is achievedby weakly enforcing continuity of normal fluxes across a coupling surface. For ourinterested aeroacoustics control problem, the adjoint equations that determine thesensitivity of the cost
Aschepkov, Leonid T; Kim, Taekyun; Agarwal, Ravi P
2016-01-01
This book is based on lectures from a one-year course at the Far Eastern Federal University (Vladivostok, Russia) as well as on workshops on optimal control offered to students at various mathematical departments at the university level. The main themes of the theory of linear and nonlinear systems are considered, including the basic problem of establishing the necessary and sufficient conditions of optimal processes. In the first part of the course, the theory of linear control systems is constructed on the basis of the separation theorem and the concept of a reachability set. The authors prove the closure of a reachability set in the class of piecewise continuous controls, and the problems of controllability, observability, identification, performance and terminal control are also considered. The second part of the course is devoted to nonlinear control systems. Using the method of variations and the Lagrange multipliers rule of nonlinear problems, the authors prove the Pontryagin maximum principle for prob...
Self-optimizing robust nonlinear model predictive control
Lazar, M.; Heemels, W.P.M.H.; Jokic, A.; Thoma, M.; Allgöwer, F.; Morari, M.
2009-01-01
This paper presents a novel method for designing robust MPC schemes that are self-optimizing in terms of disturbance attenuation. The method employs convex control Lyapunov functions and disturbance bounds to optimize robustness of the closed-loop system on-line, at each sampling instant - a unique
Optimal control of suspended sediment distribution model of Talaga lake
Ratianingsih, R.; Resnawati, Azim, Mardlijah, Widodo, B.
2017-08-01
Talaga Lake is one of several lakes in Central Sulawesi that potentially to be managed in multi purposes scheme because of its characteristic. The scheme is addressed not only due to the lake maintenance because of its sediment but also due to the Algae farming for its biodiesel fuel. This paper governs a suspended sediment distribution model of Talaga lake. The model is derived from the two dimensional hydrodynamic shallow water equations of the mass and momentum conservation law of sediment transport. An order reduction of the model gives six equations of hyperbolic systems of the depth, two dimension directional velocities and sediment concentration while the bed elevation as the second order of turbulent diffusion and dispersion are neglected. The system is discreted and linearized such that could be solved numerically by box-Keller method for some initial and boundary condition. The solutions shows that the downstream velocity is play a role in transversal direction of stream function flow. The downstream accumulated sediment indicate that the suspended sediment and its changing should be controlled by optimizing the downstream velocity and transversal suspended sediment changing due to the ideal algae growth need.
Existence and characterization of optimal control in mathematics model of diabetics population
Permatasari, A. H.; Tjahjana, R. H.; Udjiani, T.
2018-03-01
Diabetes is a chronic disease with a huge burden affecting individuals and the whole society. In this paper, we constructed the optimal control mathematical model by applying a strategy to control the development of diabetic population. The constructed mathematical model considers the dynamics of disabled people due to diabetes. Moreover, an optimal control approach is proposed in order to reduce the burden of pre-diabetes. Implementation of control is done by preventing the pre-diabetes develop into diabetics with and without complications. The existence of optimal control and characterization of optimal control is discussed in this paper. Optimal control is characterized by applying the Pontryagin minimum principle. The results indicate that there is an optimal control in optimization problem in mathematics model of diabetic population. The effect of the optimal control variable (prevention) is strongly affected by the number of healthy people.
Adaptive Flight Control Design with Optimal Control Modification on an F-18 Aircraft Model
Burken, John J.; Nguyen, Nhan T.; Griffin, Brian J.
2010-01-01
In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to as the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly; however, a large adaptive gain can lead to high-frequency oscillations which can adversely affect the robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient robustness. A damping term (v) is added in the modification to increase damping as needed. Simulations were conducted on a damaged F-18 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) with both the standard baseline dynamic inversion controller and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model.
The analysis of optimal singular controls for SEIR model of tuberculosis
Marpaung, Faridawaty; Rangkuti, Yulita M.; Sinaga, Marlina S.
2014-12-01
The optimally of singular control for SEIR model of Tuberculosis is analyzed. There are controls that correspond to time of the vaccination and treatment schedule. The optimally of singular control is obtained by differentiate a switching function of the model. The result shows that vaccination and treatment control are singular.
Optimal Stochastic Modeling and Control of Flexible Structures
1988-09-01
1.37] and McLane [1.18] considered multivariable systems and derived their optimal control characteristics. Kleinman, Gorman and Zaborsky considered...Leondes [1.72,1.73] studied various aspects of multivariable linear stochastic, discrete-time systems that are partly deterministic, and partly stochastic...June 1966. 1.8. A.V. Balaknishnan, Applied Functional Analaysis , 2nd ed., New York, N.Y.: Springer-Verlag, 1981 1.9. Peter S. Maybeck, Stochastic
Directory of Open Access Journals (Sweden)
Sie Long Kek
2015-01-01
Full Text Available A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation are integrated interactively. On the other hand, the output is measured from the real plant and is fed back into the parameter estimation problem to establish a matching scheme. During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.
An optimal control model for load shifting - With application in the energy management of a colliery
International Nuclear Information System (INIS)
Middelberg, Arno; Zhang Jiangfeng; Xia Xiaohua
2009-01-01
This paper presents an optimal control model for the load shifting problem in energy management and its application in a South African colliery. It is illustrated in the colliery scenario that how the optimal control model can be applied to optimize load shifting and improve energy efficiency through the control of conveyor belts. The time-of-use electricity tariff is used as an input to the objective function in order to obtain a solution that minimizes electricity costs and thus maximizes load shifting. The case study yields promising results that show the potential of applying this optimal control model to other industrial Demand Side Management initiatives
Energy Optimal Path Planning: Integrating Coastal Ocean Modelling with Optimal Control
Subramani, D. N.; Haley, P. J., Jr.; Lermusiaux, P. F. J.
2016-02-01
A stochastic optimization methodology is formulated for computing energy-optimal paths from among time-optimal paths of autonomous vehicles navigating in a dynamic flow field. To set up the energy optimization, the relative vehicle speed and headings are considered to be stochastic, and new stochastic Dynamically Orthogonal (DO) level-set equations that govern their stochastic time-optimal reachability fronts are derived. Their solution provides the distribution of time-optimal reachability fronts and corresponding distribution of time-optimal paths. An optimization is then performed on the vehicle's energy-time joint distribution to select the energy-optimal paths for each arrival time, among all stochastic time-optimal paths for that arrival time. The accuracy and efficiency of the DO level-set equations for solving the governing stochastic level-set reachability fronts are quantitatively assessed, including comparisons with independent semi-analytical solutions. Energy-optimal missions are studied in wind-driven barotropic quasi-geostrophic double-gyre circulations, and in realistic data-assimilative re-analyses of multiscale coastal ocean flows. The latter re-analyses are obtained from multi-resolution 2-way nested primitive-equation simulations of tidal-to-mesoscale dynamics in the Middle Atlantic Bight and Shelbreak Front region. The effects of tidal currents, strong wind events, coastal jets, and shelfbreak fronts on the energy-optimal paths are illustrated and quantified. Results showcase the opportunities for longer-duration missions that intelligently utilize the ocean environment to save energy, rigorously integrating ocean forecasting with optimal control of autonomous vehicles.
Dynamic optimization and robust explicit model predictive control of hydrogen storage tank
Panos, C.
2010-09-01
We present a general framework for the optimal design and control of a metal-hydride bed under hydrogen desorption operation. The framework features: (i) a detailed two-dimension dynamic process model, (ii) a design and operational dynamic optimization step, and (iii) an explicit/multi-parametric model predictive controller design step. For the controller design, a reduced order approximate model is obtained, based on which nominal and robust multi-parametric controllers are designed. © 2010 Elsevier Ltd.
Dynamic optimization and robust explicit model predictive control of hydrogen storage tank
Panos, C.; Kouramas, K.I.; Georgiadis, M.C.; Pistikopoulos, E.N.
2010-01-01
We present a general framework for the optimal design and control of a metal-hydride bed under hydrogen desorption operation. The framework features: (i) a detailed two-dimension dynamic process model, (ii) a design and operational dynamic optimization step, and (iii) an explicit/multi-parametric model predictive controller design step. For the controller design, a reduced order approximate model is obtained, based on which nominal and robust multi-parametric controllers are designed. © 2010 Elsevier Ltd.
Deng, Lujuan; Xie, Songhe; Cui, Jiantao; Liu, Tao
2006-11-01
It is the essential goal of intelligent greenhouse environment optimal control to enhance income of cropper and energy save. There were some characteristics such as uncertainty, imprecision, nonlinear, strong coupling, bigger inertia and different time scale in greenhouse environment control system. So greenhouse environment optimal control was not easy and especially model-based optimal control method was more difficult. So the optimal control problem of plant environment in intelligent greenhouse was researched. Hierarchical greenhouse environment control system was constructed. In the first level data measuring was carried out and executive machine was controlled. Optimal setting points of climate controlled variable in greenhouse was calculated and chosen in the second level. Market analysis and planning were completed in third level. The problem of the optimal setting point was discussed in this paper. Firstly the model of plant canopy photosynthesis responses and the model of greenhouse climate model were constructed. Afterwards according to experience of the planting expert, in daytime the optimal goals were decided according to the most maximal photosynthesis rate principle. In nighttime on plant better growth conditions the optimal goals were decided by energy saving principle. Whereafter environment optimal control setting points were computed by GA. Compared the optimal result and recording data in real system, the method is reasonable and can achieve energy saving and the maximal photosynthesis rate in intelligent greenhouse
An Optimal Control Modification to Model-Reference Adaptive Control for Fast Adaptation
Nguyen, Nhan T.; Krishnakumar, Kalmanje; Boskovic, Jovan
2008-01-01
This paper presents a method that can achieve fast adaptation for a class of model-reference adaptive control. It is well-known that standard model-reference adaptive control exhibits high-gain control behaviors when a large adaptive gain is used to achieve fast adaptation in order to reduce tracking error rapidly. High gain control creates high-frequency oscillations that can excite unmodeled dynamics and can lead to instability. The fast adaptation approach is based on the minimization of the squares of the tracking error, which is formulated as an optimal control problem. The necessary condition of optimality is used to derive an adaptive law using the gradient method. This adaptive law is shown to result in uniform boundedness of the tracking error by means of the Lyapunov s direct method. Furthermore, this adaptive law allows a large adaptive gain to be used without causing undesired high-gain control effects. The method is shown to be more robust than standard model-reference adaptive control. Simulations demonstrate the effectiveness of the proposed method.
Domestic appliances energy optimization with model predictive control
International Nuclear Information System (INIS)
Rodrigues, E.M.G.; Godina, R.; Pouresmaeil, E.; Ferreira, J.R.; Catalão, J.P.S.
2017-01-01
Highlights: • An alternative power management control for home appliances that require thermal regulation is presented. • A Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat. • Problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. • A modulation scheme of a two-level Model Predictive Control signal as an interface block is presented. • The implementation costs in home appliances with thermal regulation requirements are reduced. - Abstract: A vital element in making a sustainable world is correctly managing the energy in the domestic sector. Thus, this sector evidently stands as a key one for to be addressed in terms of climate change goals. Increasingly, people are aware of electricity savings by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Much of the reduction was due to technological improvements, however with the advancing of the years new types of control arise. Domestic appliances with the purpose of heating and cooling rely on thermostatic regulation technique. The study in this paper is focused on the subject of an alternative power management control for home appliances that require thermal regulation. In this paper a Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat with the aim of minimizing the cooling energy consumption through the minimization of the energy cost while satisfying the adequate temperature range for the human comfort. In addition, the Model Predictive Control problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. For this purpose, the typical consumption of a 24 h period of a summer day was simulated a three-level tariff scheme was used. The new
Centralised gaming models: providing optimal gambling behaviour controls
Griffiths, MD; Wood, RTA
2009-01-01
The expansion in the gaming industry and its widening attraction points to the need for ever more verifiable means of controlling problem gambling. Various strategies have been built into casino venue operations to address this, but recently, following a new focus on social responsibility, a group of experts considered the possibilities of a centralised gaming model as a more effective control mechanism for dealing with gambling behaviours.
Oguz, Ozgur S; Zhou, Zhehua; Glasauer, Stefan; Wollherr, Dirk
2018-04-03
Human motor control is highly efficient in generating accurate and appropriate motor behavior for a multitude of tasks. This paper examines how kinematic and dynamic properties of the musculoskeletal system are controlled to achieve such efficiency. Even though recent studies have shown that the human motor control relies on multiple models, how the central nervous system (CNS) controls this combination is not fully addressed. In this study, we utilize an Inverse Optimal Control (IOC) framework in order to find the combination of those internal models and how this combination changes for different reaching tasks. We conducted an experiment where participants executed a comprehensive set of free-space reaching motions. The results show that there is a trade-off between kinematics and dynamics based controllers depending on the reaching task. In addition, this trade-off depends on the initial and final arm configurations, which in turn affect the musculoskeletal load to be controlled. Given this insight, we further provide a discomfort metric to demonstrate its influence on the contribution of different inverse internal models. This formulation together with our analysis not only support the multiple internal models (MIMs) hypothesis but also suggest a hierarchical framework for the control of human reaching motions by the CNS.
International Nuclear Information System (INIS)
Severin, V.P.
2007-01-01
The mathematical modeling of automatic control systems of reactor facility WWER-1000 with various regulator types is considered. The linear and nonlinear models of neutron power control systems of nuclear reactor WWER-1000 with various group numbers of delayed neutrons are designed. The results of optimization of direct quality indexes of neutron power control systems of nuclear reactor WWER-1000 are designed. The identification and optimization of level control systems with various regulator types of steam generator are executed
DEFF Research Database (Denmark)
Mørkholt, Jakob
1997-01-01
Optimal feedback control of broadband sound radiation from a rectangular baffled panel has been investigated through computer simulations. Special emphasis has been put on the sensitivity of the optimal feedback control to uncertainties in the modelling of the system under control.A model...... in terms of a set of radiation filters modelling the radiation dynamics.Linear quadratic feedback control applied to the panel in order to minimise the radiated sound power has then been simulated. The sensitivity of the model based controller to modelling uncertainties when using feedback from actual...
Combustion Model and Control Parameter Optimization Methods for Single Cylinder Diesel Engine
Directory of Open Access Journals (Sweden)
Bambang Wahono
2014-01-01
Full Text Available This research presents a method to construct a combustion model and a method to optimize some control parameters of diesel engine in order to develop a model-based control system. The construction purpose of the model is to appropriately manage some control parameters to obtain the values of fuel consumption and emission as the engine output objectives. Stepwise method considering multicollinearity was applied to construct combustion model with the polynomial model. Using the experimental data of a single cylinder diesel engine, the model of power, BSFC, NOx, and soot on multiple injection diesel engines was built. The proposed method succesfully developed the model that describes control parameters in relation to the engine outputs. Although many control devices can be mounted to diesel engine, optimization technique is required to utilize this method in finding optimal engine operating conditions efficiently beside the existing development of individual emission control methods. Particle swarm optimization (PSO was used to calculate control parameters to optimize fuel consumption and emission based on the model. The proposed method is able to calculate control parameters efficiently to optimize evaluation item based on the model. Finally, the model which added PSO then was compiled in a microcontroller.
Optimal speech motor control and token-to-token variability: a Bayesian modeling approach.
Patri, Jean-François; Diard, Julien; Perrier, Pascal
2015-12-01
The remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the central nervous system selects one of the possible strategies, a common approach, in line with optimal motor control theories, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability. The present paper proposes an alternative approach by formulating feedforward optimal control in a probabilistic Bayesian modeling framework. This is illustrated by controlling a biomechanical model of the vocal tract for speech production and by comparing it with an existing optimal control model (GEPPETO). The essential elements of this optimal control model are presented first. From them the Bayesian model is constructed in a progressive way. Performance of the Bayesian model is evaluated based on computer simulations and compared to the optimal control model. This approach is shown to be appropriate for solving the speech planning problem while accounting for variability in a principled way.
Development of an Optimizing Control Concept for Fossil-Fired Boilers using a Simulation Model
DEFF Research Database (Denmark)
Mortensen, J. H.; Mølbak, T.; Commisso, M.B.
1997-01-01
of implementation and commissioning. The optimizing control system takes into account the multivariable and nonlinear characteristics of the boiler process as a gain-scheduled LQG-controller is utilized. For the purpose of facilitating the control concept development a dynamic simulation model of the boiler process......An optimizing control system for improving the load following capabilities of power plant units has been developed. The system is implemented as a complement producing additive control signals to the existing boiler control system, a concept which has various practical advantages in terms...... model when designing a new control concept are discussed....
Dynamic Modeling, Model-Based Control, and Optimization of Solid Oxide Fuel Cells
Spivey, Benjamin James
2011-07-01
Solid oxide fuel cells are a promising option for distributed stationary power generation that offers efficiencies ranging from 50% in stand-alone applications to greater than 80% in cogeneration. To advance SOFC technology for widespread market penetration, the SOFC should demonstrate improved cell lifetime and load-following capability. This work seeks to improve lifetime through dynamic analysis of critical lifetime variables and advanced control algorithms that permit load-following while remaining in a safe operating zone based on stress analysis. Control algorithms typically have addressed SOFC lifetime operability objectives using unconstrained, single-input-single-output control algorithms that minimize thermal transients. Existing SOFC controls research has not considered maximum radial thermal gradients or limits on absolute temperatures in the SOFC. In particular, as stress analysis demonstrates, the minimum cell temperature is the primary thermal stress driver in tubular SOFCs. This dissertation presents a dynamic, quasi-two-dimensional model for a high-temperature tubular SOFC combined with ejector and prereformer models. The model captures dynamics of critical thermal stress drivers and is used as the physical plant for closed-loop control simulations. A constrained, MIMO model predictive control algorithm is developed and applied to control the SOFC. Closed-loop control simulation results demonstrate effective load-following, constraint satisfaction for critical lifetime variables, and disturbance rejection. Nonlinear programming is applied to find the optimal SOFC size and steady-state operating conditions to minimize total system costs.
Toulabi, Mohammadreza; Bahrami, Shahab; Ranjbar, Ali Mohammad
2018-03-01
In most of the existing studies, the frequency response in the variable speed wind turbines (VSWTs) is simply realized by changing the torque set-point via appropriate inputs such as frequency deviations signal. However, effective dynamics and systematic process design have not been comprehensively discussed yet. Accordingly, this paper proposes a proportional-derivative frequency controller and investigates its performance in a wind farm consisting of several VSWTs. A band-pass filter is deployed before the proposed controller to avoid responding to either steady state frequency deviations or high rate of change of frequency. To design the controller, the frequency model of the wind farm is first characterized. The proposed controller is then designed based on the obtained open loop system. The stability region associated with the controller parameters is analytically determined by decomposing the closed-loop system's characteristic polynomial into the odd and even parts. The performance of the proposed controller is evaluated through extensive simulations in MATLAB/Simulink environment in a power system comprising a high penetration of VSWTs equipped with the proposed controller. Finally, based on the obtained feasible area and appropriate objective function, the optimal values associated with the controller parameters are determined using the genetic algorithm (GA). Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Prediction Model of Battery State of Charge and Control Parameter Optimization for Electric Vehicle
Directory of Open Access Journals (Sweden)
Bambang Wahono
2015-07-01
Full Text Available This paper presents the construction of a battery state of charge (SOC prediction model and the optimization method of the said model to appropriately control the number of parameters in compliance with the SOC as the battery output objectives. Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences has tested its electric vehicle research prototype on the road, monitoring its voltage, current, temperature, time, vehicle velocity, motor speed, and SOC during the operation. Using this experimental data, the prediction model of battery SOC was built. Stepwise method considering multicollinearity was able to efficiently develops the battery prediction model that describes the multiple control parameters in relation to the characteristic values such as SOC. It was demonstrated that particle swarm optimization (PSO succesfully and efficiently calculated optimal control parameters to optimize evaluation item such as SOC based on the model.
Energy Technology Data Exchange (ETDEWEB)
Braun, Jan; Hoffmann, Frank; Krettek, Johannes; Bertram, Torsten [Technische Univ. Dortmund (Germany). Lehrstuhl RST
2009-07-01
Evolutionary algorithms require a large number of fitness evaluations in order to find an optimal solution. This property limits their application to hardware in the loop optimization or optimization of time-consuming simulations and calculations. This contribution proposes a preselection with data based models in order to reduce the number of true fitness evaluations. It extends previous approaches for model assisted scalar optimization to multiobjective problems by a proper redefinition of model quality and ?-control. The application to multiobjective benchmark optimization problems underlies the improved convergence of the model assisted evolution strategy compared to a multiobjective evolution strategy as well as the advantages of a {lambda}-controlled variant compared to a static preselection. (orig.)
Free terminal time optimal control problem of an HIV model based on a conjugate gradient method.
Jang, Taesoo; Kwon, Hee-Dae; Lee, Jeehyun
2011-10-01
The minimum duration of treatment periods and the optimal multidrug therapy for human immunodeficiency virus (HIV) type 1 infection are considered. We formulate an optimal tracking problem, attempting to drive the states of the model to a "healthy" steady state in which the viral load is low and the immune response is strong. We study an optimal time frame as well as HIV therapeutic strategies by analyzing the free terminal time optimal tracking control problem. The minimum duration of treatment periods and the optimal multidrug therapy are found by solving the corresponding optimality systems with the additional transversality condition for the terminal time. We demonstrate by numerical simulations that the optimal dynamic multidrug therapy can lead to the long-term control of HIV by the strong immune response after discontinuation of therapy.
Optimal control of nutrition restricted dynamics model of Microalgae biomass growth model
Ratianingsih, R.; Azim; Nacong, N.; Resnawati; Mardlijah; Widodo, B.
2017-12-01
The biomass of the microalgae is very potential to be proposed as an alternative renewable energy resources because it could be extracted into lipid. Afterward, the lipid could be processed to get the biodiesel or bioethanol. The extraction of the biomass on lipid synthesis process is very important to be studied because the process just gives some amount of lipid. A mathematical model of restricted microalgae biomass growth just gives 1/3 proportion of lipid with respect to the biomass in the synthesis process. An optimal control is designed to raise the ratio between the number of lipid formation and the microalgae biomass to be used in synthesis process. The minimum/ Pontryagin maximum principle is used to get the optimal lipid production. The simulation shows that the optimal lipid formation could be reach by simultaneously controlling the carbon dioxide, in the respiration and photosynthesis the process, and intake nutrition rates of liquid waste and urea substrate. The production of controlled microalgae lipid could be increase 6.5 times comparing to the uncontrolled one.
Complex fluid network optimization and control integrative design based on nonlinear dynamic model
International Nuclear Information System (INIS)
Sui, Jinxue; Yang, Li; Hu, Yunan
2016-01-01
In view of distribution according to complex fluid network’s needs, this paper proposed one optimization computation method of the nonlinear programming mathematical model based on genetic algorithm. The simulation result shows that the overall energy consumption of the optimized fluid network has a decrease obviously. The control model of the fluid network is established based on nonlinear dynamics. We design the control law based on feedback linearization, take the optimal value by genetic algorithm as the simulation data, can also solve the branch resistance under the optimal value. These resistances can provide technical support and reference for fluid network design and construction, so can realize complex fluid network optimization and control integration design.
An optimal control strategies using vaccination and fogging in dengue fever transmission model
Fitria, Irma; Winarni, Pancahayani, Sigit; Subchan
2017-08-01
This paper discussed regarding a model and an optimal control problem of dengue fever transmission. We classified the model as human and vector (mosquito) population classes. For the human population, there are three subclasses, such as susceptible, infected, and resistant classes. Then, for the vector population, we divided it into wiggler, susceptible, and infected vector classes. Thus, the model consists of six dynamic equations. To minimize the number of dengue fever cases, we designed two optimal control variables in the model, the giving of fogging and vaccination. The objective function of this optimal control problem is to minimize the number of infected human population, the number of vector, and the cost of the controlling efforts. By giving the fogging optimally, the number of vector can be minimized. In this case, we considered the giving of vaccination as a control variable because it is one of the efforts that are being developed to reduce the spreading of dengue fever. We used Pontryagin Minimum Principle to solve the optimal control problem. Furthermore, the numerical simulation results are given to show the effect of the optimal control strategies in order to minimize the epidemic of dengue fever.
Optimal closed-loop identification test design for internal model control
Zhu, Y.; Bosch, van den P.P.J.
2000-01-01
In this work, optimal closed-loop test design for control is studied. Simple design formulas are derived based on the asymptotic theory of Ljung. The control scheme used is internal model control (IMC) and the design constraint is the power of the process output or that of the reference signal. The
Optimal control for mathematical models of cancer therapies an application of geometric methods
Schättler, Heinz
2015-01-01
This book presents applications of geometric optimal control to real life biomedical problems with an emphasis on cancer treatments. A number of mathematical models for both classical and novel cancer treatments are presented as optimal control problems with the goal of constructing optimal protocols. The power of geometric methods is illustrated with fully worked out complete global solutions to these mathematically challenging problems. Elaborate constructions of optimal controls and corresponding system responses provide great examples of applications of the tools of geometric optimal control and the outcomes aid the design of simpler, practically realizable suboptimal protocols. The book blends mathematical rigor with practically important topics in an easily readable tutorial style. Graduate students and researchers in science and engineering, particularly biomathematics and more mathematical aspects of biomedical engineering, would find this book particularly useful.
Isolation strategy of a two-strain avian influenza model using optimal control
Mardlijah, Ariani, Tika Desi; Asfihani, Tahiyatul
2017-08-01
Avian influenza has killed many victims of both birds and humans. Most cases of avian influenza infection in humans have resulted transmission from poultry to humans. To prevent or minimize the patients of avian influenza can be done by pharmaceutical and non-pharmaceutical measures such as the use of masks, isolation, etc. We will be analyzed two strains of avian influenza models that focus on treatment of symptoms with insulation, then investigate the stability of the equilibrium point by using Routh-Hurwitz criteria. We also used optimal control to reduce the number of humans infected by making the isolation level as the control then proceeds optimal control will be simulated. The completion of optimal control used in this study is the Pontryagin Minimum Principle and for simulation we are using Runge Kutta method. The results obtained showed that the application of two control is more optimal compared to apply one control only.
Dynamic Modeling, Optimization, and Advanced Control for Large Scale Biorefineries
DEFF Research Database (Denmark)
Prunescu, Remus Mihail
with a complex conversion route. Computational fluid dynamics is used to model transport phenomena in large reactors capturing tank profiles, and delays due to plug flows. This work publishes for the first time demonstration scale real data for validation showing that the model library is suitable...
Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler
Directory of Open Access Journals (Sweden)
Zhenhao Tang
2017-01-01
Full Text Available To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process.
Optimization of inverse model identification for multi-axial test rig control
Directory of Open Access Journals (Sweden)
Müller Tino
2016-01-01
Full Text Available Laboratory testing of multi-axial fatigue situations improves repeatability and allows a time condensing of tests which can be carried out until component failure, compared to field testing. To achieve realistic and convincing durability results, precise load data reconstruction is necessary. Cross-talk and a high number of degrees of freedom negatively affect the control accuracy. Therefore a multiple input/multiple output (MIMO model of the system, capturing all inherent cross-couplings is identified. In a first step the model order is estimated based on the physical fundamentals of a one channel hydraulic-servo system. Subsequently, the structure of the MIMO model is optimized using correlation of the outputs, to increase control stability and reduce complexity of the parameter optimization. The identification process is successfully applied to the iterative control of a multi-axial suspension rig. The results show accurate control, with increased stability compared to control without structure optimization.
Yang, Chenguang; Li, Zhijun; Li, Jing
2013-02-01
In this paper, we investigate optimized adaptive control and trajectory generation for a class of wheeled inverted pendulum (WIP) models of vehicle systems. Aiming at shaping the controlled vehicle dynamics to be of minimized motion tracking errors as well as angular accelerations, we employ the linear quadratic regulation optimization technique to obtain an optimal reference model. Adaptive control has then been developed using variable structure method to ensure the reference model to be exactly matched in a finite-time horizon, even in the presence of various internal and external uncertainties. The minimized yaw and tilt angular accelerations help to enhance the vehicle rider's comfort. In addition, due to the underactuated mechanism of WIP, the vehicle forward velocity dynamics cannot be controlled separately from the pendulum tilt angle dynamics. Inspired by the control strategy of human drivers, who usually manipulate the tilt angle to control the forward velocity, we design a neural-network-based adaptive generator of implicit control trajectory (AGICT) of the tilt angle which indirectly "controls" the forward velocity such that it tracks the desired velocity asymptotically. The stability and optimal tracking performance have been rigorously established by theoretic analysis. In addition, simulation studies have been carried out to demonstrate the efficiency of the developed AGICT and optimized adaptive controller.
Mathematical Modelling, Simulation, and Optimal Control of the 2014 Ebola Outbreak in West Africa
Directory of Open Access Journals (Sweden)
Amira Rachah
2015-01-01
it is crucial to modelize the virus and simulate it. In this paper, we begin by studying a simple mathematical model that describes the 2014 Ebola outbreak in Liberia. Then, we use numerical simulations and available data provided by the World Health Organization to validate the obtained mathematical model. Moreover, we develop a new mathematical model including vaccination of individuals. We discuss different cases of vaccination in order to predict the effect of vaccination on the infected individuals over time. Finally, we apply optimal control to study the impact of vaccination on the spread of the Ebola virus. The optimal control problem is solved numerically by using a direct multiple shooting method.
Real-time economic optimization for a fermentation process using Model Predictive Control
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Jørgensen, John Bagterp
2014-01-01
Fermentation is a widely used process in production of many foods, beverages, and pharmaceuticals. The main goal of the control system is to maximize profit of the fermentation process, and thus this is also the main goal of this paper. We present a simple dynamic model for a fermentation process...... and demonstrate its usefulness in economic optimization. The model is formulated as an index-1 differential algebraic equation (DAE), which guarantees conservation of mass and energy in discrete form. The optimization is based on recent advances within Economic Nonlinear Model Predictive Control (E......-NMPC), and also utilizes the index-1 DAE model. The E-NMPC uses the single-shooting method and the adjoint method for computation of the optimization gradients. The process constraints are relaxed to soft-constraints on the outputs. Finally we derive the analytical solution to the economic optimization problem...
The optimal dynamic immunization under a controlled heterogeneous node-based SIRS model
Yang, Lu-Xing; Draief, Moez; Yang, Xiaofan
2016-05-01
Dynamic immunizations, under which the state of the propagation network of electronic viruses can be changed by adjusting the control measures, are regarded as an alternative to static immunizations. This paper addresses the optimal dynamical immunization under the widely accepted SIRS assumption. First, based on a controlled heterogeneous node-based SIRS model, an optimal control problem capturing the optimal dynamical immunization is formulated. Second, the existence of an optimal dynamical immunization scheme is shown, and the corresponding optimality system is derived. Next, some numerical examples are given to show that an optimal immunization strategy can be worked out by numerically solving the optimality system, from which it is found that the network topology has a complex impact on the optimal immunization strategy. Finally, the difference between a payoff and the minimum payoff is estimated in terms of the deviation of the corresponding immunization strategy from the optimal immunization strategy. The proposed optimal immunization scheme is justified, because it can achieve a low level of infections at a low cost.
Modelling for Fuel Optimal Control of a Variable Compression Engine
Nilsson, Ylva
2007-01-01
Variable compression engines are a mean to meet the demand on lower fuel consumption. A high compression ratio results in high engine efficiency, but also increases the knock tendency. On conventional engines with fixed compression ratio, knock is avoided by retarding the ignition angle. The variable compression engine offers an extra dimension in knock control, since both ignition angle and compression ratio can be adjusted. The central question is thus for what combination of compression ra...
Tuning rules for robust FOPID controllers based on multi-objective optimization with FOPDT models.
Sánchez, Helem Sabina; Padula, Fabrizio; Visioli, Antonio; Vilanova, Ramon
2017-01-01
In this paper a set of optimally balanced tuning rules for fractional-order proportional-integral-derivative controllers is proposed. The control problem of minimizing at once the integrated absolute error for both the set-point and the load disturbance responses is addressed. The control problem is stated as a multi-objective optimization problem where a first-order-plus-dead-time process model subject to a robustness, maximum sensitivity based, constraint has been considered. A set of Pareto optimal solutions is obtained for different normalized dead times and then the optimal balance between the competing objectives is obtained by choosing the Nash solution among the Pareto-optimal ones. A curve fitting procedure has then been applied in order to generate suitable tuning rules. Several simulation results show the effectiveness of the proposed approach. Copyright © 2016. Published by Elsevier Ltd.
Quantum optimal control theory and dynamic coupling in the spin-boson model
International Nuclear Information System (INIS)
Jirari, H.; Poetz, W.
2006-01-01
A Markovian master equation describing the evolution of open quantum systems in the presence of a time-dependent external field is derived within the Bloch-Redfield formalism. It leads to a system-bath interaction which depends on the control field. Optimal control theory is used to select control fields which allow accelerated or decelerated system relaxation, or suppression of relaxation (dissipation) altogether, depending on the dynamics we impose on the quantum system. The control-dissipation correlation and the nonperturbative treatment of the control field are essential for reaching this goal. The optimal control problem is formulated within Pontryagin's minimum principle and the resulting optimal differential system is solved numerically. As an application, we study the dynamics of a spin-boson model in the strong coupling regime under the influence of an external control field. We show how trapping the system in unstable quantum states and transfer of population can be achieved by optimized control of the dissipative quantum system. We also used optimal control theory to find the driving field that generates the quantum Z gate. In several cases studied, we find that the selected optimal field which reduces the purity loss significantly is a multicomponent low-frequency field including higher harmonics, all of which lie below the phonon cutoff frequency. Finally, in the undriven case we present an analytic result for the Lamb shift at zero temperature
Directory of Open Access Journals (Sweden)
Xiangyong Chen
2014-01-01
hybrid dynamic systems is established based on Lanchester equation in a (n,1 battle, where a heterogeneous force of n different troop types faces a homogeneous force. This model can be characterized by the interaction of continuous-time models (governed by Lanchester equation, and discrete event systems (described by variable tactics. Furthermore, an expository discussion is presented on an optimal variable tactics control problem for warfare hybrid dynamic system. The optimal control strategies are designed based on dynamic programming and differential game theory. As an example of the consequences of this optimal control problem, we take the (2, 1 case and solve the optimal strategies in a (2, 1 case. Simulation results show the feasibility of warfare hybrid system model and the effectiveness of the optimal control strategies designed.
Building Energy Modeling and Control Methods for Optimization and Renewables Integration
Burger, Eric M.
dynamics within a building by learning from sensor data. Control techniques encompass the application of optimal control theory, model predictive control, and convex distributed optimization to TCLs. First, we present the alternative control trajectory (ACT) representation, a novel method for the approximate optimization of non-convex discrete systems. This approach enables the optimal control of a population of non-convex agents using distributed convex optimization techniques. Second, we present a distributed convex optimization algorithm for the control of a TCL population. Experimental results demonstrate the application of this algorithm to the problem of renewable energy generation following. This dissertation contributes to the development of intelligent energy management systems for buildings by presenting a suite of novel and adaptable modeling and control techniques. Applications focus on optimizing the performance of building operations and on facilitating the integration of renewable energy resources.
Optimal control in a micro gas grid of prosumers using Model Predictive Control
Alkano, Desti; Nefkens, W.J.; Scherpen, Jacqueline M.A.; Volkerts, M.
This paper studies the optimal control of a micro grid of biogas prosumers equipped with local storage devices. Excess biogas can be upgraded and injected into the low- pressure gas grid or, alternatively, shipped per lorry to be used elsewhere in an effort to create revenue. The aim of the control
Directory of Open Access Journals (Sweden)
Patrick Piprek
2018-02-01
Full Text Available This paper presents an approach to model a ski jumper as a multi-body system for an optimal control application. The modeling is based on the constrained Newton-Euler-Equations. Within this paper the complete multi-body modeling methodology as well as the musculoskeletal modeling is considered. For the musculoskeletal modeling and its incorporation in the optimization model, we choose a nonlinear dynamic inversion control approach. This approach uses the muscle models as nonlinear reference models and links them to the ski jumper movement by a control law. This strategy yields a linearized input-output behavior, which makes the optimal control problem easier to solve. The resulting model of the ski jumper can then be used for trajectory optimization whose results are compared to literature jumps. Ultimately, this enables the jumper to get a very detailed feedback of the flight. To achieve the maximal jump length, exact positioning of his body with respect to the air can be displayed.
Model checking optimal finite-horizon control for probabilistic gene regulatory networks.
Wei, Ou; Guo, Zonghao; Niu, Yun; Liao, Wenyuan
2017-12-14
Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random perturbations to express the impact of external stimuli, is considered to be more suitable for modeling small biological systems intervened by conditions from the outside. In this paper, we apply probabilistic model checking, a formal verification technique, to optimal control for a CS-PBNp that minimizes the expected cost over a finite control horizon. We first describe a procedure of modeling a CS-PBNp using the language provided by a widely used probabilistic model checker PRISM. We then analyze the reward-based temporal properties and the computation in probabilistic model checking; based on the analysis, we provide a method to formulate the optimal control problem as minimum reachability reward properties. Furthermore, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. We conduct experiments on two examples, an apoptosis network and a WNT5A network. Preliminary experiment results show the feasibility and effectiveness of our approach. The approach based on probabilistic model checking for optimal control avoids explicit computation of large-size state transition relations associated with PBNs. It enables a natural depiction of the dynamics of gene regulatory networks, and provides a canonical form to formulate optimal control problems using temporal properties that can be automated solved by leveraging the analysis power of underlying model checking engines. This work will be helpful for further utilization of the advances in formal verification techniques in system biology.
Optimal blood glucose level control using dynamic programming based on minimal Bergman model
Rettian Anggita Sari, Maria; Hartono
2018-03-01
The purpose of this article is to simulate the glucose dynamic and the insulin kinetic of diabetic patient. The model used in this research is a non-linear Minimal Bergman model. Optimal control theory is then applied to formulate the problem in order to determine the optimal dose of insulin in the treatment of diabetes mellitus such that the glucose level is in the normal range for some specific time range. The optimization problem is solved using dynamic programming. The result shows that dynamic programming is quite reliable to represent the interaction between glucose and insulin levels in diabetes mellitus patient.
Integrated production planning and control: A multi-objective optimization model
Directory of Open Access Journals (Sweden)
Cheng Wang
2013-09-01
Full Text Available Purpose: Production planning and control has crucial impact on the production and business activities of enterprise. Enterprise Resource Planning (ERP is the most popular resources planning and management system, however there are some shortcomings and deficiencies in the production planning and control because its core component is still the Material Requirements Planning (MRP. For the defects of ERP system, many local improvement and optimization schemes have been proposed, and improve the feasibility and practicality of the plan in some extent, but study considering the whole planning system optimization in the multiple performance management objectives and achieving better application performance is less. The purpose of this paper is to propose a multi-objective production planning optimization model Based on the point of view of the integration of production planning and control, in order to achieve optimization and control of enterprise manufacturing management. Design/methodology/approach: On the analysis of ERP planning system’s defects and disadvantages, and related research and literature, a multi-objective production planning optimization model is proposed, in addition to net demand and capacity, multiple performance management objectives, such as on-time delivery, production balance, inventory, overtime production, are considered incorporating into the examination scope of the model, so that the manufacturing process could be management and controlled Optimally between multiple objectives. The validity and practicability of the model will be verified by the instance in the last part of the paper. Findings: The main finding is that production planning management of manufacturing enterprise considers not only the capacity and materials, but also a variety of performance management objectives in the production process, and building a multi-objective optimization model can effectively optimize the management and control of enterprise
Solving Optimal Control Problem of Monodomain Model Using Hybrid Conjugate Gradient Methods
Directory of Open Access Journals (Sweden)
Kin Wei Ng
2012-01-01
Full Text Available We present the numerical solutions for the PDE-constrained optimization problem arising in cardiac electrophysiology, that is, the optimal control problem of monodomain model. The optimal control problem of monodomain model is a nonlinear optimization problem that is constrained by the monodomain model. The monodomain model consists of a parabolic partial differential equation coupled to a system of nonlinear ordinary differential equations, which has been widely used for simulating cardiac electrical activity. Our control objective is to dampen the excitation wavefront using optimal applied extracellular current. Two hybrid conjugate gradient methods are employed for computing the optimal applied extracellular current, namely, the Hestenes-Stiefel-Dai-Yuan (HS-DY method and the Liu-Storey-Conjugate-Descent (LS-CD method. Our experiment results show that the excitation wavefronts are successfully dampened out when these methods are used. Our experiment results also show that the hybrid conjugate gradient methods are superior to the classical conjugate gradient methods when Armijo line search is used.
Energy Technology Data Exchange (ETDEWEB)
Velut, Stephane; Raaberg, Martin; Wendel, Hans (Grontmij AB (SE))
2007-12-15
Thermal power plants are complex processes in which many variables must be monitored and controlled in real-time for a safe and economic operation. The complex interactions between actuators and controlled variables as well as the load dependent dynamics make the design and tuning of all controllers a challenging task. A mathematical model of the process that describes critical characteristics such as dynamics, interactions, and nonlinearities might greatly facilitate the task of the control engineer. Such controllers can be designed in a rather systematic way to achieve good performance in terms of response time and robustness. This enables the operator to run the process closer to its limits while minimizing damage risks. The goal of the project was threefold. The first objective was to describe the available methods to compute process models directly from experimental data and illustrate how those models can be used for control design. The second objective was to apply some of the fore mentioned methods on a specific process, namely a feed water heater train to control the level in each preheater. The third objective was to analyze how the level in each preheater affects the thermal efficiency of the plant and derive adequate set-points for the model-based controllers. The project started at the end of the production season, which resulted in a tight schedule for the planning and the realization of experiments. Informative data could however be collected and models could be derived for some specific loads. Unfortunately the effect of the changes in the level set point could not be verified because of the limited length of the experiments. The project results can be summarized as follows: The way the condensate level should be chosen in every preheater has been formulated as a simple optimization problem that aims as maximizing the thermal efficiency of the plant. Even though the model used in the optimization was simple, the results were pretty intuitive. The
The cost of model reference adaptive control - Analysis, experiments, and optimization
Messer, R. S.; Haftka, R. T.; Cudney, H. H.
1993-01-01
In this paper the performance of Model Reference Adaptive Control (MRAC) is studied in numerical simulations and verified experimentally with the objective of understanding how differences between the plant and the reference model affect the control effort. MRAC is applied analytically and experimentally to a single degree of freedom system and analytically to a MIMO system with controlled differences between the model and the plant. It is shown that the control effort is sensitive to differences between the plant and the reference model. The effects of increased damping in the reference model are considered, and it is shown that requiring the controller to provide increased damping actually decreases the required control effort when differences between the plant and reference model exist. This result is useful because one of the first attempts to counteract the increased control effort due to differences between the plant and reference model might be to require less damping, however, this would actually increase the control effort. Optimization of weighting matrices is shown to help reduce the increase in required control effort. However, it was found that eventually the optimization resulted in a design that required an extremely high sampling rate for successful realization.
Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.
Kamesh, Reddi; Rani, K Yamuna
2016-09-01
A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Chen, Xiao; Wang, Qian; Srebric, Jelena
2016-01-01
Highlights: • This study evaluates an occupant-feedback driven Model Predictive Controller (MPC). • The MPC adjusts indoor temperature based on a dynamic thermal sensation (DTS) model. • A chamber model for predicting chamber air temperature is developed and validated. • Experiments show that MPC using DTS performs better than using Predicted Mean Vote. - Abstract: In current centralized building climate control, occupants do not have much opportunity to intervene the automated control system. This study explores the benefit of using thermal comfort feedback from occupants in the model predictive control (MPC) design based on a novel dynamic thermal sensation (DTS) model. This DTS model based MPC was evaluated in chamber experiments. A hierarchical structure for thermal control was adopted in the chamber experiments. At the high level, an MPC controller calculates the optimal supply air temperature of the chamber heating, ventilation, and air conditioning (HVAC) system, using the feedback of occupants’ votes on thermal sensation. At the low level, the actual supply air temperature is controlled by the chiller/heater using a PI control to achieve the optimal set point. This DTS-based MPC was also compared to an MPC designed based on the Predicted Mean Vote (PMV) model for thermal sensation. The experiment results demonstrated that the DTS-based MPC using occupant feedback allows significant energy saving while maintaining occupant thermal comfort compared to the PMV-based MPC.
Economic Optimization of Spray Dryer Operation using Nonlinear Model Predictive Control
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2014-01-01
In this paper we investigate an economically optimizing Nonlinear Model Predictive Control (E-NMPC) for a spray drying process. By simulation we evaluate the economic potential of this E-NMPC compared to a conventional PID based control strategy. Spray drying is the preferred process to reduce...... the water content for many liquid foodstuffs and produces a free flowing powder. The main challenge in controlling the spray drying process is to meet the residual moisture specifications and avoid that the powder sticks to the chamber walls of the spray dryer. We present a model for a spray dryer that has...... been validated on experimental data from a pilot plant. We use this model for simulation as well as for prediction in the E-NMPC. The E-NMPC is designed with hard input constraints and soft output constraints. The open-loop optimal control problem in the E-NMPC is solved using the single...
The Application of Time-Delay Dependent H∞ Control Model in Manufacturing Decision Optimization
Directory of Open Access Journals (Sweden)
Haifeng Guo
2015-01-01
Full Text Available This paper uses a time-delay dependent H∞ control model to analyze the effect of manufacturing decisions on the process of transmission from resources to capability. We establish a theoretical framework of manufacturing management process based on three terms: resource, manufacturing decision, and capability. Then we build a time-delay H∞ robust control model to analyze the robustness of manufacturing management. With the state feedback controller between manufacturing resources and decision, we find that there is an optimal decision to adjust the process of transmission from resources to capability under uncertain environment. Finally, we provide an example to prove the robustness of this model.
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2015-01-01
In this paper, we compare the performance of an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) to a linear tracking Model Predictive Controller (MPC) for a spray drying plant. We find in this simulation study, that the economic performance of the two controllers are almost...... equal. We evaluate the economic performance with an industrially recorded disturbance scenario, where unmeasured disturbances and model mismatch are present. The state of the spray dryer, used in the E-NMPC and MPC, is estimated using Kalman Filters with noise covariances estimated by a maximum...
Weerts, H.H.M.; Shafiei, S.E.; Stoustrup, J.; Izadi-Zamanabadi, R.; Boje, E.; Xia, X.
2014-01-01
A new formulation of model predictive control for supermarket refrigeration systems is proposed to facilitate the regulatory power services as well as energy cost optimization of such systems in the smart grid. Nonlinear dynamics existed in large-scale refrigeration plants challenges the predictive
Huang, Darong; Bai, Xing-Rong
Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.
Optimal input shaping for Fisher identifiability of control-oriented lithium-ion battery models
Rothenberger, Michael J.
This dissertation examines the fundamental challenge of optimally shaping input trajectories to maximize parameter identifiability of control-oriented lithium-ion battery models. Identifiability is a property from information theory that determines the solvability of parameter estimation for mathematical models using input-output measurements. This dissertation creates a framework that exploits the Fisher information metric to quantify the level of battery parameter identifiability, optimizes this metric through input shaping, and facilitates faster and more accurate estimation. The popularity of lithium-ion batteries is growing significantly in the energy storage domain, especially for stationary and transportation applications. While these cells have excellent power and energy densities, they are plagued with safety and lifespan concerns. These concerns are often resolved in the industry through conservative current and voltage operating limits, which reduce the overall performance and still lack robustness in detecting catastrophic failure modes. New advances in automotive battery management systems mitigate these challenges through the incorporation of model-based control to increase performance, safety, and lifespan. To achieve these goals, model-based control requires accurate parameterization of the battery model. While many groups in the literature study a variety of methods to perform battery parameter estimation, a fundamental issue of poor parameter identifiability remains apparent for lithium-ion battery models. This fundamental challenge of battery identifiability is studied extensively in the literature, and some groups are even approaching the problem of improving the ability to estimate the model parameters. The first approach is to add additional sensors to the battery to gain more information that is used for estimation. The other main approach is to shape the input trajectories to increase the amount of information that can be gained from input
Lan, C. Edward; Ge, Fuying
1989-01-01
Control system design for general nonlinear flight dynamic models is considered through numerical simulation. The design is accomplished through a numerical optimizer coupled with analysis of flight dynamic equations. The general flight dynamic equations are numerically integrated and dynamic characteristics are then identified from the dynamic response. The design variables are determined iteratively by the optimizer to optimize a prescribed objective function which is related to desired dynamic characteristics. Generality of the method allows nonlinear effects to aerodynamics and dynamic coupling to be considered in the design process. To demonstrate the method, nonlinear simulation models for an F-5A and an F-16 configurations are used to design dampers to satisfy specifications on flying qualities and control systems to prevent departure. The results indicate that the present method is simple in formulation and effective in satisfying the design objectives.
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Jørgensen, John Bagterp; Rawlings, James B.
2015-01-01
In this paper, we develop an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) for a complete spray drying plant with multiple stages. In the E-NMPC the initial state is estimated by an extended Kalman Filter (EKF) with noise covariances estimated by an autocovariance least...... squares method (ALS). We present a model for the spray drying plant and use this model for simulation as well as for prediction in the E-NMPC. The open-loop optimal control problem in the E-NMPC is solved using the single-shooting method combined with a quasi-Newton Sequential Quadratic programming (SQP......) algorithm and the adjoint method for computation of gradients. We evaluate the economic performance when unmeasured disturbances are present. By simulation, we demonstrate that the E-NMPC improves the profit of spray drying by 17% compared to conventional PI control....
Optimal control on hybrid ode systems with application to a tick disease model.
Ding, Wandi
2007-10-01
We are considering an optimal control problem for a type of hybrid system involving ordinary differential equations and a discrete time feature. One state variable has dynamics in only one season of the year and has a jump condition to obtain the initial condition for that corresponding season in the next year. The other state variable has continuous dynamics. Given a general objective functional, existence, necessary conditions and uniqueness for an optimal control are established. We apply our approach to a tick-transmitted disease model with age structure in which the tick dynamics changes seasonally while hosts have continuous dynamics. The goal is to maximize disease-free ticks and minimize infected ticks through an optimal control strategy of treatment with acaricide. Numerical examples are given to illustrate the results.
Wang, Y. M.; Xu, W. C.; Wu, S. Q.; Chai, C. W.; Liu, X.; Wang, S. H.
2018-03-01
The torsional oscillation is the dominant vibration form for the impression cylinder of printing machine (printing cylinder for short), directly restricting the printing speed up and reducing the quality of the prints. In order to reduce torsional vibration, the active control method for the printing cylinder is obtained. Taking the excitation force and moment from the cylinder gap and gripper teeth open & closing cam mechanism as variable parameters, authors establish the dynamic mathematical model of torsional vibration for the printing cylinder. The torsional active control method is based on Particle Swarm Optimization(PSO) algorithm to optimize input parameters for the serve motor. Furthermore, the input torque of the printing cylinder is optimized, and then compared with the numerical simulation results. The conclusions are that torsional vibration active control based on PSO is an availability method to the torsional vibration of printing cylinder.
An optimal control model for reducing and trading of carbon emissions
Guo, Huaying; Liang, Jin
2016-03-01
A stochastic optimal control model of reducing and trading for carbon emissions is established in this paper. With considerations of reducing the carbon emission growth and the price of the allowances in the market, an optimal policy is searched to have the minimum total costs to achieve the agreement of emission reduction targets. The model turns to a two-dimension HJB equation problem. By the methods of reducing dimension and Cole-Hopf transformation, a semi-closed form solution of the corresponding HJB problem under some assumptions is obtained. For more general cases, the numerical calculations, analysis and comparisons are presented.
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai
2016-01-01
This paper presents the Distributed Model Predictive Control (D-MPC) of a wind farm equipped with fast and short-term Energy Storage System (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D-MPC control of the wind farm...... is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines and the ESS unit, the wind turbine mechanical loads are alleviated. With the fast gradient method, the convergence rate of the DMPC is significantly improved which leads...
Optimal control of an invasive species using a reaction-diffusion model and linear programming
Bonneau, Mathieu; Johnson, Fred A.; Smith, Brian J.; Romagosa, Christina M.; Martin, Julien; Mazzotti, Frank J.
2017-01-01
Managing an invasive species is particularly challenging as little is generally known about the species’ biological characteristics in its new habitat. In practice, removal of individuals often starts before the species is studied to provide the information that will later improve control. Therefore, the locations and the amount of control have to be determined in the face of great uncertainty about the species characteristics and with a limited amount of resources. We propose framing spatial control as a linear programming optimization problem. This formulation, paired with a discrete reaction-diffusion model, permits calculation of an optimal control strategy that minimizes the remaining number of invaders for a fixed cost or that minimizes the control cost for containment or protecting specific areas from invasion. We propose computing the optimal strategy for a range of possible model parameters, representing current uncertainty on the possible invasion scenarios. Then, a best strategy can be identified depending on the risk attitude of the decision-maker. We use this framework to study the spatial control of the Argentine black and white tegus (Salvator merianae) in South Florida. There is uncertainty about tegu demography and we considered several combinations of model parameters, exhibiting various dynamics of invasion. For a fixed one-year budget, we show that the risk-averse strategy, which optimizes the worst-case scenario of tegus’ dynamics, and the risk-neutral strategy, which optimizes the expected scenario, both concentrated control close to the point of introduction. A risk-seeking strategy, which optimizes the best-case scenario, focuses more on models where eradication of the species in a cell is possible and consists of spreading control as much as possible. For the establishment of a containment area, assuming an exponential growth we show that with current control methods it might not be possible to implement such a strategy for some of the
Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions
Bole, Brian; Goebel, Kai; Vachtsevanos, George
2012-01-01
A generalized Markov chain representation of fault dynamics is presented for the case that available modeling of fault growth physics and future environmental stresses can be represented by two independent stochastic process models. A contrived but representatively challenging example will be presented and analyzed, in which uncertainty in the modeling of fault growth physics is represented by a uniformly distributed dice throwing process, and a discrete random walk is used to represent uncertain modeling of future exogenous loading demands to be placed on the system. A finite horizon dynamic programming algorithm is used to solve for an optimal control policy over a finite time window for the case that stochastic models representing physics of failure and future environmental stresses are known, and the states of both stochastic processes are observable by implemented control routines. The fundamental limitations of optimization performed in the presence of uncertain modeling information are examined by comparing the outcomes obtained from simulations of an optimizing control policy with the outcomes that would be achievable if all modeling uncertainties were removed from the system.
Model-predictive control and real-time optimization of a cat cracker unit
Directory of Open Access Journals (Sweden)
Stig Strand
1997-04-01
Full Text Available A project for control and optimization of the Residual Catalytic Cracking Process at the Mongstad refinery is near completion. Four model-predictive control applications have been successfully implemented, using the IDCOM control software from Setpoint Inc. The most attractive feature of the controller is the well-defined control prioritizing hierarchy, and the linear impulse-response models have proved to give satisfactory performance on this process. Excitation and identification of the dynamic models proved to be a difficult task, and careful design and monitoring of the tests was mandatory in order to produce good results. Multi-variable Pseudo Random Binary Test Sequences were used for the excitation. Technical performance and operator acceptance of the new control functions have been good, but it is realized that a continuing effort is needed to fine-tune and maintain such functions.
Optimal control applied to native-invasive species competition via a PDE model
Directory of Open Access Journals (Sweden)
Wandi Ding
2012-12-01
Full Text Available We consider an optimal control problem of a system of parabolic partial differential equations modelling the competition between an invasive and a native species. The motivating example is cottonwood-salt cedar competition, where the effect of disturbance in the system (such as flooding is taken to be a control variable. Flooding being detrimental at low and high levels, and advantageous at medium levels led us to consider the quadratic growth function of the control. The objective is to maximize the native species and minimize the invasive species while minimizing the cost of implementing the control. An existence result for an optimal control is given. Numerical examples are presented to illustrate the results.
Switching and optimizing control for coal flotation process based on a hybrid model
Dong, Zhiyong; Wang, Ranfeng; Fan, Minqiang; Fu, Xiang
2017-01-01
Flotation is an important part of coal preparation, and the flotation column is widely applied as efficient flotation equipment. This process is complex and affected by many factors, with the froth depth and reagent dosage being two of the most important and frequently manipulated variables. This paper proposes a new method of switching and optimizing control for the coal flotation process. A hybrid model is built and evaluated using industrial data. First, wavelet analysis and principal component analysis (PCA) are applied for signal pre-processing. Second, a control model for optimizing the set point of the froth depth is constructed based on fuzzy control, and a control model is designed to optimize the reagent dosages based on expert system. Finally, the least squares-support vector machine (LS-SVM) is used to identify the operating conditions of the flotation process and to select one of the two models (froth depth or reagent dosage) for subsequent operation according to the condition parameters. The hybrid model is developed and evaluated on an industrial coal flotation column and exhibits satisfactory performance. PMID:29040305
Optimization and Control of Agent-Based Models in Biology: A Perspective.
An, G; Fitzpatrick, B G; Christley, S; Federico, P; Kanarek, A; Neilan, R Miller; Oremland, M; Salinas, R; Laubenbacher, R; Lenhart, S
2017-01-01
Agent-based models (ABMs) have become an increasingly important mode of inquiry for the life sciences. They are particularly valuable for systems that are not understood well enough to build an equation-based model. These advantages, however, are counterbalanced by the difficulty of analyzing and using ABMs, due to the lack of the type of mathematical tools available for more traditional models, which leaves simulation as the primary approach. As models become large, simulation becomes challenging. This paper proposes a novel approach to two mathematical aspects of ABMs, optimization and control, and it presents a few first steps outlining how one might carry out this approach. Rather than viewing the ABM as a model, it is to be viewed as a surrogate for the actual system. For a given optimization or control problem (which may change over time), the surrogate system is modeled instead, using data from the ABM and a modeling framework for which ready-made mathematical tools exist, such as differential equations, or for which control strategies can explored more easily. Once the optimization problem is solved for the model of the surrogate, it is then lifted to the surrogate and tested. The final step is to lift the optimization solution from the surrogate system to the actual system. This program is illustrated with published work, using two relatively simple ABMs as a demonstration, Sugarscape and a consumer-resource ABM. Specific techniques discussed include dimension reduction and approximation of an ABM by difference equations as well systems of PDEs, related to certain specific control objectives. This demonstration illustrates the very challenging mathematical problems that need to be solved before this approach can be realistically applied to complex and large ABMs, current and future. The paper outlines a research program to address them.
Energy Technology Data Exchange (ETDEWEB)
Didriksen, H.; Sandvig Nielsen, J.; Weel Hansen, M.
2001-06-01
The aim of the project is to present a procedure to optimize existing drying processes. The optimization deals with energy consumption, capacity utilization and product quality. Other factors can also be included in the optimization, e.g. minimization of volume of discharged air. The optimization of existing drying processes will use calculation tool based on a mathematical simulation model for the process to calculate the most optimum operation situation on the basis of given conditions. In the project mathematical models have been developed precisely with this aim. The calculation tools have been developed with a user interface so that the tools can be used by technical staff in industrial companies and by consultants. The project also illustrates control of drying processes. Based on the developed models, the effect of using different types of control strategies by means of model simulations is illustrated. Three types of drying processes are treated: drum dryers, disc dryers and drying chambers. The work with the development of the simulation models has been very central in the project, as these have to be the basis for the optimization of the processes. The work is based on a large amount of information from academical literature and knowledge and experience about modelling thermal processes at dk-TEKNIK. The models constitute the core in the simulation programmes. The models describe the most important physical effects in connection with mass and energy transfer and transport under the drying for the three treated drying technologies. (EHS)
DEFF Research Database (Denmark)
Weerts, Hermanus H. M.; Shafiei, Seyed Ehsan; Stoustrup, Jakob
2014-01-01
A new formulation of model predictive control for supermarket refrigeration systems is proposed to facilitate the regulatory power services as well as energy cost optimization of such systems in the smart grid. Nonlinear dynamics existed in large-scale refrigeration plants challenges the predictive...... control design. It is however shown that taking into account the knowledge of different time scales in the dynamical subsystems makes possible a linear formulation of a centralized predictive controller. A realistic scenario of regulatory power services in the smart grid is considered and formulated...... in the same objective as of cost optimization one. A simulation benchmark validated against real data and including significant dynamics of the system are employed to show the effectiveness of the proposed control scheme....
Model-free adaptive control optimization using a chaotic particle swarm approach
International Nuclear Information System (INIS)
Santos Coelho, Leandro dos; Rodrigues Coelho, Antonio Augusto
2009-01-01
It is well known that conventional control theories are widely suited for applications where the processes can be reasonably described in advance. However, when the plant's dynamics are hard to characterize precisely or are subject to environmental uncertainties, one may encounter difficulties in applying the conventional controller design methodologies. Despite the difficulty in achieving high control performance, the fine tuning of controller parameters is a tedious task that always requires experts with knowledge in both control theory and process information. Nowadays, more and more studies have focused on the development of adaptive control algorithms that can be directly applied to complex processes whose dynamics are poorly modeled and/or have severe nonlinearities. In this context, the design of a Model-Free Learning Adaptive Control (MFLAC) based on pseudo-gradient concepts and optimization procedure by a Particle Swarm Optimization (PSO) approach using constriction coefficient and Henon chaotic sequences (CPSOH) is presented in this paper. PSO is a stochastic global optimization technique inspired by social behavior of bird flocking. The PSO models the exploration of a problem space by a population of particles. Each particle in PSO has a randomized velocity associated to it, which moves through the space of the problem. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed CPSOH introduces chaos mapping which introduces some flexibility in particle movements in each iteration. The chaotic sequences allow also explorations at early stages and exploitations at later stages during the search procedure of CPSOH. Motivation for application of CPSOH approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range when designed by trial-and-error by user. Numerical results of the MFLAC with CPSOH
Model-free adaptive control optimization using a chaotic particle swarm approach
Energy Technology Data Exchange (ETDEWEB)
Santos Coelho, Leandro dos [Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Pontifical Catholic University of Parana, PUCPR, Imaculada Conceicao, 1155, 80215-901 Curitiba, Parana (Brazil)], E-mail: leandro.coelho@pucpr.br; Rodrigues Coelho, Antonio Augusto [Department of Automation and Systems, Federal University of Santa Catarina, Box 476, 88040-900 Florianopolis, Santa Catarina (Brazil)], E-mail: aarc@das.ufsc.br
2009-08-30
It is well known that conventional control theories are widely suited for applications where the processes can be reasonably described in advance. However, when the plant's dynamics are hard to characterize precisely or are subject to environmental uncertainties, one may encounter difficulties in applying the conventional controller design methodologies. Despite the difficulty in achieving high control performance, the fine tuning of controller parameters is a tedious task that always requires experts with knowledge in both control theory and process information. Nowadays, more and more studies have focused on the development of adaptive control algorithms that can be directly applied to complex processes whose dynamics are poorly modeled and/or have severe nonlinearities. In this context, the design of a Model-Free Learning Adaptive Control (MFLAC) based on pseudo-gradient concepts and optimization procedure by a Particle Swarm Optimization (PSO) approach using constriction coefficient and Henon chaotic sequences (CPSOH) is presented in this paper. PSO is a stochastic global optimization technique inspired by social behavior of bird flocking. The PSO models the exploration of a problem space by a population of particles. Each particle in PSO has a randomized velocity associated to it, which moves through the space of the problem. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed CPSOH introduces chaos mapping which introduces some flexibility in particle movements in each iteration. The chaotic sequences allow also explorations at early stages and exploitations at later stages during the search procedure of CPSOH. Motivation for application of CPSOH approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range when designed by trial-and-error by user. Numerical results of the MFLAC with
2016-01-01
This book provides essential background knowledge on the development of model-based real-world solutions in the field of control and decision making for water systems. It presents system engineering methods for modelling surface water and groundwater resources as well as water transportation systems (rivers, channels and pipelines). The models in turn provide information on both the water quantity (flow rates, water levels) of surface water and groundwater and on water quality. In addition, methods for modelling and predicting water demand are described. Sample applications of the models are presented, such as a water allocation decision support system for semi-arid regions, a multiple-criteria control model for run-of-river hydropower plants, and a supply network simulation for public services.
Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint
Energy Technology Data Exchange (ETDEWEB)
Raszmann, Emma; Baker, Kyri; Shi, Ying; Christensen, Dane
2017-02-22
Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.
Modelling and Optimal Control of Typhoid Fever Disease with Cost-Effective Strategies.
Tilahun, Getachew Teshome; Makinde, Oluwole Daniel; Malonza, David
2017-01-01
We propose and analyze a compartmental nonlinear deterministic mathematical model for the typhoid fever outbreak and optimal control strategies in a community with varying population. The model is studied qualitatively using stability theory of differential equations and the basic reproductive number that represents the epidemic indicator is obtained from the largest eigenvalue of the next-generation matrix. Both local and global asymptotic stability conditions for disease-free and endemic equilibria are determined. The model exhibits a forward transcritical bifurcation and the sensitivity analysis is performed. The optimal control problem is designed by applying Pontryagin maximum principle with three control strategies, namely, the prevention strategy through sanitation, proper hygiene, and vaccination; the treatment strategy through application of appropriate medicine; and the screening of the carriers. The cost functional accounts for the cost involved in prevention, screening, and treatment together with the total number of the infected persons averted. Numerical results for the typhoid outbreak dynamics and its optimal control revealed that a combination of prevention and treatment is the best cost-effective strategy to eradicate the disease.
Scheller, Johannes; Braza, Marianna; Triantafyllou, Michael
2016-11-01
Bats and other animals rapidly change their wingspan in order to control the aerodynamic forces. A NACA0013 type airfoil with dynamically changing span is proposed as a simple model to experimentally study these biomimetic morphing wings. Combining this large-scale morphing with inline motion allows to control both force magnitude and direction. Force measurements are conducted in order to analyze the impact of the 4 degree of freedom flapping motion on the flow. A blade-element theory augmented unsteady aerodynamic model is then used to derive optimal flapping trajectories.
A model of reward- and effort-based optimal decision making and motor control.
Directory of Open Access Journals (Sweden)
Lionel Rigoux
Full Text Available Costs (e.g. energetic expenditure and benefits (e.g. food are central determinants of behavior. In ecology and economics, they are combined to form a utility function which is maximized to guide choices. This principle is widely used in neuroscience as a normative model of decision and action, but current versions of this model fail to consider how decisions are actually converted into actions (i.e. the formation of trajectories. Here, we describe an approach where decision making and motor control are optimal, iterative processes derived from the maximization of the discounted, weighted difference between expected rewards and foreseeable motor efforts. The model accounts for decision making in cost/benefit situations, and detailed characteristics of control and goal tracking in realistic motor tasks. As a normative construction, the model is relevant to address the neural bases and pathological aspects of decision making and motor control.
Model-Based Control of an Aircraft Engine using an Optimal Tuner Approach
Connolly, Joseph W.; Chicatelli, Amy; Garg, Sanjay
2012-01-01
This paper covers the development of a model-based engine control (MBEC) method- ology applied to an aircraft turbofan engine. Here, a linear model extracted from the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40k) at a cruise operating point serves as the engine and the on-board model. The on-board model is up- dated using an optimal tuner Kalman Filter (OTKF) estimation routine, which enables the on-board model to self-tune to account for engine performance variations. The focus here is on developing a methodology for MBEC with direct control of estimated parameters of interest such as thrust and stall margins. MBEC provides the ability for a tighter control bound of thrust over the entire life cycle of the engine that is not achievable using traditional control feedback, which uses engine pressure ratio or fan speed. CMAPSS40k is capable of modeling realistic engine performance, allowing for a verification of the MBEC tighter thrust control. In addition, investigations of using the MBEC to provide a surge limit for the controller limit logic are presented that could provide benefits over a simple acceleration schedule that is currently used in engine control architectures.
A path-following driver/vehicle model with optimized lateral dynamic controller
Directory of Open Access Journals (Sweden)
Behrooz Mashadi
Full Text Available Reduction in traffic congestion and overall number of accidents, especially within the last decade, can be attributed to the enormous progress in active safety. Vehicle path following control with the presence of driver commands can be regarded as one of the important issues in vehicle active safety systems development and more realistic explanation of vehicle path tracking problem. In this paper, an integrated driver/DYC control system is presented that regulates the steering angle and yaw moment, considering driver previewed path. Thus, the driver previewed distance, the heading error and the lateral deviation between the vehicle and desired path are used as inputs. Then, the controller determines and applies a corrective steering angle and a direct yaw moment to make the vehicle follow the desired path. A PID controller with optimized gains is used for the control of integrated driver/DYC system. Genetic Algorithm as an intelligent optimization method is utilized to adapt PID controller gains for various working situations. Proposed integrated driver/DYC controller is examined on lane change manuvers andthe sensitivity of the control system is investigated through the changes in the driver model and vehicle parameters. Simulation results show the pronounced effectiveness of the controller in vehicle path following and stability.
On a mean field game optimal control approach modeling fast exit scenarios in human crowds
Burger, Martin; Di Francesco, Marco; Markowich, Peter A.; Wolfram, Marie Therese
2013-01-01
The understanding of fast exit and evacuation situations in crowd motion research has received a lot of scientific interest in the last decades. Security issues in larger facilities, like shopping malls, sports centers, or festivals necessitate a better understanding of the major driving forces in crowd dynamics. In this paper we present an optimal control approach modeling fast exit scenarios in pedestrian crowds. The model is formulated in the framework of mean field games and based on a parabolic optimal control problem. We consider the case of a large human crowd trying to exit a room as fast as possible. The motion of every pedestrian is determined by minimizing a cost functional, which depends on his/her position and velocity, the overall density of people, and the time to exit. This microscopic setup leads in a mean-field formulation to a nonlinear macroscopic optimal control problem, which raises challenging questions for the analysis and numerical simulations.We discuss different aspects of the mathematical modeling and illustrate them with various computational results. ©2013 IEEE.
Directory of Open Access Journals (Sweden)
Wei Zhang
2012-01-01
Full Text Available This paper presents the model and algorithms for traffic flow data monitoring and optimal traffic light control based on wireless sensor networks. Given the scenario that sensor nodes are sparsely deployed along the segments between signalized intersections, an analytical model is built using continuum traffic equation and develops the method to estimate traffic parameter with the scattered sensor data. Based on the traffic data and principle of traffic congestion formation, we introduce the congestion factor which can be used to evaluate the real-time traffic congestion status along the segment and to predict the subcritical state of traffic jams. The result is expected to support the timing phase optimization of traffic light control for the purpose of avoiding traffic congestion before its formation. We simulate the traffic monitoring based on the Mobile Century dataset and analyze the performance of traffic light control on VISSIM platform when congestion factor is introduced into the signal timing optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring and evaluate traffic congestion status with high precision. It is helpful to remarkably alleviate urban traffic congestion and decrease the average traffic delays and maximum queue length.
On a mean field game optimal control approach modeling fast exit scenarios in human crowds
Burger, Martin
2013-12-01
The understanding of fast exit and evacuation situations in crowd motion research has received a lot of scientific interest in the last decades. Security issues in larger facilities, like shopping malls, sports centers, or festivals necessitate a better understanding of the major driving forces in crowd dynamics. In this paper we present an optimal control approach modeling fast exit scenarios in pedestrian crowds. The model is formulated in the framework of mean field games and based on a parabolic optimal control problem. We consider the case of a large human crowd trying to exit a room as fast as possible. The motion of every pedestrian is determined by minimizing a cost functional, which depends on his/her position and velocity, the overall density of people, and the time to exit. This microscopic setup leads in a mean-field formulation to a nonlinear macroscopic optimal control problem, which raises challenging questions for the analysis and numerical simulations.We discuss different aspects of the mathematical modeling and illustrate them with various computational results. ©2013 IEEE.
Optimal control of transverse mode coupling instability based on the two particle model
International Nuclear Information System (INIS)
Ogata, Atsushi
1985-01-01
The optimal regulator design technique is applied to asymptotically stabilize the transverse mode coupling instability of a storage ring. The state equations are based on the two particle model. These are a pair of equation sets, one for the first and one for the second half of the synchrotron phase. Each set consists of first-order difference equations in vector-matrix form, with time step equal to the revolution time of the ring. Solution of the discrete Riccati equation gives the optimal gain matrix of the transverse feedback. Computer simulations are carried out to verify its effectiveness. Some modifications necessary to apply it to the real accelerator operation are made. The old methods, the classical output feedback and the reactive feedback, are interpreted from the viewpoint of the optimal control. (orig.)
Simplified model-based optimal control of VAV air-conditioning system
Energy Technology Data Exchange (ETDEWEB)
Nassif, N.; Kajl, S.; Sabourin, R. [Ecole de Technologie Superieure, Montreal, PQ (Canada). Dept. of Construction Engineering
2005-07-01
The improvement of Variable Air Volume (VAV) system performance is one of several attempts being made to minimize the high energy use associated with the operation of heating, ventilation and air conditioning (HVAC) systems. A Simplified Optimization Process (SOP) comprised of controller set point strategies and a simplified VAV model was presented in this paper. The aim of the SOP was to determine supply set points. The advantage of the SOP over previous methods was that it did not require a detailed VAV model and optimization program. In addition, the monitored data for representative local-loop control can be checked on-line, after which controller set points can be updated in order to ensure proper operation by opting for real situations with minimum energy use. The SOP was validated using existing monitoring data and a model of an existing VAV system. Energy use simulations were compared to that of the existing VAV system. At each simulation step, 3 controller set point values were proposed and studied using the VAV model in order to select a value for each point which corresponded to the best performance of the VAV system. Simplified VAV component models were presented. Strategies for controller set points were described, including zone air temperature, duct static pressure set points; chilled water supply set points and supply air temperature set points. Simplified optimization process calculations were presented. Results indicated that the SOP provided significant energy savings when applied to specific AHU systems. In a comparison with a Detailed Optimization Process (DOP), the SOP was capable of determining set points close to those obtained by the DOP. However, it was noted that the controller set points determined by the SOP need a certain amount of time to reach optimal values when outdoor conditions or thermal loads are significantly changed. It was suggested that this disadvantage could be overcome by the use of a dynamic incremental value, which
Optimal control and stability analysis of an epidemic model with population dispersal
International Nuclear Information System (INIS)
Jana, Soovoojeet; Haldar, Palash; Kar, T.K.
2016-01-01
Highlights: • SEIR type epidemic model with population dispersal is considered here. • Vaccination is taken as a control parameter. • Optimal control problem is formulated and then solved. • Simulation shows that different diseases can be analyzed through this model. - Abstract: In the present paper we consider an SEIR type epidemic model with transport related infection between two cities. It is observed that transportation among regions has a strong impact on the dynamic evolution of a disease which can be eradicated in the absence of transportation. Transportation can lead to the incorporation of a positive risk probability. The epidemiological threshold, commonly known as the basic reproduction number, is derived and it is observed that when the basic reproduction number is less than unity the disease dies out, where as if it exceeds unity the disease may persist in the system. A thorough dynamical behavior of the constructed model is studied. We formulate and solve an optimal control problem using vaccination as a control tool. Extensive numerical simulations are carried out based on our analytical results. Finally we try to relate our work with a real world problem.
Application of model predictive control for optimal operation of wind turbines
Yuan, Yuan; Cao, Pei; Tang, J.
2017-04-01
For large-scale wind turbines, reducing maintenance cost is a major challenge. Model predictive control (MPC) is a promising approach to deal with multiple conflicting objectives using the weighed sum approach. In this research, model predictive control method is applied to wind turbine to find an optimal balance between multiple objectives, such as the energy capture, loads on turbine components, and the pitch actuator usage. The actuator constraints are integrated into the objective function at the control design stage. The analysis is carried out in both the partial load region and full load region, and the performances are compared with those of a baseline gain scheduling PID controller. The application of this strategy achieves enhanced balance of component loads, the average power and actuator usages in partial load region.
Dynamic Modeling of Steam Condenser and Design of PI Controller Based on Grey Wolf Optimizer
Directory of Open Access Journals (Sweden)
Shu-Xia Li
2015-01-01
Full Text Available Shell-and-tube condenser is a heat exchanger for cooling steam with high temperature and pressure, which is one of the main kinds of heat exchange equipment in thermal, nuclear, and marine power plant. Based on the lumped parameter modeling method, the dynamic mathematical model of the simplified steam condenser is established. Then, the pressure PI control system of steam condenser based on the Matlab/Simulink simulation platform is designed. In order to obtain better performance, a new metaheuristic intelligent algorithm, grey wolf optimizer (GWO, is used to realize the fine-tuning of PI controller parameters. On the other hand, the Z-N engineering tuning method, genetic algorithm, and particle swarm algorithm are adopted for tuning PI controller parameters and compared with GWO algorithm. Simulation results show that GWO algorithm has better control performance than other four algorithms.
A nonlinear optimal control approach to stabilization of a macroeconomic development model
Rigatos, G.; Siano, P.; Ghosh, T.; Sarno, D.
2017-11-01
A nonlinear optimal (H-infinity) control approach is proposed for the problem of stabilization of the dynamics of a macroeconomic development model that is known as the Grossman-Helpman model of endogenous product cycles. The dynamics of the macroeconomic development model is divided in two parts. The first one describes economic activities in a developed country and the second part describes variation of economic activities in a country under development which tries to modify its production so as to serve the needs of the developed country. The article shows that through control of the macroeconomic model of the developed country, one can finally control the dynamics of the economy in the country under development. The control method through which this is achieved is the nonlinear H-infinity control. The macroeconomic model for the country under development undergoes approximate linearization round a temporary operating point. This is defined at each time instant by the present value of the system's state vector and the last value of the control input vector that was exerted on it. The linearization is based on Taylor series expansion and the computation of the associated Jacobian matrices. For the linearized model an H-infinity feedback controller is computed. The controller's gain is calculated by solving an algebraic Riccati equation at each iteration of the control method. The asymptotic stability of the control approach is proven through Lyapunov analysis. This assures that the state variables of the macroeconomic model of the country under development will finally converge to the designated reference values.
International Nuclear Information System (INIS)
Kim, Jae Hwan; Park, Soon Ho; Na, Man Gyun
2014-01-01
Highlights: • A model predictive controller for load-following operation was developed. • Genetic algorithm optimizes the five nonlinear discrete control rod speeds. • The boron concentration is adjusted with automatic adjustment logic. • The proposed controller reflects the realistic control rod drive mechanism movement. • The performance was confirmed to be satisfactory by simulation from BOC to EOC. - Abstract: Currently, most existing nuclear power plants alter the reactor power by adjusting the boron concentration in the coolant because it has a smaller effect on the reactor power distribution. Frequent control rod movements for load-following operation induce xenon-oscillation. Therefore, a controller that can subdue this phenomenon effectively is needed. At an APR1400 nuclear power plant which is a pressurized water reactor (PWR), the reactor power is controlled automatically using a Reactor Regulating System (RRS) but the power distribution is controlled manually by operators. Therefore, for APR+ nuclear power plants which is an improved version of APR1400 nuclear reactor, a new concept of a reactor controller is needed to control both the reactor power and power distribution automatically. The model predictive control (MPC) method is applicable to multiple-input multiple-output control, and can be applied for complex and nonlinear systems, such as the nuclear power plants. In this study, an MPC controller was developed by applying a genetic algorithm to optimize the discrete control rod speeds and by reflecting the realistic movement of the control rod drive mechanism that moves at only five discrete speeds. The performance of the proposed controller was confirmed to be satisfactory by simulating the load-following operation of an APR+ nuclear power plant through interface with KISPAC-1D code
Power, control and optimization
Vasant, Pandian; Barsoum, Nader
2013-01-01
The book consists of chapters based on selected papers of international conference „Power, Control and Optimization 2012”, held in Las Vegas, USA. Readers can find interesting chapters discussing various topics from the field of power control, its distribution and related fields. Book discusses topics like energy consumption impacted by climate, mathematical modeling of the influence of thermal power plant on the aquatic environment, investigation of cost reduction in residential electricity bill using electric vehicle at peak times or allocation and size evaluation of distributed generation using ANN model and others. Chapter authors are to the best of our knowledge the originators or closely related to the originators of presented ideas and its applications. Hence, this book certainly is one of the few books discussing the benefit from intersection of those modern and fruitful scientific fields of research with very tight and deep impact on real life and industry. This book is devoted to the studies o...
Model Based Optimal Control, Estimation, and Validation of Lithium-Ion Batteries
Perez, Hector Eduardo
notion of interval observers to PDE models using a sensitivity-based approach. Practically, this chapter quantifies the sensitivity of battery state estimates to parameter variations, enabling robust battery management schemes. The effectiveness of the proposed sensitivity-based interval observers is verified via a numerical study for the range of uncertain parameters. Chapter 4: This chapter seeks to derive insight on battery charging control using electrochemistry models. Directly using full order complex multi-partial differential equation (PDE) electrochemical battery models is difficult and sometimes impossible to implement. This chapter develops an approach for obtaining optimal charge control schemes, while ensuring safety through constraint satisfaction. An optimal charge control problem is mathematically formulated via a coupled reduced order electrochemical-thermal model which conserves key electrochemical and thermal state information. The Legendre-Gauss-Radau (LGR) pseudo-spectral method with adaptive multi-mesh-interval collocation is employed to solve the resulting nonlinear multi-state optimal control problem. Minimum time charge protocols are analyzed in detail subject to solid and electrolyte phase concentration constraints, as well as temperature constraints. The optimization scheme is examined using different input current bounds, and an insight on battery design for fast charging is provided. Experimental results are provided to compare the tradeoffs between an electrochemical-thermal model based optimal charge protocol and a traditional charge protocol. Chapter 5: Fast and safe charging protocols are crucial for enhancing the practicality of batteries, especially for mobile applications such as smartphones and electric vehicles. This chapter proposes an innovative approach to devising optimally health-conscious fast-safe charge protocols. A multi-objective optimal control problem is mathematically formulated via a coupled electro
Mathematical modeling of zika virus disease with nonlinear incidence and optimal control
Goswami, Naba Kumar; Srivastav, Akhil Kumar; Ghosh, Mini; Shanmukha, B.
2018-04-01
The Zika virus was first discovered in a rhesus monkey in the Zika Forest of Uganda in 1947, and it was isolated from humans in Nigeria in 1952. Zika virus disease is primarily a mosquito-borne disease, which is transmitted to human primarily through the bite of an infected Aedes species mosquito. However, there is documented evidence of sexual transmission of this disease too. In this paper, a nonlinear mathematical model for Zika virus by considering nonlinear incidence is formulated and analyzed. The equilibria and the basic reproduction number (R0) of the model are found. The stability of the different equilibria of the model is discussed in detail. When the basic reproduction number R0 1, we have endemic equilibrium which is locally stable under some restriction on parameters. Further this model is extended to optimal control model and is analyzed by using Pontryagin’s Maximum Principle. It has been observed that optimal control plays a significant role in reducing the number of zika infectives. Finally, numerical simulation is performed to illustrate the analytical findings.
Zakary, Omar; Rachik, Mostafa; Elmouki, Ilias
2017-08-01
First, we devise in this paper, a multi-regions discrete-time model which describes the spatial-temporal spread of an epidemic which starts from one region and enters to regions which are connected with their neighbors by any kind of anthropological movement. We suppose homogeneous Susceptible-Infected-Removed (SIR) populations, and we consider in our simulations, a grid of colored cells, which represents the whole domain affected by the epidemic while each cell can represent a sub-domain or region. Second, in order to minimize the number of infected individuals in one region, we propose an optimal control approach based on a travel-blocking vicinity strategy which aims to control only one cell by restricting movements of infected people coming from all neighboring cells. Thus, we show the influence of the optimal control approach on the controlled cell. We should also note that the cellular modeling approach we propose here, can also describes infection dynamics of regions which are not necessarily attached one to an other, even if no empty space can be viewed between cells. The theoretical method we follow for the characterization of the travel-locking optimal controls, is based on a discrete version of Pontryagin's maximum principle while the numerical approach applied to the multi-points boundary value problems we obtain here, is based on discrete progressive-regressive iterative schemes. We illustrate our modeling and control approaches by giving an example of 100 regions.
Directory of Open Access Journals (Sweden)
Reza eSharif Razavian
2015-10-01
Full Text Available This paper presents a new model-based method to define muscle synergies. Unlike the conventional factorization approach, which extracts synergies from electromyographic data, the proposed method employs a biomechanical model and formally defines the synergies as the solution of an optimal control problem. As a result, the number of required synergies is directly related to the dimensions of the operational space. The estimated synergies are posture-dependent, which correlate well with the results of standard factorization methods. Two examples are used to showcase this method: a two-dimensional forearm model, and a three-dimensional driver arm model. It has been shown here that the synergies need to be task-specific (i.e. they are defined for the specific operational spaces: the elbow angle and the steering wheel angle in the two systems. This functional definition of synergies results in a low-dimensional control space, in which every force in the operational space is accurately created by a unique combination of synergies. As such, there is no need for extra criteria (e.g., minimizing effort in the process of motion control. This approach is motivated by the need for fast and bio-plausible feedback control of musculoskeletal systems, and can have important implications in engineering, motor control, and biomechanics.
Sharif Razavian, Reza; Mehrabi, Naser; McPhee, John
2015-01-01
This paper presents a new model-based method to define muscle synergies. Unlike the conventional factorization approach, which extracts synergies from electromyographic data, the proposed method employs a biomechanical model and formally defines the synergies as the solution of an optimal control problem. As a result, the number of required synergies is directly related to the dimensions of the operational space. The estimated synergies are posture-dependent, which correlate well with the results of standard factorization methods. Two examples are used to showcase this method: a two-dimensional forearm model, and a three-dimensional driver arm model. It has been shown here that the synergies need to be task-specific (i.e., they are defined for the specific operational spaces: the elbow angle and the steering wheel angle in the two systems). This functional definition of synergies results in a low-dimensional control space, in which every force in the operational space is accurately created by a unique combination of synergies. As such, there is no need for extra criteria (e.g., minimizing effort) in the process of motion control. This approach is motivated by the need for fast and bio-plausible feedback control of musculoskeletal systems, and can have important implications in engineering, motor control, and biomechanics.
Voice Communications over 802.11 Ad Hoc Networks: Modeling, Optimization and Call Admission Control
Xu, Changchun; Xu, Yanyi; Liu, Gan; Liu, Kezhong
Supporting quality-of-service (QoS) of multimedia communications over IEEE 802.11 based ad hoc networks is a challenging task. This paper develops a simple 3-D Markov chain model for queuing analysis of IEEE 802.11 MAC layer. The model is applied for performance analysis of voice communications over IEEE 802.11 single-hop ad hoc networks. By using the model, we finish the performance optimization of IEEE MAC layer and obtain the maximum number of voice calls in IEEE 802.11 ad hoc networks as well as the statistical performance bounds. Furthermore, we design a fully distributed call admission control (CAC) algorithm which can provide strict statistical QoS guarantee for voice communications over IEEE 802.11 ad hoc networks. Extensive simulations indicate the accuracy of the analytical model and the CAC scheme.
Optimal decoupling controllers revisited
Czech Academy of Sciences Publication Activity Database
Kučera, Vladimír
2013-01-01
Roč. 42, č. 1 (2013), s. 1-16 ISSN 0324-8569 R&D Projects: GA TA ČR(CZ) TE01020197 Institutional support: RVO:67985556 Keywords : linear systems * fractional representations * decoupling control lers * stabilizing control lers * optimal control lers Subject RIV: BC - Control Systems Theory
Nonlinear optimal control theory
Berkovitz, Leonard David
2012-01-01
Nonlinear Optimal Control Theory presents a deep, wide-ranging introduction to the mathematical theory of the optimal control of processes governed by ordinary differential equations and certain types of differential equations with memory. Many examples illustrate the mathematical issues that need to be addressed when using optimal control techniques in diverse areas. Drawing on classroom-tested material from Purdue University and North Carolina State University, the book gives a unified account of bounded state problems governed by ordinary, integrodifferential, and delay systems. It also dis
Modelling Supported Driving as an Optimal Control Cycle : Framework and Model Characteristics
Wang, M.; Treiber, M.; Daamen, W.; Hoogendoorn, S.P.; Van Arem, B.
2013-01-01
Driver assistance systems support drivers in operating vehicles in a safe, comfortable and efficient way, and thus may induce changes in traffic flow characteristics. This paper puts forward a receding horizon control framework to model driver assistance and cooperative systems. The accelerations of
Directory of Open Access Journals (Sweden)
Lili Wang
2015-01-01
Full Text Available With the rapid development of urban rail transit, the phenomenon of outburst passenger flows flocking to stations is occurring much more frequently. Passenger flow control is one of the main methods used to ensure passengers’ safety. While most previous studies have only focused on control measures inside the target station, ignoring the collaboration between stops, this paper puts emphasis on joint passenger control methods during the occurrence of large passenger flows. To provide a theoretic description for the problem under consideration, an integer programming model is built, based on the analysis of passenger delay and the processes by which passengers alight and board. Taking average passenger delay as the objective, the proposed model aims to disperse the pressure of oversaturated stations into others, achieving the optimal state for the entire line. The model is verified using a case study and the results show that restricted access measures taken collaboratively by stations produce less delay and faster evacuation. Finally, a sensitivity analysis is conducted, from which we find that the departure interval and maximum conveying capacity of the train affect passenger delay markedly in the process of passenger control and infer that control measures should be taken at stations near to the one experiencing an emergency.
Optimization modeling with spreadsheets
Baker, Kenneth R
2015-01-01
An accessible introduction to optimization analysis using spreadsheets Updated and revised, Optimization Modeling with Spreadsheets, Third Edition emphasizes model building skills in optimization analysis. By emphasizing both spreadsheet modeling and optimization tools in the freely available Microsoft® Office Excel® Solver, the book illustrates how to find solutions to real-world optimization problems without needing additional specialized software. The Third Edition includes many practical applications of optimization models as well as a systematic framework that il
Shimansky, Yury P; Kang, Tao; He, Jiping
2004-02-01
A computational model of a learning system (LS) is described that acquires knowledge and skill necessary for optimal control of a multisegmental limb dynamics (controlled object or CO), starting from "knowing" only the dimensionality of the object's state space. It is based on an optimal control problem setup different from that of reinforcement learning. The LS solves the optimal control problem online while practicing the manipulation of CO. The system's functional architecture comprises several adaptive components, each of which incorporates a number of mapping functions approximated based on artificial neural nets. Besides the internal model of the CO's dynamics and adaptive controller that computes the control law, the LS includes a new type of internal model, the minimal cost (IM(mc)) of moving the controlled object between a pair of states. That internal model appears critical for the LS's capacity to develop an optimal movement trajectory. The IM(mc) interacts with the adaptive controller in a cooperative manner. The controller provides an initial approximation of an optimal control action, which is further optimized in real time based on the IM(mc). The IM(mc) in turn provides information for updating the controller. The LS's performance was tested on the task of center-out reaching to eight randomly selected targets with a 2DOF limb model. The LS reached an optimal level of performance in a few tens of trials. It also quickly adapted to movement perturbations produced by two different types of external force field. The results suggest that the proposed design of a self-optimized control system can serve as a basis for the modeling of motor learning that includes the formation and adaptive modification of the plan of a goal-directed movement.
Verguet, Stéphane; Johri, Mira; Morris, Shaun K; Gauvreau, Cindy L; Jha, Prabhat; Jit, Mark
2015-03-03
The Measles & Rubella Initiative, a broad consortium of global health agencies, has provided support to measles-burdened countries, focusing on sustaining high coverage of routine immunization of children and supplementing it with a second dose opportunity for measles vaccine through supplemental immunization activities (SIAs). We estimate optimal scheduling of SIAs in countries with the highest measles burden. We develop an age-stratified dynamic compartmental model of measles transmission. We explore the frequency of SIAs in order to achieve measles control in selected countries and two Indian states with high measles burden. Specifically, we compute the maximum allowable time period between two consecutive SIAs to achieve measles control. Our analysis indicates that a single SIA will not control measles transmission in any of the countries with high measles burden. However, regular SIAs at high coverage levels are a viable strategy to prevent measles outbreaks. The periodicity of SIAs differs between countries and even within a single country, and is determined by population demographics and existing routine immunization coverage. Our analysis can guide country policymakers deciding on the optimal scheduling of SIA campaigns and the best combination of routine and SIA vaccination to control measles. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.
Numerical research of the optimal control problem in the semi-Markov inventory model
Energy Technology Data Exchange (ETDEWEB)
Gorshenin, Andrey K. [Institute of Informatics Problems, Russian Academy of Sciences, Vavilova str., 44/2, Moscow, Russia MIREA, Faculty of Information Technology (Russian Federation); Belousov, Vasily V. [Institute of Informatics Problems, Russian Academy of Sciences, Vavilova str., 44/2, Moscow (Russian Federation); Shnourkoff, Peter V.; Ivanov, Alexey V. [National research university Higher school of economics, Moscow (Russian Federation)
2015-03-10
This paper is devoted to the numerical simulation of stochastic system for inventory management products using controlled semi-Markov process. The results of a special software for the system’s research and finding the optimal control are presented.
Numerical research of the optimal control problem in the semi-Markov inventory model
International Nuclear Information System (INIS)
Gorshenin, Andrey K.; Belousov, Vasily V.; Shnourkoff, Peter V.; Ivanov, Alexey V.
2015-01-01
This paper is devoted to the numerical simulation of stochastic system for inventory management products using controlled semi-Markov process. The results of a special software for the system’s research and finding the optimal control are presented
Directory of Open Access Journals (Sweden)
Guo Jiuwang
2015-01-01
Full Text Available Because of the randomness and fluctuation of wind energy, as well as the impact of strongly nonlinear characteristic of variable speed constant frequency (VSCF wind power generation system with doubly fed induction generators (DFIG, traditional active power control strategies are difficult to achieve high precision control and the output power of wind turbines is more fluctuated. In order to improve the quality of output electric energy of doubly fed wind turbines, on the basis of analyzing the operating principles and dynamic characteristics of doubly fed wind turbines, this paper proposes a new active power optimal control method of doubly fed wind turbines based on predictive control theory. This method uses state space model of wind turbines, based on the prediction of the future state of wind turbines, moves horizon optimization, and meanwhile, gets the control signals of pitch angle and generator torque. Simulation results show that the proposed control strategies can guarantee the utilization efficiency for wind energy. Simultaneously, they can improve operation stability of wind turbines and the quality of electric energy.
Sharifi, N; Ozgoli, S; Ramezani, A
2017-06-01
Mixed immunotherapy and chemotherapy of tumours is one of the most efficient ways to improve cancer treatment strategies. However, it is important to 'design' an effective treatment programme which can optimize the ways of combining immunotherapy and chemotherapy to diminish their imminent side effects. Control engineering techniques could be used for this. The method of multiple model predictive controller (MMPC) is applied to the modified Stepanova model to induce the best combination of drugs scheduling under a better health criteria profile. The proposed MMPC is a feedback scheme that can perform global optimization for both tumour volume and immune competent cell density by performing multiple constraints. Although current studies usually assume that immunotherapy has no side effect, this paper presents a new method of mixed drug administration by employing MMPC, which implements several constraints for chemotherapy and immunotherapy by considering both drug toxicity and autoimmune. With designed controller we need maximum 57% and 28% of full dosage of drugs for chemotherapy and immunotherapy in some instances, respectively. Therefore, through the proposed controller less dosage of drugs are needed, which contribute to suitable results with a perceptible reduction in medicine side effects. It is observed that in the presence of MMPC, the amount of required drugs is minimized, while the tumour volume is reduced. The efficiency of the presented method has been illustrated through simulations, as the system from an initial condition in the malignant region of the state space (macroscopic tumour volume) transfers into the benign region (microscopic tumour volume) in which the immune system can control tumour growth. Copyright © 2017 Elsevier B.V. All rights reserved.
Simplified Building Thermal Model Used for Optimal Control of Radiant Cooling System
Directory of Open Access Journals (Sweden)
Lei He
2016-01-01
Full Text Available MPC has the ability to optimize the system operation parameters for energy conservation. Recently, it has been used in HVAC systems for saving energy, but there are very few applications in radiant cooling systems. To implement MPC in buildings with radiant terminals, the predictions of cooling load and thermal environment are indispensable. In this paper, a simplified thermal model is proposed for predicting cooling load and thermal environment in buildings with radiant floor. In this thermal model, the black-box model is introduced to derive the incident solar radiation, while the genetic algorithm is utilized to identify the parameters of the thermal model. In order to further validate this simplified thermal model, simulated results from TRNSYS are compared with those from this model and the deviation is evaluated based on coefficient of variation of root mean square (CV. The results show that the simplified model can predict the operative temperature with a CV lower than 1% and predict cooling loads with a CV lower than 10%. For the purpose of supervisory control in HVAC systems, this simplified RC thermal model has an acceptable accuracy and can be used for further MPC in buildings with radiation terminals.
Ramezanpour, H R; Setayeshi, S; Akbari, M E
2011-01-01
Determining the optimal and effective scheme for administrating the chemotherapy agents in breast cancer is the main goal of this scientific research. The most important issue here is the amount of drug or radiation administrated in chemotherapy and radiotherapy for increasing patient's survival. This is because in these cases, the therapy not only kills the tumor cells, but also kills some of the healthy tissues and causes serious damages. In this paper we investigate optimal drug scheduling effect for breast cancer model which consist of nonlinear ordinary differential time-delay equations. In this paper, a mathematical model of breast cancer tumors is discussed and then optimal control theory is applied to find out the optimal drug adjustment as an input control of system. Finally we use Sensitivity Approach (SA) to solve the optimal control problem. The goal of this paper is to determine optimal and effective scheme for administering the chemotherapy agent, so that the tumor is eradicated, while the immune systems remains above a suitable level. Simulation results confirm the effectiveness of our proposed procedure. In this paper a new scheme is proposed to design a therapy protocol for chemotherapy in Breast Cancer. In contrast to traditional pulse drug delivery, a continuous process is offered and optimized, according to the optimal control theory for time-delay systems.
Optimal distributed control of a diffuse interface model of tumor growth
Colli, Pierluigi; Gilardi, Gianni; Rocca, Elisabetta; Sprekels, Jürgen
2017-06-01
In this paper, a distributed optimal control problem is studied for a diffuse interface model of tumor growth which was proposed by Hawkins-Daruud et al in Hawkins-Daruud et al (2011 Int. J. Numer. Math. Biomed. Eng. 28 3-24). The model consists of a Cahn-Hilliard equation for the tumor cell fraction φ coupled to a reaction-diffusion equation for a function σ representing the nutrient-rich extracellular water volume fraction. The distributed control u monitors as a right-hand side of the equation for σ and can be interpreted as a nutrient supply or a medication, while the cost function, which is of standard tracking type, is meant to keep the tumor cell fraction under control during the evolution. We show that the control-to-state operator is Fréchet differentiable between appropriate Banach spaces and derive the first-order necessary optimality conditions in terms of a variational inequality involving the adjoint state variables. The financial support of the FP7-IDEAS-ERC-StG #256872 (EntroPhase) and of the project Fondazione Cariplo-Regione Lombardia MEGAsTAR ‘Matematica d’Eccellenza in biologia ed ingegneria come accelleratore di una nuona strateGia per l’ATtRattività dell’ateneo pavese’ is gratefully acknowledged. The paper also benefited from the support of the MIUR-PRIN Grant 2015PA5MP7 ‘Calculus of Variations’ for PC and GG, and the GNAMPA (Gruppo Nazionale per l’Analisi Matematica, la Probabilità e le loro Applicazioni) of INdAM (Istituto Nazionale di Alta Matematica) for PC, GG and ER.
Parameter identification of a BWR nuclear power plant model for use in optimal control
International Nuclear Information System (INIS)
Volf, K.
1976-02-01
The problem being considered is the modeling of a nuclear power plant for the development of an optimal control system of the plant. Current system identification concepts, combining input/output information with a-priori structural information are employed. Two of the known parameter identification methods i.e., a least squares method and a maximum likelihood technique, are studied as ways of parameter identification from measurement data. A low order state variable stochastic model of a BWR nuclear power plant is presented as an application of this approach. The model consists of a deterministic and a noise part. The deterministic part is formed by simplified modeling of the major plant dynamic phenomena. The moise part models the effects of input random disturbances to the deterministic part and additive measurement noise. Most of the model parameters are assumed to be initially unknown. They are identified using measurement data records. A detailed high order digital computer simulation is used to simulate plant dynamic behaviour since it is not conceivable for experimentation of this kind to be performed on the real nuclear power plant. The identification task consists in adapting the performance of the simple model to the data acquired from this plant simulation ensuring the applicability of the techniques to measurement data acquired directly from the plant. (orig.) [de
Dynamics and optimal control of a non-linear epidemic model with relapse and cure
Lahrouz, A.; El Mahjour, H.; Settati, A.; Bernoussi, A.
2018-04-01
In this work, we introduce the basic reproduction number R0 for a general epidemic model with graded cure, relapse and nonlinear incidence rate in a non-constant population size. We established that the disease free-equilibrium state Ef is globally asymptotically exponentially stable if R0 1, we proved that the system model has at least one endemic state Ee. Then, by means of an appropriate Lyapunov function, we showed that Ee is unique and globally asymptotically stable under some acceptable biological conditions. On the other hand, we use two types of control to reduce the number of infectious individuals. The optimality system is formulated and solved numerically using a Gauss-Seidel-like implicit finite-difference method.
Directory of Open Access Journals (Sweden)
Xun Li
2017-01-01
Full Text Available We proposed a signal control optimization model for urban main trunk line intersections. Four-phase intersection was analyzed and modeled based on the Cell Transmission Model (CTM. CTM and signal control model in our study had both been improved for multi-intersections by three-phase theory and information-exchanging. To achieve a real-time application, an improved genetic algorithm (GA was proposed finally, the DISCO traffic simulation software was used for numerical simulation experiment, and comparisons with the standard GA and CTM were reported in this paper. Experimental results indicate that our searching time is less than that of SGA by 38%, and our method needs only 1/3 iteration time of SGA. According to our DISCO traffic simulation processing, compared with SGA, if the input traffic flow is changed from free phase to synchronized phase, for example, less than 900 vel/h, the delay time can reduce to 87.99% by our method, and the minimum delay time is 77.76% of existing method. Furthermore, if input traffic volume is increased to 1200 vel/h or more at the synchronized phase, the summary and minimum values of average delay time are reduced to 81.16% and 75.83%, respectively, and the average delay time is reduced to 17.72 seconds.
Anisimov, D. N.; Dang, Thai Son; Banerjee, Santo; Mai, The Anh
2017-07-01
In this paper, an intelligent system use fuzzy-PD controller based on relation models is developed for a two-wheeled self-balancing robot. Scaling factors of the fuzzy-PD controller are optimized by a Cross-Entropy optimization method. A linear Quadratic Regulator is designed to bring a comparison with the fuzzy-PD controller by control quality parameters. The controllers are ported and run on STM32F4 Discovery Kit based on the real-time operating system. The experimental results indicate that the proposed fuzzy-PD controller runs exactly on embedded system and has desired performance in term of fast response, good balance and stabilize.
International Nuclear Information System (INIS)
Li Xi; Deng Zhonghua; Wei Dong; Xu Chunshan; Cao Guangyi
2011-01-01
Highlights: →We build up the thermal expressions of PEMFC stack. → The expressions are converted into the affine state space control-oriented model for the VSC strategy. → The NGA is developed to optimize the parameter of thermal-model-oriented control law. → Numerical results demonstrate the effectiveness and rationality of the method proposed. - Abstract: It is critical to understand and manage the thermal effects in optimizing the performance and durability of proton exchange membrane fuel cell (PEMFC) stack. And building up the control-oriented thermal model of PEMFC stack is necessary. The thermal model, a set of differential equations, is established according to the conservation equations of mass and energy, which can be used to reflect truly the actual temperature response of PEMFC stack, however, the expressions of the model are too complicated to be used in the design of control. For this reason, the expressions are converted into the affine state space control-oriented model in detail for the variable structure control (VSC) strategy. Meanwhile, the accurate model must be established for the VSC and the parameters of VSC laws should be optimized. Consequently, a novel genetic algorithm (NGA) is developed to optimize the parameter of thermal-model-oriented control law for PEMFC stack. Finally, numerical test results demonstrate the effectiveness and rationality of the method proposed in this paper. It lays the foundation for the realization of online thermal management of PEMFC stack based on VSC.
Energy Optimal Tracking Control with Discrete Fluid Power Systems using Model Predictive Control
DEFF Research Database (Denmark)
Hansen, Anders Hedegaard; Asmussen, Magnus Færing; Bech, Michael Møller
2017-01-01
For Discrete Displacement Cylinder (DDC) drives the control task lies in choosing force level. Hence, which force level to apply and thereby which pressure level each cylinder chambers shall be connected to. The DDC system is inherently a force system why often a force reference is generated...... and compared to a PID like tracking controller combined with a FSA. The results indicate that the energy efficiency of position tracking DDC systems may be improved significantly by using the MPC algorithm....
Dynamic Modeling and Control Strategy Optimization for a Hybrid Electric Tracked Vehicle
Directory of Open Access Journals (Sweden)
Hong Wang
2015-01-01
Full Text Available A new hybrid electric tracked bulldozer composed of an engine generator, two driving motors, and an ultracapacitor is put forward, which can provide high efficiencies and less fuel consumption comparing with traditional ones. This paper first presents the terramechanics of this hybrid electric tracked bulldozer. The driving dynamics for this tracked bulldozer is then analyzed. After that, based on analyzing the working characteristics of the engine, generator, and driving motors, the power train system model and control strategy optimization is established by using MATLAB/Simulink and OPTIMUS software. Simulation is performed under a representative working condition, and the results demonstrate that fuel economy of the HETV can be significantly improved.
Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid
Directory of Open Access Journals (Sweden)
Xiaogang Guo
2018-01-01
Full Text Available In this paper, an adaptively constrained stochastic model predictive control (MPC is proposed to achieve less-conservative coordination between energy storage units and uncertain renewable energy sources (RESs in a microgrid (MG. Besides the economic objective of MG operation, the limits of state-of-charge (SOC and discharging/charging power of the energy storage unit are formulated as chance constraints when accommodating uncertainties of RESs, considering mild violations of these constraints are allowed during long-term operation, and a closed-loop online update strategy is performed to adaptively tighten or relax constraints according to the actual deviation probability of violation level from the desired one as well as the current change rate of deviation probability. Numerical studies show that the proposed adaptively constrained stochastic MPC for MG optimal operation is much less conservative compared with the scenario optimization based robust MPC, and also presents a better convergence performance to the desired constraint violation level than other online update strategies.
Lin, XuXun; Yuan, PengCheng
2018-01-01
In this research we consider commuters' dynamic learning effect by modeling the trip mode choice behavior from a new perspective of dynamic evolutionary game theory. We explore the behavior pattern of different types of commuters and study the evolution path and equilibrium properties under different traffic conditions. We further establish a dynamic parking charge optimal control (referred to as DPCOC) model to alter commuters' trip mode choice while minimizing the total social cost. Numerical tests show. (1) Under fixed parking fee policy, the evolutionary results are completely decided by the travel time and the only method for public transit induction is to increase the parking charge price. (2) Compared with fixed parking fee policy, DPCOC policy proposed in this research has several advantages. Firstly, it can effectively turn the evolutionary path and evolutionary stable strategy to a better situation while minimizing the total social cost. Secondly, it can reduce the sensitivity of trip mode choice behavior to traffic congestion and improve the ability to resist interferences and emergencies. Thirdly, it is able to control the private car proportion to a stable state and make the trip behavior more predictable for the transportation management department. The research results can provide theoretical basis and decision-making references for commuters' mode choice prediction, dynamic setting of urban parking charge prices and public transit induction.
A population-feedback control based algorithm for well trajectory optimization using proxy model
Directory of Open Access Journals (Sweden)
Javad Kasravi
2017-04-01
Full Text Available Wellbore instability is one of the concerns in the field of drilling engineering. This phenomenon is affected by several factors such as azimuth, inclination angle, in-situ stress, mud weight, and rock strength parameters. Among these factors, azimuth, inclination angle, and mud weight are controllable. The objective of this paper is to introduce a new procedure based on elastoplastic theory in wellbore stability solution to determine the optimum well trajectory and global minimum mud pressure required (GMMPR. Genetic algorithm (GA was applied as a main optimization engine that employs proportional feedback controller to obtain the minimum mud pressure required (MMPR. The feedback function repeatedly calculated and updated the error between the simulated and set point of normalized yielded zone area (NYZA. To reduce computation expenses, an artificial neural network (ANN was used as a proxy (surrogate model to approximate the behavior of the actual wellbore model. The methodology was applied to a directional well in southwestern Iranian oilfield. The results demonstrated that the error between the predicted GMMPR and practical safe mud pressure was 4% for elastoplastic method, and 22% for conventional elastic solution.
International Nuclear Information System (INIS)
Romero, Alberto; Millar, Dean; Carvalho, Monica; Maestre, José M.; Camacho, Eduardo F.
2015-01-01
Mine dewatering can represent up to 5% of the total energy demand of a mine, and is one of the mine systems that aim to guarantee safe operating conditions. As mines go deeper, dewatering pumping heads become bigger, potentially involving several lift stages. Greater depth does not only mean greater dewatering cost, but more complex systems that require more sophisticated control systems, especially if mine operators wish to gain benefits from demand response incentives that are becoming a routine part of electricity tariffs. This work explores a two stage economic optimization procedure of an underground mine dewatering system, comprising two lifting stages, each one including a pump station and a water reservoir. First, the system design is optimized considering hourly characteristic dewatering demands for twelve days, one day representing each month of the year to account for seasonal dewatering demand variations. This design optimization minimizes the annualized cost of the system, and therefore includes the investment costs in underground reservoirs. Reservoir size, as well as an hourly pumping operation plan are calculated for specific operating environments, defined by characteristic hourly electricity prices and water inflows (seepage and water use from production activities), at best known through historical observations for the previous year. There is no guarantee that the system design will remain optimal when it faces the water inflows and market determined electricity prices of the year ahead, or subsequent years ahead, because these remain unknown at design time. Consequently, the dewatering optimized system design is adopted subsequently as part of a Model Predictive Control (MPC) strategy that adaptively maintains optimality during the operations phase. Centralized, distributed and non-centralized MPC strategies are explored. Results show that the system can be reliably controlled using any of these control strategies proposed. Under the operating
Optimization of accelerator control
International Nuclear Information System (INIS)
Vasiljev, N.D.; Mozin, I.V.; Shelekhov, V.A.; Efremov, D.V.
1992-01-01
Expensive exploitation of charged particle accelerators is inevitably concerned with requirements of effectively obtaining of the best characteristics of accelerated beams for physical experiments. One of these characteristics is intensity. Increase of intensity is hindered by a number of effects, concerned with the influence of the volume charge field on a particle motion dynamics in accelerator's chamber. However, ultimate intensity, determined by a volume charge, is almost not achieved for the most of the operating accelerators. This fact is caused by losses of particles during injection, at the initial stage of acceleration and during extraction. These losses are caused by deviations the optimal from real characteristics of the accelerating and magnetic system. This is due to a number of circumstances, including technological tolerances on structural elements of systems, influence of measuring and auxiliary equipment and beam consumers' installations, placed in the closed proximity to magnets, and instability in operation of technological systems of accelerator. Control task consists in compensation of deviations of characteristics of magnetic and electric fields by optimal selection of control actions. As for technical means, automatization of modern accelerators allows to solve optimal control problems in real time. Therefore, the report is devoted to optimal control methods and experimental results. (J.P.N.)
Baron, S.; Levison, W. H.
1977-01-01
Application of the optimal control model of the human operator to problems in display analysis is discussed. Those aspects of the model pertaining to the operator-display interface and to operator information processing are reviewed and discussed. The techniques are then applied to the analysis of advanced display/control systems for a Terminal Configured Vehicle. Model results are compared with those obtained in a large, fixed-base simulation.
Shorikov, A. F.; Butsenko, E. V.
2017-10-01
This paper discusses the problem of multicriterial adaptive optimization the control of investment projects in the presence of several technologies. On the basis of network modeling proposed a new economic and mathematical model and a method for solving the problem of multicriterial adaptive optimization the control of investment projects in the presence of several technologies. Network economic and mathematical modeling allows you to determine the optimal time and calendar schedule for the implementation of the investment project and serves as an instrument to increase the economic potential and competitiveness of the enterprise. On a meaningful practical example, the processes of forming network models are shown, including the definition of the sequence of actions of a particular investment projecting process, the network-based work schedules are constructed. The calculation of the parameters of network models is carried out. Optimal (critical) paths have been formed and the optimal time for implementing the chosen technologies of the investment project has been calculated. It also shows the selection of the optimal technology from a set of possible technologies for project implementation, taking into account the time and cost of the work. The proposed model and method for solving the problem of managing investment projects can serve as a basis for the development, creation and application of appropriate computer information systems to support the adoption of managerial decisions by business people.
Optimal control of quantum measurement
Energy Technology Data Exchange (ETDEWEB)
Egger, Daniel; Wilhelm, Frank [Theoretical Physics, Saarland University, 66123 Saarbruecken (Germany)
2015-07-01
Pulses to steer the time evolution of quantum systems can be designed with optimal control theory. In most cases it is the coherent processes that can be controlled and one optimizes the time evolution towards a target unitary process, sometimes also in the presence of non-controllable incoherent processes. Here we show how to extend the GRAPE algorithm in the case where the incoherent processes are controllable and the target time evolution is a non-unitary quantum channel. We perform a gradient search on a fidelity measure based on Choi matrices. We illustrate our algorithm by optimizing a measurement pulse for superconducting phase qubits. We show how this technique can lead to large measurement contrast close to 99%. We also show, within the validity of our model, that this algorithm can produce short 1.4 ns pulses with 98.2% contrast.
Lankford, George Bernard
In this dissertation, we address applying mathematical and numerical techniques in the fields of high energy physics and biomedical sciences. The first portion of this thesis presents a method for optimizing the design of klystron circuits. A klystron is an electron beam tube lined with cavities that emit resonant frequencies to velocity modulate electrons that pass through the tube. Radio frequencies (RF) inserted in the klystron are amplified due to the velocity modulation of the electrons. The routine described in this work automates the selection of cavity positions, resonant frequencies, quality factors, and other circuit parameters to maximize the efficiency with required gain. The method is based on deterministic sampling methods. We will describe the procedure and give several examples for both narrow and wide band klystrons, using the klystron codes AJDISK (Java) and TESLA (Python). The rest of the dissertation is dedicated to developing, calibrating and using a mathematical model for hepatitis C dynamics with triple drug combination therapy. Groundbreaking new drugs, called direct acting antivirals, have been introduced recently to fight off chronic hepatitis C virus infection. The model we introduce is for hepatitis C dynamics treated with the direct acting antiviral drug, telaprevir, along with traditional interferon and ribavirin treatments to understand how this therapy affects the viral load of patients exhibiting different types of response. We use sensitivity and identifiability techniques to determine which parameters can be best estimated from viral load data. We use these estimations to give patient-specific fits of the model to partial viral response, end-of-treatment response, and breakthrough patients. We will then revise the model to incorporate an immune response dynamic to more accurately describe the dynamics. Finally, we will implement a suboptimal control to acquire a drug treatment regimen that will alleviate the systemic cost
Phatak, A. V.; Kessler, K. M.
1975-01-01
The selection of the structure of optimal control type models for the human gunner in an anti aircraft artillery system is considered. Several structures within the LQG framework may be formulated. Two basic types are considered: (1) kth derivative controllers; and (2) proportional integral derivative (P-I-D) controllers. It is shown that a suitable criterion for model structure determination can be based on the ensemble statistics of the tracking error. In the case when the ensemble tracking steady state error is zero, it is suggested that a P-I-D controller formulation be used in preference to the kth derivative controller.
Optimal control of raw timber production processes
Ivan Kolenka
1978-01-01
This paper demonstrates the possibility of optimal planning and control of timber harvesting activ-ities with mathematical optimization models. The separate phases of timber harvesting are represented by coordinated models which can be used to select the optimal decision for the execution of any given phase. The models form a system whose components are connected and...
Pradanti, Paskalia; Hartono
2018-03-01
Determination of insulin injection dose in diabetes mellitus treatment can be considered as an optimal control problem. This article is aimed to simulate optimal blood glucose control for patient with diabetes mellitus. The blood glucose regulation of diabetic patient is represented by Ackerman’s Linear Model. This problem is then solved using dynamic programming method. The desired blood glucose level is obtained by minimizing the performance index in Lagrange form. The results show that dynamic programming based on Ackerman’s Linear Model is quite good to solve the problem.
Stochastic optimal control of non-stationary response of a single-degree-of-freedom vehicle model
Narayanan, S.; Raju, G. V.
1990-09-01
An active suspension system to control the non-stationary response of a single-degree-of-freedom (sdf) vehicle model with variable velocity traverse over a rough road is investigated. The suspension is optimized with respect to ride comfort and road holding, using stochastic optimal control theory. The ground excitation is modelled as a spatial homogeneous random process, being the output of a linear shaping filter to white noise. The effect of the rolling contact of the tyre is considered by an additional filter in cascade. The non-stationary response with active suspension is compared with that of a passive system.
Optimization Method of Intersection Signal Coordinated Control Based on Vehicle Actuated Model
Directory of Open Access Journals (Sweden)
Chen Zhao-Meng
2015-01-01
Full Text Available Traditional timing green wave control with predetermined cycle, split, and offset cannot adapt for dynamic real-time traffic flow. This paper proposes a coordinated control method for variable cycle time green wave bandwidth optimization integrated with traffic-actuated control. In the coordinated control, green split is optimized in real time by the measured presence of arriving and/or standing vehicles in each intersection and simultaneously green waves along arterials are guaranteed. Specifically, the dynamic bound of green wave is firstly determined, and then green early-start and green late-start algorithms are presented respectively to accommodate the fluctuations in vehicle arrival rates in each phase. Numerical examples show that the proposed method improves green time, expands green wave bandwidth, and reduces queuing.
Global optimization for integrated design and control of computationally expensive process models
Egea, J.A.; Vries, D.; Alonso, A.A.; Banga, J.R.
2007-01-01
The problem of integrated design and control optimization of process plants is discussed in this paper. We consider it as a nonlinear programming problem subject to differential-algebraic constraints. This class of problems is frequently multimodal and "costly" (i.e., computationally expensive to
Galelli, Stefano; Goedbloed, Albert; Schmitter, Petra; Castelletti, Andrea
2014-05-01
Urban water reservoirs are a viable adaptation option to account for increasing drinking water demand of urbanized areas as they allow storage and re-use of water that is normally lost. In addition, the direct availability of freshwater reduces pumping costs and diversifies the portfolios of drinking water supply. Yet, these benefits have an associated twofold cost. Firstly, the presence of large, impervious areas increases the hydraulic efficiency of urban catchments, with short time of concentration, increased runoff rates, losses of infiltration and baseflow, and higher risk of flash floods. Secondly, the high concentration of nutrients and sediments characterizing urban discharges is likely to cause water quality problems. In this study we propose a new control scheme combining Model Predictive Control (MPC), hydro-meteorological forecasts and dynamic model emulation to design real-time operating policies that conjunctively optimize water quantity and quality targets. The main advantage of this scheme stands in its capability of exploiting real-time hydro-meteorological forecasts, which are crucial in such fast-varying systems. In addition, the reduced computational requests of the MPC scheme allows coupling it with dynamic emulators of water quality processes. The approach is demonstrated on Marina Reservoir, a multi-purpose reservoir located in the heart of Singapore and characterized by a large, highly urbanized catchment with a short (i.e. approximately one hour) time of concentration. Results show that the MPC scheme, coupled with a water quality emulator, provides a good compromise between different operating objectives, namely flood risk reduction, drinking water supply and salinity control. Finally, the scheme is used to assess the effect of source control measures (e.g. green roofs) aimed at restoring the natural hydrological regime of Marina Reservoir catchment.
Oil Reservoir Production Optimization using Optimal Control
DEFF Research Database (Denmark)
Völcker, Carsten; Jørgensen, John Bagterp; Stenby, Erling Halfdan
2011-01-01
Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using the adjo...... reservoir using water ooding and smart well technology. Compared to the uncontrolled case, the optimal operation increases the Net Present Value of the oil field by 10%.......Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using...
Optimal control of xenon concentration by observer design under reactor model uncertainty
International Nuclear Information System (INIS)
Cho, Nam Z.; Yang, Chae Y.; Woo, Hae S.
1989-01-01
The state feedback in control theory enjoys many advantages, such as stabilization and improved transient response, which could be beneficially used for control of the xenon oscillation in a power reactor. It is, however, not possible in nuclear reactors to measure the state variables, such as xenon and iodine concentrations. For implementation of the optimal state feedback control law, it is thus necessary to estimate the unmeasurable state variables. This paper uses the Luenberger observer to estimate the xenon and iodine concentrations to be used in a linear quadratic problem with state feedback. To overcome the stiffness problem in reactor kinetics, a singular perturbation method is used
Model-based Optimization and Feedback Control of the Current Density Profile Evolution in NSTX-U
Ilhan, Zeki Okan
Nuclear fusion research is a highly challenging, multidisciplinary field seeking contributions from both plasma physics and multiple engineering areas. As an application of plasma control engineering, this dissertation mainly explores methods to control the current density profile evolution within the National Spherical Torus eXperiment-Upgrade (NSTX-U), which is a substantial upgrade based on the NSTX device, which is located in Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ. Active control of the toroidal current density profile is among those plasma control milestones that the NSTX-U program must achieve to realize its next-step operational goals, which are characterized by high-performance, long-pulse, MHD-stable plasma operation with neutral beam heating. Therefore, the aim of this work is to develop model-based, feedforward and feedback controllers that can enable time regulation of the current density profile in NSTX-U by actuating the total plasma current, electron density, and the powers of the individual neutral beam injectors. Motivated by the coupled, nonlinear, multivariable, distributed-parameter plasma dynamics, the first step towards control design is the development of a physics-based, control-oriented model for the current profile evolution in NSTX-U in response to non-inductive current drives and heating systems. Numerical simulations of the proposed control-oriented model show qualitative agreement with the high-fidelity physics code TRANSP. The next step is to utilize the proposed control-oriented model to design an open-loop actuator trajectory optimizer. Given a desired operating state, the optimizer produces the actuator trajectories that can steer the plasma to such state. The objective of the feedforward control design is to provide a more systematic approach to advanced scenario planning in NSTX-U since the development of such scenarios is conventionally carried out experimentally by modifying the tokamak's actuator
Riel, van N.A.W.; Giuseppin, M.L.F.; Verrips, C.T.
2000-01-01
The theory of dynamic optimal metabolic control (DOMC), as developed by Giuseppin and Van Riel (Metab. Eng., 2000), is applied to model the central nitrogen metabolism (CNM) in Saccharomyces cerevisiae. The CNM represents a typical system encountered in advanced metabolic engineering. The CNM is the
Pouchol, Camille; Clairambault, Jean; Lorz, Alexander; Tré lat, Emmanuel
2017-01-01
to the treatment. We analyse the asymptotic behaviour of the model under constant infusion of drugs. By designing an appropriate Lyapunov function, we prove that both cell densities converge to Dirac masses. We then define an optimal control problem, by considering
DEFF Research Database (Denmark)
Ursem, Rasmus Kjær
population and many generations, which essentially turns the problem into a series of related static problems. To our surprise, the control problem could easily be solved when optimized like this. To further examine this, we compared the EA with a particle swarm and a local search approach, which we...... simulate an evolutionary process where the goal is to evolve solutions by means of crossover, mutation, and selection based on their quality (fitness) with respect to the optimization problem at hand. Evolutionary algorithms (EAs) are highly relevant for industrial applications, because they are capable...... of handling problems with non-linear constraints, multiple objectives, and dynamic components – properties that frequently appear in real-world problems. This thesis presents research in three fundamental areas of EC; fitness function design, methods for parameter control, and techniques for multimodal...
Global stability, periodic solutions, and optimal control in a nonlinear differential delay model
Directory of Open Access Journals (Sweden)
Anatoli F. Ivanov
2010-09-01
Full Text Available A nonlinear differential equation with delay serving as a mathematical model of several applied problems is considered. Sufficient conditions for the global asymptotic stability and for the existence of periodic solutions are given. Two particular applications are treated in detail. The first one is a blood cell production model by Mackey, for which new periodicity criteria are derived. The second application is a modified economic model with delay due to Ramsey. An optimization problem for a maximal consumption is stated and solved for the latter.
Optimal control for chemical engineers
Upreti, Simant Ranjan
2013-01-01
Optimal Control for Chemical Engineers gives a detailed treatment of optimal control theory that enables readers to formulate and solve optimal control problems. With a strong emphasis on problem solving, the book provides all the necessary mathematical analyses and derivations of important results, including multiplier theorems and Pontryagin's principle.The text begins by introducing various examples of optimal control, such as batch distillation and chemotherapy, and the basic concepts of optimal control, including functionals and differentials. It then analyzes the notion of optimality, de
WaveSAX device: design optimization through scale modelling and a PTO strategical control system
Peviani, Maximo; Danelli, Andrea; Dadone, Gianluca; Dalmasso, Alberto
2017-04-01
WaveSAX is an innovative OWC (Oscillating Water Column) device for the generation of electricity from wave power, conceived to be installed in coastal marine structures, such as ports and harbours. The device - especially designed for the typical wave climate of Mediterranean Sea - is characterized by two important aspects: flexibility to fit in different structural configurations and replication in a large number of units. A model of the WaveSAX device on a scale 1:5 has been built and tested in the ocean tank at Ecole Centrale de Nantes (France). The study aimed to analyse the behaviour of the device, including two Wells turbine configurations (with three and four blades), with regular and irregular wave conditions in the ocean wave tank. The model and the wave basin were equipped with a series of sensors which allowed to measure the following parameters during the tests: pressure in different points inside the device, the free water surface displacement inside and outside the device, the rotational velocity and the torque at the top of the axis. The tests had the objective to optimize the device design, especially as far as the characteristics of the rotor of the turbine is concern. Although the performance of the WaveSAX has been satisfactory for regular wave conditions, the behaviour of the Wells turbines for irregular wave climate has shown limitations in terms of maintaining the capacity to transform hydraulics energy into mechanical power. To optimize the efficiency of the turbine, an electronical system has been built on the basis of the ocean tank tests. It allows to continuously monitor and command the rotational speed and the torque of the rotor connected with the turbine, and to control in real time the electrical flow of a motor-generator, either absorbing energy as a generator, or providing power to the turbine working as an engine. Two strategies - based on the velocity and the torque control - have been investigate in the electronic test bench
Introduction to optimal control theory
International Nuclear Information System (INIS)
Agrachev, A.A.
2002-01-01
These are lecture notes of the introductory course in Optimal Control theory treated from the geometric point of view. Optimal Control Problem is reduced to the study of controls (and corresponding trajectories) leading to the boundary of attainable sets. We discuss Pontryagin Maximum Principle, basic existence results, and apply these tools to concrete simple optimal control problems. Special sections are devoted to the general theory of linear time-optimal problems and linear-quadratic problems. (author)
Optimal Application Timing of Pest Control Tactics in Nonautonomous Pest Growth Model
Zhang, Shujuan; Liang, Juhua; Tang, Sanyi
2014-01-01
Considering the effects of the living environment on growth of populations, it is unrealistic to assume that the growth rates of predator and prey are all constants in the models with integrated pest management (IPM) strategies. Therefore, a nonautonomous predator-prey system with impulsive effect is developed and investigated in the present work. In order to determine the optimal application timing of IPM tactics, the threshold value which guarantees the stability of pest-free periodic solut...
Pouchol, Camille
2017-10-27
We consider a system of two coupled integro-differential equations modelling populations of healthy and cancer cells under chemotherapy. Both populations are structured by a phenotypic variable, representing their level of resistance to the treatment. We analyse the asymptotic behaviour of the model under constant infusion of drugs. By designing an appropriate Lyapunov function, we prove that both cell densities converge to Dirac masses. We then define an optimal control problem, by considering all possible infusion protocols and minimising the number of cancer cells over a prescribed time frame. We provide a quasi-optimal strategy and prove that it solves this problem for large final times. For this modelling framework, we illustrate our results with numerical simulations, and compare our optimal strategy with periodic treatment schedules.
Optimal control of hydroelectric facilities
Zhao, Guangzhi
This thesis considers a simple yet realistic model of pump-assisted hydroelectric facilities operating in a market with time-varying but deterministic power prices. Both deterministic and stochastic water inflows are considered. The fluid mechanical and engineering details of the facility are described by a model containing several parameters. We present a dynamic programming algorithm for optimizing either the total energy produced or the total cash generated by these plants. The algorithm allows us to give the optimal control strategy as a function of time and to see how this strategy, and the associated plant value, varies with water inflow and electricity price. We investigate various cases. For a single pumped storage facility experiencing deterministic power prices and water inflows, we investigate the varying behaviour for an oversimplified constant turbine- and pump-efficiency model with simple reservoir geometries. We then generalize this simple model to include more realistic turbine efficiencies, situations with more complicated reservoir geometry, and the introduction of dissipative switching costs between various control states. We find many results which reinforce our physical intuition about this complicated system as well as results which initially challenge, though later deepen, this intuition. One major lesson of this work is that the optimal control strategy does not differ much between two differing objectives of maximizing energy production and maximizing its cash value. We then turn our attention to the case of stochastic water inflows. We present a stochastic dynamic programming algorithm which can find an on-average optimal control in the face of this randomness. As the operator of a facility must be more cautious when inflows are random, the randomness destroys facility value. Following this insight we quantify exactly how much a perfect hydrological inflow forecast would be worth to a dam operator. In our final chapter we discuss the
International Nuclear Information System (INIS)
Ding Xudong; Cai Wenjian; Jia Lei; Wen Changyun; Zhang Guiqing
2009-01-01
In this paper, a simple, yet accurate hybrid modeling technique for condensers is presented. The method starts with fundamental physical principles but captures only few key operational characteristic parameters to predict the system performances. The advantages of the methods lie that linear or non-linear least-squares methods can be directly used to determine no more than four key operational characteristic parameters in the model, which can significantly reduce the computational burden. The developed model is verified with the experimental data taken from a pilot system. The testing results confirm that the proposed model can predict accurately the performance of the real-time operating condenser with the maximum error of less than ±10%. The model technique proposed will have wide applications not only in condenser operating optimization, but also in performance assessment and fault detection and diagnosis.
Short-Term Wave Forecasting with AR models in Real-Time Optimal Control of Wave Energy Converters
Fusco, Francesco; Ringwood, John
2010-01-01
Time domain control of wave energy converters requires knowledge of future incident wave elevation in order to approach conditions for optimal energy extraction. Autoregressive models revealed to be a promising approach to the prediction of future values of the wave elevation only from its past history. Results on real wave observations from different ocean locations show that AR models allow to achieve very good predictions for more than one wave period in the future if ...
Menshikh, V.; Samorokovskiy, A.; Avsentev, O.
2018-03-01
The mathematical model of optimizing the allocation of resources to reduce the time for management decisions and algorithms to solve the general problem of resource allocation. The optimization problem of choice of resources in organizational systems in order to reduce the total execution time of a job is solved. This problem is a complex three-level combinatorial problem, for the solving of which it is necessary to implement the solution to several specific problems: to estimate the duration of performing each action, depending on the number of performers within the group that performs this action; to estimate the total execution time of all actions depending on the quantitative composition of groups of performers; to find such a distribution of the existing resource of performers in groups to minimize the total execution time of all actions. In addition, algorithms to solve the general problem of resource allocation are proposed.
Optimal Control of Mechanical Systems
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Vadim Azhmyakov
2007-01-01
Full Text Available In the present work, we consider a class of nonlinear optimal control problems, which can be called “optimal control problems in mechanics.” We deal with control systems whose dynamics can be described by a system of Euler-Lagrange or Hamilton equations. Using the variational structure of the solution of the corresponding boundary-value problems, we reduce the initial optimal control problem to an auxiliary problem of multiobjective programming. This technique makes it possible to apply some consistent numerical approximations of a multiobjective optimization problem to the initial optimal control problem. For solving the auxiliary problem, we propose an implementable numerical algorithm.
Euler's fluid equations: Optimal control vs optimization
International Nuclear Information System (INIS)
Holm, Darryl D.
2009-01-01
An optimization method used in image-processing (metamorphosis) is found to imply Euler's equations for incompressible flow of an inviscid fluid, without requiring that the Lagrangian particle labels exactly follow the flow lines of the Eulerian velocity vector field. Thus, an optimal control problem and an optimization problem for incompressible ideal fluid flow both yield the same Euler fluid equations, although their Lagrangian parcel dynamics are different. This is a result of the gauge freedom in the definition of the fluid pressure for an incompressible flow, in combination with the symmetry of fluid dynamics under relabeling of their Lagrangian coordinates. Similar ideas are also illustrated for SO(N) rigid body motion.
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В.П. Харченко
2010-01-01
Full Text Available The issue of application of Air Traffic Control (ATC competence reference models as a mean of air navigation services provider’s charge optimization is described in the article, and this issue is interpretated as an optimization task. The data relating to the significant growth of aviation traffic, especially using the airspace of Ukraine, given by authors, and the statement of fact that Air Traffic Management (ATM system’s technical component reliability increasement takes place on the basis of practically invariable psychophysiological abilities of aviation controller, make the substantiation of ANSP provision with the most trained ATC controllers for the work on the working places of ATC Unit actual. The ‘mechanism’ of ATC controllers competence reference model creation is defined step-by step. There is an example of candidate’s for the working place competence quantitative individual model forming as a common criteria of competence, which, in its turn, is the compressed format of all parameters of its working activity, received at the stage of control. The approach, according to which the individual parameters of graduating student’s output model’s professional characteristics, which he received after the examination of his work as Tower controller (ATM Unit of aerodrome control service, approach controller or area control service controller, are compared with the predetermined specialist’s competence reference model, relating to the special working place in ATM system, is supposed here. Notably, the conception, relating to the correspondence of the graduating student’s competence output level to the defined reference model of ATC controller, relating to the special working place of ATM Unit, is realised.
Optimal control of hybrid vehicles
Jager, Bram; Kessels, John
2013-01-01
Optimal Control of Hybrid Vehicles provides a description of power train control for hybrid vehicles. The background, environmental motivation and control challenges associated with hybrid vehicles are introduced. The text includes mathematical models for all relevant components in the hybrid power train. The power split problem in hybrid power trains is formally described and several numerical solutions detailed, including dynamic programming and a novel solution for state-constrained optimal control problems based on Pontryagin’s maximum principle. Real-time-implementable strategies that can approximate the optimal solution closely are dealt with in depth. Several approaches are discussed and compared, including a state-of-the-art strategy which is adaptive for vehicle conditions like velocity and mass. Two case studies are included in the book: · a control strategy for a micro-hybrid power train; and · experimental results obtained with a real-time strategy implemented in...
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Silvério Rosa
2018-02-01
Full Text Available A state wide Human Respiratory Syncytial Virus (HRSV surveillance system was implemented in Florida in 1999 to support clinical decision-making for prophylaxis of premature infants. The research presented in this paper addresses the problem of fitting real data collected by the Florida HRSV surveillance system by using a periodic SEIRS mathematical model. A sensitivity and cost-effectiveness analysis of the model is done and an optimal control problem is formulated and solved with treatment as the control variable.
On Optimizing H. 264/AVC Rate Control by Improving R-D Model and Incorporating HVS Characteristics
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Jiang Gangyi
2010-01-01
Full Text Available The state-of-the-art JVT-G012 rate control algorithm of H.264 is improved from two aspects. First, the quadratic rate-distortion (R-D model is modified based on both empirical observations and theoretical analysis. Second, based on the existing physiological and psychological research findings of human vision, the rate control algorithm is optimized by incorporating the main characteristics of the human visual system (HVS such as contrast sensitivity, multichannel theory, and masking effect. Experiments are conducted, and experimental results show that the improved algorithm can simultaneously enhance the overall subjective visual quality and improve the rate control precision effectively.
Economic policy optimization based on both one stochastic model and the parametric control theory
Ashimov, Abdykappar; Borovskiy, Yuriy; Onalbekov, Mukhit
2016-06-01
A nonlinear dynamic stochastic general equilibrium model with financial frictions is developed to describe two interacting national economies in the environment of the rest of the world. Parameters of nonlinear model are estimated based on its log-linearization by the Bayesian approach. The nonlinear model is verified by retroprognosis, estimation of stability indicators of mappings specified by the model, and estimation the degree of coincidence for results of internal and external shocks' effects on macroeconomic indicators on the basis of the estimated nonlinear model and its log-linearization. On the base of the nonlinear model, the parametric control problems of economic growth and volatility of macroeconomic indicators of Kazakhstan are formulated and solved for two exchange rate regimes (free floating and managed floating exchange rates)
Dynamic Modeling of Steam Condenser and Design of PI Controller Based on Grey Wolf Optimizer
Shu-Xia Li; Jie-Sheng Wang
2015-01-01
Shell-and-tube condenser is a heat exchanger for cooling steam with high temperature and pressure, which is one of the main kinds of heat exchange equipment in thermal, nuclear, and marine power plant. Based on the lumped parameter modeling method, the dynamic mathematical model of the simplified steam condenser is established. Then, the pressure PI control system of steam condenser based on the Matlab/Simulink simulation platform is designed. In order to obtain better performance, a new meta...
Becus, Georges A.; Chan, Alistair K.
1993-01-01
Three neural network processing approaches in a direct numerical optimization model reduction scheme are proposed and investigated. Large structural systems, such as large space structures, offer new challenges to both structural dynamicists and control engineers. One such challenge is that of dimensionality. Indeed these distributed parameter systems can be modeled either by infinite dimensional mathematical models (typically partial differential equations) or by high dimensional discrete models (typically finite element models) often exhibiting thousands of vibrational modes usually closely spaced and with little, if any, damping. Clearly, some form of model reduction is in order, especially for the control engineer who can actively control but a few of the modes using system identification based on a limited number of sensors. Inasmuch as the amount of 'control spillover' (in which the control inputs excite the neglected dynamics) and/or 'observation spillover' (where neglected dynamics affect system identification) is to a large extent determined by the choice of particular reduced model (RM), the way in which this model reduction is carried out is often critical.
van Riel, N A; Giuseppin, M L; Verrips, C T
2000-01-01
The theory of dynamic optimal metabolic control (DOMC), as developed by Giuseppin and Van Riel (Metab. Eng., 2000), is applied to model the central nitrogen metabolism (CNM) in Saccharomyces cerevisiae. The CNM represents a typical system encountered in advanced metabolic engineering. The CNM is the source of the cellular amino acids and proteins, including flavors and potentially valuable biomolecules; therefore, it is also of industrial interest. In the DOMC approach the cell is regarded as an optimally controlled system. Given the metabolic genotype, the cell faces a control problem to maintain an optimal flux distribution in a changing environment. The regulation is based on strategies and balances feedback control of homeostasis and feedforward regulation for adaptation. The DOMC approach is an integrative, holistic approach, not based on mechanistic descriptions and (therefore) not biased by the variation present in biochemical and molecular biological data. It is an effective tool to structure the rapidly increasing amount of data on the function of genes and pathways. The DOMC model is used successfully to predict the responses of pulses of ammonia and glutamine to nitrogen-limited continuous cultures of a wild-type strain and a glutamine synthetase-negative mutant. The simulation results are validated with experimental data.
Sun, Kaibiao; Zhang, Tonghua; Tian, Yuan
2016-09-01
This work presents a pest control predator-prey model, where rate of change in prey density follows a scaling law with exponent less than one and the control is by an integrated management strategy. The aim is to investigate the change in system dynamics and determine a pest control level with minimum control price. First, the dynamics of the proposed model without control is investigated by taking the exponent as an index parameter. And then, to determine the frequency of spraying chemical pesticide and yield releases of the predator, the existence of the order-1 periodic orbit of the control system is discussed in cases. Furthermore, to ensure a certain robustness of the adopted control, i.e., for an inaccurately detected species density or a deviation, the control system could be stabilized at the order-1 periodic orbit, the stability of the order-1 periodic orbit is verified by an stability criterion for a general semi-continuous dynamical system. In addition, to minimize the total cost input in pest control, an optimization problem is formulated and the optimum pest control level is obtained. At last, the numerical simulations with a specific model are carried out to complement the theoretical results. Copyright © 2016 Elsevier Inc. All rights reserved.
Optimal control of a head-of-line processor sharing model with regular and opportunity customers
Wijk, van A.C.C.
2011-01-01
Motivated by a workload control setting, we study a model where two types of customers are served by a single server according to the head-of-line processor sharing discipline. Regular customers and opportunity customers are arriving to the system according to two independent Poisson processes, each
Chen, Xiangyong; Zhang, Ancai
2014-01-01
For the particularity of warfare hybrid dynamic process, a class of warfare hybrid dynamic systems is established based on Lanchester equation in a (n,1) battle, where a heterogeneous force of n different troop types faces a homogeneous force. This model can be characterized by the interaction of continuous-time models (governed by Lanchester equation), and discrete event systems (described by variable tactics). Furthermore, an expository discussion is presented on an optimal variable tact...
Optimal Control of Production and Remanufacturing in a Reverse Logistics Model with Backlogging
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I. Konstantaras
2010-01-01
Full Text Available Reverse logistics activities have received increasing attention within logistics and operations management during the last years, both from a theoretical and a practical point of view. The field of reverse logistics includes all logistics processes starting with the take-back of used products from customers up to the stage of making them reusable products or disposing them. In this paper, a single-product recovery system is studied. In such system, used products are collected from customers and are kept at the recoverable inventory warehouse in view to be recovered. The constant demand rate can be satisfied either by newly produced products or by recovered ones (serviceable inventory, which are regarded as perfectly as the new ones. Excess demand is completely backlogged. Following an exact analytical approach, the optimal set-up numbers and the optimal lot sizes for the production of new products and for the recovery of returned products are obtained. A numerical cost comparison of this model with the corresponding one without backordering is also performed.
Directory of Open Access Journals (Sweden)
Samson S Kiware
Full Text Available Despite great achievements by insecticide-treated nets (ITNs and indoor residual spraying (IRS in reducing malaria transmission, it is unlikely these tools will be sufficient to eliminate malaria transmission on their own in many settings today. Fortunately, field experiments indicate that there are many promising vector control interventions that can be used to complement ITNs and/or IRS by targeting a wide range of biological and environmental mosquito resources. The majority of these experiments were performed to test a single vector control intervention in isolation; however, there is growing evidence and consensus that effective vector control with the goal of malaria elimination will require a combination of interventions.We have developed a model of mosquito population dynamic to describe the mosquito life and feeding cycles and to optimize the impact of vector control intervention combinations at suppressing mosquito populations. The model simulations were performed for the main three malaria vectors in sub-Saharan Africa, Anopheles gambiae s.s, An. arabiensis and An. funestus. We considered areas having low, moderate and high malaria transmission, corresponding to entomological inoculation rates of 10, 50 and 100 infective bites per person per year, respectively. In all settings, we considered baseline ITN coverage of 50% or 80% in addition to a range of other vector control tools to interrupt malaria transmission. The model was used to sweep through parameters space to select the best optimal intervention packages. Sample model simulations indicate that, starting with ITNs at a coverage of 50% (An. gambiae s.s. and An. funestus or 80% (An. arabiensis and adding interventions that do not require human participation (e.g. larviciding at 80% coverage, endectocide treated cattle at 50% coverage and attractive toxic sugar baits at 50% coverage may be sufficient to suppress all the three species to an extent required to achieve local malaria
Optimal Control Inventory Stochastic With Production Deteriorating
Affandi, Pardi
2018-01-01
In this paper, we are using optimal control approach to determine the optimal rate in production. Most of the inventory production models deal with a single item. First build the mathematical models inventory stochastic, in this model we also assume that the items are in the same store. The mathematical model of the problem inventory can be deterministic and stochastic models. In this research will be discussed how to model the stochastic as well as how to solve the inventory model using optimal control techniques. The main tool in the study problems for the necessary optimality conditions in the form of the Pontryagin maximum principle involves the Hamilton function. So we can have the optimal production rate in a production inventory system where items are subject deterioration.
Optimization analysis of propulsion motor control efficiency
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CAI Qingnan
2017-12-01
Full Text Available [Objectives] This paper aims to strengthen the control effect of propulsion motors and decrease the energy used during actual control procedures.[Methods] Based on the traditional propulsion motor equivalence circuit, we increase the iron loss current component, introduce the definition of power matching ratio, calculate the highest efficiency of a motor at a given speed and discuss the flux corresponding to the power matching ratio with the highest efficiency. In the original motor vector efficiency optimization control module, an efficiency optimization control module is added so as to achieve motor efficiency optimization and energy conservation.[Results] MATLAB/Simulink simulation data shows that the efficiency optimization control method is suitable for most conditions. The operation efficiency of the improved motor model is significantly higher than that of the original motor model, and its dynamic performance is good.[Conclusions] Our motor efficiency optimization control method can be applied in engineering to achieve energy conservation.
Model and experiments to optimize co-adaptation in a simplified myoelectric control system.
Couraud, M; Cattaert, D; Paclet, F; Oudeyer, P Y; de Rugy, A
2018-04-01
To compensate for a limb lost in an amputation, myoelectric prostheses use surface electromyography (EMG) from the remaining muscles to control the prosthesis. Despite considerable progress, myoelectric controls remain markedly different from the way we normally control movements, and require intense user adaptation. To overcome this, our goal is to explore concurrent machine co-adaptation techniques that are developed in the field of brain-machine interface, and that are beginning to be used in myoelectric controls. We combined a simplified myoelectric control with a perturbation for which human adaptation is well characterized and modeled, in order to explore co-adaptation settings in a principled manner. First, we reproduced results obtained in a classical visuomotor rotation paradigm in our simplified myoelectric context, where we rotate the muscle pulling vectors used to reconstruct wrist force from EMG. Then, a model of human adaptation in response to directional error was used to simulate various co-adaptation settings, where perturbations and machine co-adaptation are both applied on muscle pulling vectors. These simulations established that a relatively low gain of machine co-adaptation that minimizes final errors generates slow and incomplete adaptation, while higher gains increase adaptation rate but also errors by amplifying noise. After experimental verification on real subjects, we tested a variable gain that cumulates the advantages of both, and implemented it with directionally tuned neurons similar to those used to model human adaptation. This enables machine co-adaptation to locally improve myoelectric control, and to absorb more challenging perturbations. The simplified context used here enabled to explore co-adaptation settings in both simulations and experiments, and to raise important considerations such as the need for a variable gain encoded locally. The benefits and limits of extending this approach to more complex and functional
Model and experiments to optimize co-adaptation in a simplified myoelectric control system
Couraud, M.; Cattaert, D.; Paclet, F.; Oudeyer, P. Y.; de Rugy, A.
2018-04-01
Objective. To compensate for a limb lost in an amputation, myoelectric prostheses use surface electromyography (EMG) from the remaining muscles to control the prosthesis. Despite considerable progress, myoelectric controls remain markedly different from the way we normally control movements, and require intense user adaptation. To overcome this, our goal is to explore concurrent machine co-adaptation techniques that are developed in the field of brain-machine interface, and that are beginning to be used in myoelectric controls. Approach. We combined a simplified myoelectric control with a perturbation for which human adaptation is well characterized and modeled, in order to explore co-adaptation settings in a principled manner. Results. First, we reproduced results obtained in a classical visuomotor rotation paradigm in our simplified myoelectric context, where we rotate the muscle pulling vectors used to reconstruct wrist force from EMG. Then, a model of human adaptation in response to directional error was used to simulate various co-adaptation settings, where perturbations and machine co-adaptation are both applied on muscle pulling vectors. These simulations established that a relatively low gain of machine co-adaptation that minimizes final errors generates slow and incomplete adaptation, while higher gains increase adaptation rate but also errors by amplifying noise. After experimental verification on real subjects, we tested a variable gain that cumulates the advantages of both, and implemented it with directionally tuned neurons similar to those used to model human adaptation. This enables machine co-adaptation to locally improve myoelectric control, and to absorb more challenging perturbations. Significance. The simplified context used here enabled to explore co-adaptation settings in both simulations and experiments, and to raise important considerations such as the need for a variable gain encoded locally. The benefits and limits of extending this
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M. E. Haji Abadi
2013-09-01
Full Text Available In this paper, the continuous optimal control theory is used to model and solve the maximum entropy problem for a continuous random variable. The maximum entropy principle provides a method to obtain least-biased probability density function (Pdf estimation. In this paper, to find a closed form solution for the maximum entropy problem with any number of moment constraints, the entropy is considered as a functional measure and the moment constraints are considered as the state equations. Therefore, the Pdf estimation problem can be reformulated as the optimal control problem. Finally, the proposed method is applied to estimate the Pdf of the hourly electricity prices of New England and Ontario electricity markets. Obtained results show the efficiency of the proposed method.
Finite approximations in discrete-time stochastic control quantized models and asymptotic optimality
Saldi, Naci; Yüksel, Serdar
2018-01-01
In a unified form, this monograph presents fundamental results on the approximation of centralized and decentralized stochastic control problems, with uncountable state, measurement, and action spaces. It demonstrates how quantization provides a system-independent and constructive method for the reduction of a system with Borel spaces to one with finite state, measurement, and action spaces. In addition to this constructive view, the book considers both the information transmission approach for discretization of actions, and the computational approach for discretization of states and actions. Part I of the text discusses Markov decision processes and their finite-state or finite-action approximations, while Part II builds from there to finite approximations in decentralized stochastic control problems. This volume is perfect for researchers and graduate students interested in stochastic controls. With the tools presented, readers will be able to establish the convergence of approximation models to original mo...
Computational Modelling and Optimal Control of Ebola Virus Disease with non-Linear Incidence Rate
Takaidza, I.; Makinde, O. D.; Okosun, O. K.
2017-03-01
The 2014 Ebola outbreak in West Africa has exposed the need to connect modellers and those with relevant data as pivotal to better understanding of how the disease spreads and quantifying the effects of possible interventions. In this paper, we model and analyse the Ebola virus disease with non-linear incidence rate. The epidemic model created is used to describe how the Ebola virus could potentially evolve in a population. We perform an uncertainty analysis of the basic reproductive number R 0 to quantify its sensitivity to other disease-related parameters. We also analyse the sensitivity of the final epidemic size to the time control interventions (education, vaccination, quarantine and safe handling) and provide the cost effective combination of the interventions.
Optimizing models for production and inventory control using a genetic algorithm
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Dragan S. Pamučar
2012-01-01
Full Text Available In order to make the Economic Production Quantity (EPQ model more applicable to real-world production and inventory control problems, in this paper we expand this model by assuming that some imperfect items of different product types being produced such as reworks are allowed. In addition, we may have more than one product and supplier along with warehouse space and budget limitation. We show that the model of the problem is a constrained non-linear integer program and propose a genetic algorithm to solve it. Moreover, a design of experiments is employed to calibrate the parameters of the algorithm for different problem sizes. In the end, a numerical example is presented to demonstrate the application of the proposed methodology.
Roy, Sandip; McElwain, Terry F; Wan, Yan
2011-10-01
Developing control policies for zoonotic diseases is challenging, both because of the complex spread dynamics exhibited by these diseases, and because of the need for implementing complex multi-species surveillance and control efforts using limited resources. Mathematical models, and in particular network models, of disease spread are promising as tools for control-policy design, because they can provide comprehensive quantitative representations of disease transmission. A layered dynamical network model for the transmission and control of zoonotic diseases is introduced as a tool for analyzing disease spread and designing cost-effective surveillance and control. The model development is achieved using brucellosis transmission among wildlife, cattle herds, and human sub-populations in an agricultural system as a case study. Precisely, a model that tracks infection counts in interacting animal herds of multiple species (e.g., cattle herds and groups of wildlife for brucellosis) and in human subpopulations is introduced. The model is then abstracted to a form that permits comprehensive targeted design of multiple control capabilities as well as model identification from data. Next, techniques are developed for such quantitative design of control policies (that are directed to both the animal and human populations), and for model identification from snapshot and time-course data, by drawing on recent results in the network control community. The modeling approach is shown to provide quantitative insight into comprehensive control policies for zoonotic diseases, and in turn to permit policy design for mitigation of these diseases. For the brucellosis-transmission example in particular, numerous insights are obtained regarding the optimal distribution of resources among available control capabilities (e.g., vaccination, surveillance and culling, pasteurization of milk) and points in the spread network (e.g., transhumance vs. sedentary herds). In addition, a preliminary
Optimization Modeling with Spreadsheets
Baker, Kenneth R
2011-01-01
This introductory book on optimization (mathematical programming) includes coverage on linear programming, nonlinear programming, integer programming and heuristic programming; as well as an emphasis on model building using Excel and Solver. The emphasis on model building (rather than algorithms) is one of the features that makes this book distinctive. Most books devote more space to algorithmic details than to formulation principles. These days, however, it is not necessary to know a great deal about algorithms in order to apply optimization tools, especially when relying on the sp
Connolly, Joseph W.; Csank, Jeffrey Thomas; Chicatelli, Amy; Kilver, Jacob
2013-01-01
This paper covers the development of a model-based engine control (MBEC) methodology featuring a self tuning on-board model applied to an aircraft turbofan engine simulation. Here, the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40k) serves as the MBEC application engine. CMAPSS40k is capable of modeling realistic engine performance, allowing for a verification of the MBEC over a wide range of operating points. The on-board model is a piece-wise linear model derived from CMAPSS40k and updated using an optimal tuner Kalman Filter (OTKF) estimation routine, which enables the on-board model to self-tune to account for engine performance variations. The focus here is on developing a methodology for MBEC with direct control of estimated parameters of interest such as thrust and stall margins. Investigations using the MBEC to provide a stall margin limit for the controller protection logic are presented that could provide benefits over a simple acceleration schedule that is currently used in traditional engine control architectures.
Verta, Antonella; Schena, Emiliano; Silvestri, Sergio
2010-06-01
The control of thermo-hygrometric conditions of gas delivered in neonatal mechanical ventilation appears to be a particularly difficult task, mainly due to the vast number of parameters to be monitored and the control strategies of heated humidifiers to be adopted. In the present paper, we describe the heat and fluid exchange occurring in a heated humidifier in mathematical terms; we analyze the sensitivity of the relative humidity of outlet gas as a function of thermo-hygrometric and fluid-dynamic parameters of delivered gas; we propose a control strategy that will enable the stability of outlet gas thermo-hygrometric conditions. The mathematical model is represented by a hyper-surface containing the functional relations between the input variables, which must be measured, and the output variables, which have to remain constant. Model sensitivity analysis shows that heated humidifier efficacy and stability of outlet gas thermo-hygrometric conditions are principally influenced by four parameters: liquid surface temperature, gas flow rate, inlet gas temperature and inlet gas relative humidity. The theoretical model has been experimentally validated in typical working conditions of neonatal applications. The control strategy has been implemented by a minimal measurement system composed of three thermometers, a humidity sensor, and a flow rate sensor, and based on the theoretical model. Outlet relative humidity, contained in the range 90+/-4% and 94+/-4%, corresponding with temperature variations in the range 28+/-2 degrees C and 38+/-2 degrees C respectively, has been obtained in the whole flow rate range typical of neonatal ventilation from 1 to 10 L/min. We conclude that in order to obtain the stability of the thermo-hygrometric conditions of the delivered gas mixture: (a) a control strategy with a more complex measurement system must be implemented (i.e. providing more input variables); (b) and the gas may also need to be pre-warmed before entering the humidifying
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I. P. Bolodurina
2017-01-01
Full Text Available Currently, the proportion of use of cloud computing technology in today's business processes of companies is growing steadily. Despite the fact that it allows you to reduce the cost of ownership and operation of IT infrastructure, there are a number of problems related to the control of data centers. One such problem is the efficiency of the use of available companies compute and network resources. One of the directions of optimization is the process of traffic control of cloud applications and services in data centers. Given the multi-tier architecture of modern data center, this problem does not quite trivial. The advantage of modern virtual infrastructure is the ability to use software-configurable networks and software-configurable data storages. However, existing solutions with algorithmic optimization does not take into account a number of features forming network traffic with multiple classes of applications. Within the framework of the exploration solved the problem of optimizing the distribution of traffic cloud applications and services for the software-controlled virtual data center infrastructure. A simulation model describing the traffic in data center and software-configurable network segments involved in the processing of user requests for applications and services located network environment that includes a heterogeneous cloud platform and software-configurable data storages. The developed model has allowed to implement cloud applications traffic management algorithm and optimize access to the storage system through the effective use of the channel for data transmission. In experimental studies found that the application of the developed algorithm can reduce the response time of cloud applications and services, and as a result improve the performance of processing user requests and to reduce the number of failures.
International Nuclear Information System (INIS)
Kurosaki, Yuzuru; Ho, Tak-San; Rabitz, Herschel
2014-01-01
We construct a two-state one-dimensional reaction-path model for ozone open → cyclic isomerization dynamics. The model is based on the intrinsic reaction coordinate connecting the cyclic and open isomers with the O 2 + O asymptote on the ground-state 1 A ′ potential energy surface obtained with the high-level ab initio method. Using this two-state model time-dependent wave packet optimal control simulations are carried out. Two possible pathways are identified along with their respective band-limited optimal control fields; for pathway 1 the wave packet initially associated with the open isomer is first pumped into a shallow well on the excited electronic state potential curve and then driven back to the ground electronic state to form the cyclic isomer, whereas for pathway 2 the corresponding wave packet is excited directly to the primary well of the excited state potential curve. The simulations reveal that the optimal field for pathway 1 produces a final yield of nearly 100% with substantially smaller intensity than that obtained in a previous study [Y. Kurosaki, M. Artamonov, T.-S. Ho, and H. Rabitz, J. Chem. Phys. 131, 044306 (2009)] using a single-state one-dimensional model. Pathway 2, due to its strong coupling to the dissociation channel, is less effective than pathway 1. The simulations also show that nonlinear field effects due to molecular polarizability and hyperpolarizability are small for pathway 1 but could become significant for pathway 2 because much higher field intensity is involved in the latter. The results suggest that a practical control may be feasible with the aid of a few lowly excited electronic states for ozone isomerization
Optimal control systems in hydro power plants
International Nuclear Information System (INIS)
Babunski, Darko L.
2012-01-01
The aim of the research done in this work is focused on obtaining the optimal models of hydro turbine including auxiliary equipment, analysis of governors for hydro power plants and analysis and design of optimal control laws that can be easily applicable in real hydro power plants. The methodology of the research and realization of the set goals consist of the following steps: scope of the models of hydro turbine, and their modification using experimental data; verification of analyzed models and comparison of advantages and disadvantages of analyzed models, with proposal of turbine model for design of control low; analysis of proportional-integral-derivative control with fixed parameters and gain scheduling and nonlinear control; analysis of dynamic characteristics of turbine model including control and comparison of parameters of simulated system with experimental data; design of optimal control of hydro power plant considering proposed cost function and verification of optimal control law with load rejection measured data. The hydro power plant models, including model of power grid are simulated in case of island ing and restoration after breakup and load rejection with consideration of real loading and unloading of hydro power plant. Finally, simulations provide optimal values of control parameters, stability boundaries and results easily applicable to real hydro power plants. (author)
Directory of Open Access Journals (Sweden)
Didi Faouzi
2017-06-01
become increasingly sophisticated and of an industrial nature (heating, air conditioning, control, computer, regulation, etc. New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence such as neural networks and / or fuzzy logic, etc. As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year, The application of artificial intelligence in the industry known for considerable growth, which is not the case in the field of agricultural greenhouses, where enforcement remains timid. It is from this fact, we undertake research work in this area and conduct a simulation based on meteorological data through MATLAB Simulink to finally analyze the thermal behavior - greenhouse microclimate energy.
Subthreshold SPICE Model Optimization
Lum, Gregory; Au, Henry; Neff, Joseph; Bozeman, Eric; Kamin, Nick; Shimabukuro, Randy
2011-04-01
The first step in integrated circuit design is the simulation of said design in software to verify proper functionally and design requirements. Properties of the process are provided by fabrication foundries in the form of SPICE models. These SPICE models contain the electrical data and physical properties of the basic circuit elements. A limitation of these models is that the data collected by the foundry only accurately model the saturation region. This is fine for most users, but when operating devices in the subthreshold region they are inadequate for accurate simulation results. This is why optimizing the current SPICE models to characterize the subthreshold region is so important. In order to accurately simulate this region of operation, MOSFETs of varying widths and lengths are fabricated and the electrical test data is collected. From the data collected the parameters of the model files are optimized through parameter extraction rather than curve fitting. With the completed optimized models the circuit designer is able to simulate circuit designs for the sub threshold region accurately.
Directory of Open Access Journals (Sweden)
Alexander Mikhajlovich Tarasyev
2014-09-01
Full Text Available In this paper, we develop an economic growth model taking into account two factors of production: fixed capital and labor force, to study the dynamics of GDP growth. The dependence of the output of these factors is described by a production function of the exponential type. Within the framework of the optimal control theory, the optimization problem for investment levels is being solved to maximize the integral index of consumption. We study the qualitative properties of optimal trajectories as solutions of the Hamiltonian systems arising in Pontryagin’s maximum principle. The sensitivity analysis of the equilibrium solutions of the economic system is implemented with respect to the elasticity coefficients of the production function, the depreciation rate of the capital, and the discount factor, and growth trends are indicated. The econometric analysis of the model parameters is provided basing on real data for the Russian economy. In accordance with the results of the regression analysis, the projection of economic development is constructed in conditions of the applicability of the economic growth model.
Hu, Dawei; Liu, Hong; Yang, Chenliang; Hu, Enzhu
As a subsystem of the bioregenerative life support system (BLSS), light-algae bioreactor (LABR) has properties of high reaction rate, efficiently synthesizing microalgal biomass, absorbing CO2 and releasing O2, so it is significant for BLSS to provide food and maintain gas balance. In order to manipulate the LABR properly, it has been designed as a closed-loop control system, and technology of Artificial Neural Network-Model Predictive Control (ANN-MPC) is applied to design the controller for LABR in which green microalgae, Spirulina platensis is cultivated continuously. The conclusion is drawn by computer simulation that ANN-MPC controller can intelligently learn the complicated dynamic performances of LABR, and automatically, robustly and self-adaptively regulate the light intensity illuminating on the LABR, hence make the growth of microalgae in the LABR be changed in line with the references, meanwhile provide appropriate damping to improve markedly the transient response performance of LABR.
Optimal magnetic attitude control
DEFF Research Database (Denmark)
Wisniewski, Rafal; Markley, F.L.
1999-01-01
because control torques can only be generated perpendicular to the local geomagnetic field vector. This has been a serious obstacle for using magnetorquer based control for three-axis stabilization of a low earth orbit satellite. The problem of controlling the spacecraft attitude using only magnetic...
Optimal management strategies in variable environments: Stochastic optimal control methods
Williams, B.K.
1985-01-01
Dynamic optimization was used to investigate the optimal defoliation of salt desert shrubs in north-western Utah. Management was formulated in the context of optimal stochastic control theory, with objective functions composed of discounted or time-averaged biomass yields. Climatic variability and community patterns of salt desert shrublands make the application of stochastic optimal control both feasible and necessary. A primary production model was used to simulate shrub responses and harvest yields under a variety of climatic regimes and defoliation patterns. The simulation results then were used in an optimization model to determine optimal defoliation strategies. The latter model encodes an algorithm for finite state, finite action, infinite discrete time horizon Markov decision processes. Three questions were addressed: (i) What effect do changes in weather patterns have on optimal management strategies? (ii) What effect does the discounting of future returns have? (iii) How do the optimal strategies perform relative to certain fixed defoliation strategies? An analysis was performed for the three shrub species, winterfat (Ceratoides lanata), shadscale (Atriplex confertifolia) and big sagebrush (Artemisia tridentata). In general, the results indicate substantial differences among species in optimal control strategies, which are associated with differences in physiological and morphological characteristics. Optimal policies for big sagebrush varied less with variation in climate, reserve levels and discount rates than did either shadscale or winterfat. This was attributed primarily to the overwintering of photosynthetically active tissue and to metabolic activity early in the growing season. Optimal defoliation of shadscale and winterfat generally was more responsive to differences in plant vigor and climate, reflecting the sensitivity of these species to utilization and replenishment of carbohydrate reserves. Similarities could be seen in the influence of both
Optimal Operation of Industrial Batch Crystallizers : A Nonlinear Model-based Control Approach
Mesbah, A.
2010-01-01
Batch crystallization is extensively employed in the chemical, pharmaceutical, and food industries to separate and purify high value-added chemical substances. Despite their widespread application, optimal operation of batch crystallizers is particularly challenging. The difficulties primarily
Optimal Strategy and Business Models
DEFF Research Database (Denmark)
Johnson, Peter; Foss, Nicolai Juul
2016-01-01
This study picks up on earlier suggestions that control theory may further the study of strategy. Strategy can be formally interpreted as an idealized path optimizing heterogeneous resource deployment to produce maximum financial gain. Using standard matrix methods to describe the firm Hamiltonia...... variable of firm path, suggesting in turn that the firm's business model is the codification of the application of investment resources used to control the strategic path of value realization....
Directory of Open Access Journals (Sweden)
Shuai Su
2016-02-01
Full Text Available Increasing attention is being paid to the energy efficiency in metro systems to reduce the operational cost and to advocate the sustainability of railway systems. Classical research has studied the energy-efficient operational strategy and the energy-efficient system design separately to reduce the traction energy consumption. This paper aims to combine the operational strategies and the system design by analyzing how the infrastructure and vehicle parameters of metro systems influence the operational traction energy consumption. Firstly, a solution approach to the optimal train control model is introduced, which is used to design the Optimal Train Control Simulator(OTCS. Then, based on the OTCS, the performance of some important energy-efficient system design strategies is investigated to reduce the trains’ traction energy consumption, including reduction of the train mass, improvement of the kinematic resistance, the design of the energy-saving gradient, increasing the maximum traction and braking forces, introducing regenerative braking and timetable optimization. As for these energy-efficient strategies, the performances are finally evaluated using the OTCS with the practical operational data of the Beijing Yizhuang metro line. The proposed approach gives an example to quantitatively analyze the energy reduction of different strategies in the system design procedure, which may help the decision makers to have an overview of the energy-efficient performances and then to make decisions by balancing the costs and the benefits.
Optimal control in thermal engineering
Badescu, Viorel
2017-01-01
This book is the first major work covering applications in thermal engineering and offering a comprehensive introduction to optimal control theory, which has applications in mechanical engineering, particularly aircraft and missile trajectory optimization. The book is organized in three parts: The first part includes a brief presentation of function optimization and variational calculus, while the second part presents a summary of the optimal control theory. Lastly, the third part describes several applications of optimal control theory in solving various thermal engineering problems. These applications are grouped in four sections: heat transfer and thermal energy storage, solar thermal engineering, heat engines and lubrication.Clearly presented and easy-to-use, it is a valuable resource for thermal engineers and thermal-system designers as well as postgraduate students.
Alirezaei, M.; Kanarachos, S.A.; Scheepers, B.T.M.; Maurice, J.P.
2013-01-01
The Integrated Vehicle Safety Department of TNO (Dutch Organization for Applied Scientific Research) investigates the application of modern control methods in the Integrated Vehicle Dynamics Control (IVDC) field, as a strategic research topic of the Beyond Safe framework. The aim of IVDC is to
OPTIMAL CONTROL FOR ELECTRIC VEHICLE STABILIZATION
Directory of Open Access Journals (Sweden)
MARIAN GAICEANU
2016-01-01
Full Text Available This main objective of the paper is to stabilize an electric vehicle in optimal manner to a step lane change maneuver. To define the mathematical model of the vehicle, the rigid body moving on a plane is taken into account. An optimal lane keeping controller delivers the adequate angles in order to stabilize the vehicle’s trajectory in an optimal way. Two degree of freedom linear bicycle model is adopted as vehicle model, consisting of lateral and yaw motion equations. The proposed control maintains the lateral stability by taking the feedback information from the vehicle transducers. In this way only the lateral vehicle’s dynamics are enough to considerate. Based on the obtained linear mathematical model the quadratic optimal control is designed in order to maintain the lateral stability of the electric vehicle. The numerical simulation results demonstrate the feasibility of the proposed solution.
Karam, Ayman M.
2016-12-01
Membrane Distillation (MD) is an emerging sustainable desalination technique. While MD has many advantages and can be powered by solar thermal energy, its main drawback is the low water production rate. However, the MD process has not been fully optimized in terms of its manipulated and controlled variables. This is largely due to the lack of adequate dynamic models to study and simulate the process. In addition, MD is prone to membrane fouling, which is a fault that degrades the performance of the MD process. This work has three contributions to address these challenges. First, we derive a mathematical model of Direct Contact Membrane Distillation (DCMD), which is the building block for the next parts. Then, the proposed model is extended to account for membrane fouling and an observer-based fouling detection method is developed. Finally, various control strategies are implemented to optimize the performance of the DCMD solar-powered process. In part one, a reduced-order dynamic model of DCMD is developed based on lumped capacitance method and electrical analogy to thermal systems. The result is an electrical equivalent thermal network to the DCMD process, which is modeled by a system of nonlinear differential algebraic equations (DAEs). This model predicts the water-vapor flux and the temperature distribution along the module length. Experimental data is collected to validate the steady-state and dynamic responses of the proposed model, with great agreement demonstrated in both. The second part proposes an extension of the model to account for membrane fouling. An adaptive observer for DAE systems is developed and convergence proof is presented. A method for membrane fouling detection is then proposed based on adaptive observers. Simulation results demonstrate the performance of the membrane fouling detection method. Finally, an optimization problem is formulated to maximize the process efficiency of a solar-powered DCMD. The adapted method is known as Extremum
Optimal strategies for controlling riverine tsetse flies using targets: a modelling study.
Directory of Open Access Journals (Sweden)
Glyn A Vale
2015-03-01
Full Text Available Tsetse flies occur in much of sub-Saharan Africa where they transmit the trypanosomes that cause the diseases of sleeping sickness in humans and nagana in livestock. One of the most economical and effective methods of tsetse control is the use of insecticide-treated screens, called targets, that simulate hosts. Targets have been ~1 m2, but recently it was shown that those tsetse that occupy riverine situations, and which are the main vectors of sleeping sickness, respond well to targets only ~0.06 m2. The cheapness of these tiny targets suggests the need to reconsider what intensity and duration of target deployments comprise the most cost-effective strategy in various riverine habitats.A deterministic model, written in Excel spreadsheets and managed by Visual Basic for Applications, simulated the births, deaths and movement of tsetse confined to a strip of riverine vegetation composed of segments of habitat in which the tsetse population was either self-sustaining, or not sustainable unless supplemented by immigrants. Results suggested that in many situations the use of tiny targets at high density for just a few months per year would be the most cost-effective strategy for rapidly reducing tsetse densities by the ~90% expected to have a great impact on the incidence of sleeping sickness. Local elimination of tsetse becomes feasible when targets are deployed in isolated situations, or where the only invasion occurs from populations that are not self-sustaining.Seasonal use of tiny targets deserves field trials. The ability to recognise habitat that contains tsetse populations which are not self-sustaining could improve the planning of all methods of tsetse control, against any species, in riverine, savannah or forest situations. Criteria to assist such recognition are suggested.
Optimal control of HIV/AIDS dynamic: Education and treatment
Sule, Amiru; Abdullah, Farah Aini
2014-07-01
A mathematical model which describes the transmission dynamics of HIV/AIDS is developed. The optimal control representing education and treatment for this model is explored. The existence of optimal Control is established analytically by the use of optimal control theory. Numerical simulations suggest that education and treatment for the infected has a positive impact on HIV/AIDS control.
Automated beam steering using optimal control
Energy Technology Data Exchange (ETDEWEB)
Allen, C. K. (Christopher K.)
2004-01-01
We present a steering algorithm which, with the aid of a model, allows the user to specify beam behavior throughout a beamline, rather than just at specified beam position monitor (BPM) locations. The model is used primarily to compute the values of the beam phase vectors from BPM measurements, and to define cost functions that describe the steering objectives. The steering problem is formulated as constrained optimization problem; however, by applying optimal control theory we can reduce it to an unconstrained optimization whose dimension is the number of control signals.
Symposium on Optimal Control Theory
1987-01-01
Control theory can be roughly classified as deterministic or stochastic. Each of these can further be subdivided into game theory and optimal control theory. The central problem of control theory is the so called constrained maximization (which- with slight modifications--is equivalent to minimization). One can then say, heuristically, that the major problem of control theory is to find the maximum of some performance criterion (or criteria), given a set of constraints. The starting point is, of course, a mathematical representation of the performance criterion (or criteria)- sometimes called the objective functional--along with the constraints. When the objective functional is single valued (Le. , when there is only one objective to be maximized), then one is dealing with optimal control theory. When more than one objective is involved, and the objectives are generally incompatible, then one is dealing with game theory. The first paper deals with stochastic optimal control, using the dynamic programming ...
Optimal Control and Optimization of Stochastic Supply Chain Systems
Song, Dong-Ping
2013-01-01
Optimal Control and Optimization of Stochastic Supply Chain Systems examines its subject in the context of the presence of a variety of uncertainties. Numerous examples with intuitive illustrations and tables are provided, to demonstrate the structural characteristics of the optimal control policies in various stochastic supply chains and to show how to make use of these characteristics to construct easy-to-operate sub-optimal policies. In Part I, a general introduction to stochastic supply chain systems is provided. Analytical models for various stochastic supply chain systems are formulated and analysed in Part II. In Part III the structural knowledge of the optimal control policies obtained in Part II is utilized to construct easy-to-operate sub-optimal control policies for various stochastic supply chain systems accordingly. Finally, Part IV discusses the optimisation of threshold-type control policies and their robustness. A key feature of the book is its tying together of ...
International Nuclear Information System (INIS)
Wu, Yuhu; Kumar, Madan; Shen, Tielong
2016-01-01
Highlights: • An in-cylinder pressure based measuring method for the RGF is derived. • A stochastic logical dynamical model is proposed to represent the transient behavior of the RGF. • The receding horizon controller is designed to reduce the variance of the RGF. • The effectiveness of the proposed model and control approach is validated by the experimental evidence. - Abstract: In four stroke internal combustion engines, residual gas from the previous cycle is an important factor influencing the combustion quality of the current cycle, and the residual gas fraction (RGF) is a popular index to monitor the influence of residual gas. This paper investigates the cycle-to-cycle transient behavior of the RGF in the view of systems theory and proposes a multi-valued logic-based control strategy for attenuation of RGF fluctuation. First, an in-cylinder pressure sensor-based method for measuring the RGF is provided by following the physics of the in-cylinder transient state of four-stroke internal combustion engines. Then, the stochastic property of the RGF is examined based on statistical data obtained by conducting experiments on a full-scale gasoline engine test bench. Based on the observation of the examination, a stochastic logical transient model is proposed to represent the cycle-to-cycle transient behavior of the RGF, and with the model an optimal feedback control law, which targets on rejection of the RGF fluctuation, is derived in the framework of stochastic logical system theory. Finally, experimental results are demonstrated to show the effectiveness of the proposed model and the control strategy.
Optimal control theory an introduction
Kirk, Donald E
2004-01-01
Optimal control theory is the science of maximizing the returns from and minimizing the costs of the operation of physical, social, and economic processes. Geared toward upper-level undergraduates, this text introduces three aspects of optimal control theory: dynamic programming, Pontryagin's minimum principle, and numerical techniques for trajectory optimization.Chapters 1 and 2 focus on describing systems and evaluating their performances. Chapter 3 deals with dynamic programming. The calculus of variations and Pontryagin's minimum principle are the subjects of chapters 4 and 5, and chapter
Adaptive Optimizing Nonlinear Control Design for an Over-actuated Aircraft Model
Van Oort, E.R.; Sonneveldt, L.; Chu, Q.P.; Mulder, J.A.
2011-01-01
In this paper nonlinear adaptive flight control laws based on the backstepping approach are proposed which are applicable to over-actuated nonlinear systems. Instead of solving the control allocation exactly, update laws for the desired control effector signals are defined such that they converge to
Reniers, Jorn M.; Mulder, Grietus; Ober-Blöbaum, Sina; Howey, David A.
2018-03-01
The increased deployment of intermittent renewable energy generators opens up opportunities for grid-connected energy storage. Batteries offer significant flexibility but are relatively expensive at present. Battery lifetime is a key factor in the business case, and it depends on usage, but most techno-economic analyses do not account for this. For the first time, this paper quantifies the annual benefits of grid-connected batteries including realistic physical dynamics and nonlinear electrochemical degradation. Three lithium-ion battery models of increasing realism are formulated, and the predicted degradation of each is compared with a large-scale experimental degradation data set (Mat4Bat). A respective improvement in RMS capacity prediction error from 11% to 5% is found by increasing the model accuracy. The three models are then used within an optimal control algorithm to perform price arbitrage over one year, including degradation. Results show that the revenue can be increased substantially while degradation can be reduced by using more realistic models. The estimated best case profit using a sophisticated model is a 175% improvement compared with the simplest model. This illustrates that using a simplistic battery model in a techno-economic assessment of grid-connected batteries might substantially underestimate the business case and lead to erroneous conclusions.
Optimal control of anthracnose using mixed strategies.
Fotsa Mbogne, David Jaures; Thron, Christopher
2015-11-01
In this paper we propose and study a spatial diffusion model for the control of anthracnose disease in a bounded domain. The model is a generalization of the one previously developed in [15]. We use the model to simulate two different types of control strategies against anthracnose disease. Strategies that employ chemical fungicides are modeled using a continuous control function; while strategies that rely on cultivational practices (such as pruning and removal of mummified fruits) are modeled with a control function which is discrete in time (though not in space). For comparative purposes, we perform our analyses for a spatially-averaged model as well as the space-dependent diffusion model. Under weak smoothness conditions on parameters we demonstrate the well-posedness of both models by verifying existence and uniqueness of the solution for the growth inhibition rate for given initial conditions. We also show that the set [0, 1] is positively invariant. We first study control by impulsive strategies, then analyze the simultaneous use of mixed continuous and pulse strategies. In each case we specify a cost functional to be minimized, and we demonstrate the existence of optimal control strategies. In the case of pulse-only strategies, we provide explicit algorithms for finding the optimal control strategies for both the spatially-averaged model and the space-dependent model. We verify the algorithms for both models via simulation, and discuss properties of the optimal solutions. Copyright © 2015 Elsevier Inc. All rights reserved.
Dynamic optimization and adaptive controller design
Inamdar, S. R.
2010-10-01
In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.
Madsen, Jonas S; Lin, Yu-Cheng; Squyres, Georgia R; Price-Whelan, Alexa; de Santiago Torio, Ana; Song, Angela; Cornell, William C; Sørensen, Søren J; Xavier, Joao B; Dietrich, Lars E P
2015-12-01
As biofilms grow, resident cells inevitably face the challenge of resource limitation. In the opportunistic pathogen Pseudomonas aeruginosa PA14, electron acceptor availability affects matrix production and, as a result, biofilm morphogenesis. The secreted matrix polysaccharide Pel is required for pellicle formation and for colony wrinkling, two activities that promote access to O2. We examined the exploitability and evolvability of Pel production at the air-liquid interface (during pellicle formation) and on solid surfaces (during colony formation). Although Pel contributes to the developmental response to electron acceptor limitation in both biofilm formation regimes, we found variation in the exploitability of its production and necessity for competitive fitness between the two systems. The wild type showed a competitive advantage against a non-Pel-producing mutant in pellicles but no advantage in colonies. Adaptation to the pellicle environment selected for mutants with a competitive advantage against the wild type in pellicles but also caused a severe disadvantage in colonies, even in wrinkled colony centers. Evolution in the colony center produced divergent phenotypes, while adaptation to the colony edge produced mutants with clear competitive advantages against the wild type in this O2-replete niche. In general, the structurally heterogeneous colony environment promoted more diversification than the more homogeneous pellicle. These results suggest that the role of Pel in community structure formation in response to electron acceptor limitation is unique to specific biofilm models and that the facultative control of Pel production is required for PA14 to maintain optimum benefit in different types of communities. Copyright © 2015, American Society for Microbiology. All Rights Reserved.
Directory of Open Access Journals (Sweden)
Sandip Roy
2011-10-01
Full Text Available BACKGROUND: Developing control policies for zoonotic diseases is challenging, both because of the complex spread dynamics exhibited by these diseases, and because of the need for implementing complex multi-species surveillance and control efforts using limited resources. Mathematical models, and in particular network models, of disease spread are promising as tools for control-policy design, because they can provide comprehensive quantitative representations of disease transmission. METHODOLOGY/PRINCIPAL FINDINGS: A layered dynamical network model for the transmission and control of zoonotic diseases is introduced as a tool for analyzing disease spread and designing cost-effective surveillance and control. The model development is achieved using brucellosis transmission among wildlife, cattle herds, and human sub-populations in an agricultural system as a case study. Precisely, a model that tracks infection counts in interacting animal herds of multiple species (e.g., cattle herds and groups of wildlife for brucellosis and in human subpopulations is introduced. The model is then abstracted to a form that permits comprehensive targeted design of multiple control capabilities as well as model identification from data. Next, techniques are developed for such quantitative design of control policies (that are directed to both the animal and human populations, and for model identification from snapshot and time-course data, by drawing on recent results in the network control community. CONCLUSIONS/SIGNIFICANCE: The modeling approach is shown to provide quantitative insight into comprehensive control policies for zoonotic diseases, and in turn to permit policy design for mitigation of these diseases. For the brucellosis-transmission example in particular, numerous insights are obtained regarding the optimal distribution of resources among available control capabilities (e.g., vaccination, surveillance and culling, pasteurization of milk and points in
Optimal Control Development System for Electrical Drives
Directory of Open Access Journals (Sweden)
Marian GAICEANU
2008-08-01
Full Text Available In this paper the optimal electrical drive development system is presented. It consists of both electrical drive types: DC and AC. In order to implement the optimal control for AC drive system an Altivar 71 inverter, a Frato magnetic particle brake (as load, three-phase induction machine, and dSpace 1104 controller have been used. The on-line solution of the matrix Riccati differential equation (MRDE is computed by dSpace 1104 controller, based on the corresponding feedback signals, generating the optimal speed reference for the AC drive system. The optimal speed reference is tracked by Altivar 71 inverter, conducting to energy reduction in AC drive. The classical control (consisting of rotor field oriented control with PI controllers and the optimal one have been implemented by designing an adequate ControlDesk interface. The three-phase induction machine (IM is controlled at constant flux. Therefore, the linear dynamic mathematical model of the IM has been obtained. The optimal control law provides transient regimes with minimal energy consumption. The obtained solution by integration of the MRDE is orientated towards the numerical implementation-by using a zero order hold. The development system is very useful for researchers, doctoral students or experts training in electrical drive. The experimental results are shown.
Economic COP Optimization of a Heat Pump with Hierarchical Model Predictive Control
DEFF Research Database (Denmark)
Tahersima, Fatemeh; Stoustrup, Jakob; Rasmussen, Henrik
2012-01-01
A low-temperature heating system is studied in this paper. It consists of hydronic under-floor heating pipes and an air/ground source heat pump. The heat pump in such a setup is conventionally controlled only by feed-forwarding the ambient temperature. Having shown >10% cut-down on electricity bi....... The proposed control strategy is a leap forward towards balanced load control in Smart Grids where individual heat pumps in detached houses contribute to preserve load balance through intelligent electricity pricing policies....
Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions
National Aeronautics and Space Administration — A contrived example of a dice throwing game was considered in order to provide some insight into the general problem developing prognostics-based control routines...
Agent-Based Models and Optimal Control in Biology: A Discrete Approach
2012-01-01
alive. Thus, the rules are reminiscent of a population whose survival is affected by under- and overpopulation . If we now initialize this “Game” by...helpful in models consisting of many agents of the same type, or many agents that follow the same set of rules. In modeling seasonal animal migration
A simplified modeling of mechanical cooling tower for control and optimization of HVAC systems
International Nuclear Information System (INIS)
Jin, Guang-Yu; Cai, Wen-Jian; Lu Lu; Lee, Eng Lock; Chiang, Andrew
2007-01-01
This paper proposes a new, simple, yet accurate mechanical cooling tower model for the purpose of energy conservation and management. On the basis of Merkel's theory and effectiveness-NTU method, the model is developed by energy balance and heat, mass transfer analysis. Commissioning information is then used to identified, only three model parameters by Levenberg-Marquardt method. Compared with the existing models, the proposed model has simple characteristic parameters to be determined and without requiring iterative computation when the operating point changes. The model is validated by real operating data from the cooling towers of a heating, ventilating and air conditioning (HVAC) system of a commercial hotel. The testing results show that the performance of the cooling tower varies from time to time due to different operating conditions and the proposed model is able to reflect these changes by tuning its parameters. With this feature, the proposed model can be simply used and accurately predict the performance of the real-time operating cooling tower
Optimal control for Malaria disease through vaccination
Munzir, Said; Nasir, Muhammad; Ramli, Marwan
2018-01-01
Malaria is a disease caused by an amoeba (single-celled animal) type of plasmodium where anopheles mosquito serves as the carrier. This study examines the optimal control problem of malaria disease spread based on Aron and May (1982) SIR type models and seeks the optimal solution by minimizing the prevention of the spreading of malaria by vaccine. The aim is to investigate optimal control strategies on preventing the spread of malaria by vaccination. The problem in this research is solved using analytical approach. The analytical method uses the Pontryagin Minimum Principle with the symbolic help of MATLAB software to obtain optimal control result and to analyse the spread of malaria with vaccination control.
Optimal control problem for the extended Fisher–Kolmogorov equation
Indian Academy of Sciences (India)
by methods of optimal control, such as chemical engineering and vehicle ... ern optimal control theories and applied models are not only represented by .... Obviously, equation (2.5) is an ordinary differential equation and according to ODE.
Lee, Wen-Chuan; Wu, Jong-Wuu; Tsou, Hsin-Hui; Lei, Chia-Ling
2012-10-01
This article considers that the number of defective units in an arrival order is a binominal random variable. We derive a modified mixture inventory model with backorders and lost sales, in which the order quantity and lead time are decision variables. In our studies, we also assume that the backorder rate is dependent on the length of lead time through the amount of shortages and let the backorder rate be a control variable. In addition, we assume that the lead time demand follows a mixture of normal distributions, and then relax the assumption about the form of the mixture of distribution functions of the lead time demand and apply the minimax distribution free procedure to solve the problem. Furthermore, we develop an algorithm procedure to obtain the optimal ordering strategy for each case. Finally, three numerical examples are also given to illustrate the results.
Chelcy R. Ford; Barbara C. Reynolds; James Vose
2010-01-01
The hemlock woolly adelgid (HWA, Adelges tsugae Annand) is causing widespread decline and mortality of eastern hemlock trees (Tsuga Canadensis (L.) Carr.). Stem injection of insecticide is widely used as a control measure, but its effectiveness depends on individual tree hydraulic characteristics. Recent work has shown that eastern...
An Optimization of Manufacturing Systems using a Feedback Control Scheduling Model
Ikome, John M.; Kanakana, Grace M.
2018-03-01
In complex production system that involves multiple process, unplanned disruption often turn to make the entire production system vulnerable to a number of problems which leads to customer’s dissatisfaction. However, this problem has been an ongoing problem that requires a research and methods to streamline the entire process or develop a model that will address it, in contrast to this, we have developed a feedback scheduling model that can minimize some of this problem and after a number of experiment, it shows that some of this problems can be eliminated if the correct remedial actions are implemented on time.
Optimal control linear quadratic methods
Anderson, Brian D O
2007-01-01
This augmented edition of a respected text teaches the reader how to use linear quadratic Gaussian methods effectively for the design of control systems. It explores linear optimal control theory from an engineering viewpoint, with step-by-step explanations that show clearly how to make practical use of the material.The three-part treatment begins with the basic theory of the linear regulator/tracker for time-invariant and time-varying systems. The Hamilton-Jacobi equation is introduced using the Principle of Optimality, and the infinite-time problem is considered. The second part outlines the
Chemical optimization algorithm for fuzzy controller design
Astudillo, Leslie; Castillo, Oscar
2014-01-01
In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application
Optimal control of motorsport differentials
Tremlett, A. J.; Massaro, M.; Purdy, D. J.; Velenis, E.; Assadian, F.; Moore, A. P.; Halley, M.
2015-12-01
Modern motorsport limited slip differentials (LSD) have evolved to become highly adjustable, allowing the torque bias that they generate to be tuned in the corner entry, apex and corner exit phases of typical on-track manoeuvres. The task of finding the optimal torque bias profile under such varied vehicle conditions is complex. This paper presents a nonlinear optimal control method which is used to find the minimum time optimal torque bias profile through a lane change manoeuvre. The results are compared to traditional open and fully locked differential strategies, in addition to considering related vehicle stability and agility metrics. An investigation into how the optimal torque bias profile changes with reduced track-tyre friction is also included in the analysis. The optimal LSD profile was shown to give a performance gain over its locked differential counterpart in key areas of the manoeuvre where a quick direction change is required. The methodology proposed can be used to find both optimal passive LSD characteristics and as the basis of a semi-active LSD control algorithm.
Optimal control of native predators
Martin, Julien; O'Connell, Allan F.; Kendall, William L.; Runge, Michael C.; Simons, Theodore R.; Waldstein, Arielle H.; Schulte, Shiloh A.; Converse, Sarah J.; Smith, Graham W.; Pinion, Timothy; Rikard, Michael; Zipkin, Elise F.
2010-01-01
We apply decision theory in a structured decision-making framework to evaluate how control of raccoons (Procyon lotor), a native predator, can promote the conservation of a declining population of American Oystercatchers (Haematopus palliatus) on the Outer Banks of North Carolina. Our management objective was to maintain Oystercatcher productivity above a level deemed necessary for population recovery while minimizing raccoon removal. We evaluated several scenarios including no raccoon removal, and applied an adaptive optimization algorithm to account for parameter uncertainty. We show how adaptive optimization can be used to account for uncertainties about how raccoon control may affect Oystercatcher productivity. Adaptive management can reduce this type of uncertainty and is particularly well suited for addressing controversial management issues such as native predator control. The case study also offers several insights that may be relevant to the optimal control of other native predators. First, we found that stage-specific removal policies (e.g., yearling versus adult raccoon removals) were most efficient if the reproductive values among stage classes were very different. Second, we found that the optimal control of raccoons would result in higher Oystercatcher productivity than the minimum levels recommended for this species. Third, we found that removing more raccoons initially minimized the total number of removals necessary to meet long term management objectives. Finally, if for logistical reasons managers cannot sustain a removal program by removing a minimum number of raccoons annually, managers may run the risk of creating an ecological trap for Oystercatchers.
Optimal control novel directions and applications
Aronna, Maria; Kalise, Dante
2017-01-01
Focusing on applications to science and engineering, this book presents the results of the ITN-FP7 SADCO network’s innovative research in optimization and control in the following interconnected topics: optimality conditions in optimal control, dynamic programming approaches to optimal feedback synthesis and reachability analysis, and computational developments in model predictive control. The novelty of the book resides in the fact that it has been developed by early career researchers, providing a good balance between clarity and scientific rigor. Each chapter features an introduction addressed to PhD students and some original contributions aimed at specialist researchers. Requiring only a graduate mathematical background, the book is self-contained. It will be of particular interest to graduate and advanced undergraduate students, industrial practitioners and to senior scientists wishing to update their knowledge.
Modelling and study of the optimal control of a pressurised nuclear reactor
International Nuclear Information System (INIS)
Hurault, Martine
1974-01-01
This research thesis reports the development of an analog simulation of a nuclear reactor. For this purpose, the number of equations had to be limited for technological reasons (size and capacity of the analog console). The author first presents the nuclear reactor equations, and the simulation equations, and reports the study of the simulation on an analog computer. Then, he presents a more restrained model which has been used to elaborate the command: kinetic and thermal equations, general equations, study on an analog computer. In the next part, the author presents the reactor steering system: algorithm, practical resolution, partial derivative method. He finally reports the selection of a realistic criterion and the development of a digital steering [fr
The Optimization of power reactor control system
International Nuclear Information System (INIS)
Danupoyo, S.D.
1997-01-01
A power reactor is an important part in nuclear powered electrical plant systems. Success in controlling the power reactor will establish safety of the whole power plant systems. Until now, the power reactor has been controlled by a classical control system that was designed based on output feedback method. To meet the safety requirements that are now more restricted, the recently used power reactor control system should be modified. this paper describes a power reactor control system that is designed based on a state feedback method optimized with LQG (Linear-quadrature-gaussian) method and equipped with a state estimator. A pressurized-water type reactor has been used as the model. by using a point kinetics method with one group delayed neutrons. the result of simulation testing shows that the optimized control system can control the power reactor more effective and efficient than the classical control system
Energy Technology Data Exchange (ETDEWEB)
Iov, F.; Timbus, A.V.; Hansen, A.D.; Soerensen, P.; Blaabjerg, F.
2004-03-01
This report is a general overview of the results obtained in the project 'Electrical Design and Control. Simulation Platform to Model, Optimize and Design Wind Turbines'. The motivation for this research project is the ever-increasing wind energy penetration into the power network. Therefore, the project has the main goal to create a model database in different simulation tools for a system optimization of the wind turbine systems. Using this model database a simultaneous optimization of the aerodynamic, mechanical, electrical and control systems over the whole range of wind speeds and grid characteristics can be achieved. The report is structured in six chapters. First, the background of this project and the main goals as well as the structure of the simulation platform is given. The main topologies for wind turbines, which have been taken into account during the project, are briefly presented. Then, the considered simulation tools narrely: HAWC, DIgSILENT, Saber and Matlab/Simulink have been used in this simulation platform are described. The focus here is on the modelling and simulation time scale aspects. The abilities of these tools are complementary and they can together cover all the modelling aspects of the wind turbines e.g. mechanical loads, power quality, switching, control and grid faults. However, other simulation packages e.g PSCAD/EMTDC can easily be added in the simulation platform. New models and new Control algorithms for wind turbine systems have been developed and tested in these tools. All these models are collected in dedicated libraries in Matlab/Simulink as well as in Saber. Some simulation results from the considered tools are presented for MW wind turbines. These simulation results focuses on fixed-speed and variable speed/pitch wind turbines. A good agreement with the real behaviour of these systems is obtained for each simulation tool. These models can easily be extended to model different kinds of wind turbines or large wind
Centralized Stochastic Optimal Control of Complex Systems
Energy Technology Data Exchange (ETDEWEB)
Malikopoulos, Andreas [ORNL
2015-01-01
In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain using the long-run expected average cost per unit time criterion, and we show that the control policy yielding the Pareto optimal solution minimizes the average cost criterion online. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion.
Optimal control with aerospace applications
Longuski, James M; Prussing, John E
2014-01-01
Want to know not just what makes rockets go up but how to do it optimally? Optimal control theory has become such an important field in aerospace engineering that no graduate student or practicing engineer can afford to be without a working knowledge of it. This is the first book that begins from scratch to teach the reader the basic principles of the calculus of variations, develop the necessary conditions step-by-step, and introduce the elementary computational techniques of optimal control. This book, with problems and an online solution manual, provides the graduate-level reader with enough introductory knowledge so that he or she can not only read the literature and study the next level textbook but can also apply the theory to find optimal solutions in practice. No more is needed than the usual background of an undergraduate engineering, science, or mathematics program: namely calculus, differential equations, and numerical integration. Although finding optimal solutions for these problems is a...
Optimization-Based Approaches to Control of Probabilistic Boolean Networks
Directory of Open Access Journals (Sweden)
Koichi Kobayashi
2017-02-01
Full Text Available Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs, which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.
Following an Optimal Batch Bioreactor Operations Model
DEFF Research Database (Denmark)
Ibarra-Junquera, V.; Jørgensen, Sten Bay; Virgen-Ortíz, J.J.
2012-01-01
The problem of following an optimal batch operation model for a bioreactor in the presence of uncertainties is studied. The optimal batch bioreactor operation model (OBBOM) refers to the bioreactor trajectory for nominal cultivation to be optimal. A multiple-variable dynamic optimization of fed...... as the master system which includes the optimal cultivation trajectory for the feed flow rate and the substrate concentration. The “real” bioreactor, the one with unknown dynamics and perturbations, is considered as the slave system. Finally, the controller is designed such that the real bioreactor...
Optimization and optimal control in automotive systems
Kolmanovsky, Ilya; Steinbuch, Maarten; Re, Luigi
2014-01-01
This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier approaches, based on some degree of heuristics, to the use of more and more common systematic methods. Even systematic methods can be developed and applied in a large number of forms so the text collects contributions from across the theory, methods and real-world automotive applications of optimization. Greater fuel economy, significant reductions in permissible emissions, new drivability requirements and the generally increasing complexity of automotive systems are among the criteria that the contributing authors set themselves to meet. In many cases multiple and often conflicting requirements give rise to multi-objective constrained optimization problems which are also considered. Some of these problems fall into the domain of the traditional multi-disciplinary optimization applie...
Nguyen, Gia Luong Huu
obtained experimental data, the research studied the control of airflow to regulate the temperature of reactors within the fuel processor. The dynamic model provided a platform to test the dynamic response for different control gains. With sufficient sensing and appropriate control, a rapid response to maintain the temperature of the reactor despite an increase in power was possible. The third part of the research studied the use of a fuel cell in conjunction with photovoltaic panels, and energy storage to provide electricity for buildings. This research developed an optimization framework to determine the size of each device in the hybrid energy system to satisfy the electrical demands of buildings and yield the lowest cost. The advantage of having the fuel cell with photovoltaic and energy storage was the ability to operate the fuel cell at baseload at night, thus reducing the need for large battery systems to shift the solar power produced in the day to the night. In addition, the dispatchability of the fuel cell provided an extra degree of freedom necessary for unforeseen disturbances. An operation framework based on model predictive control showed that the method is suitable for optimizing the dispatch of the hybrid energy system.
Control and optimal control theories with applications
Burghes, D N
2004-01-01
This sound introduction to classical and modern control theory concentrates on fundamental concepts. Employing the minimum of mathematical elaboration, it investigates the many applications of control theory to varied and important present-day problems, e.g. economic growth, resource depletion, disease epidemics, exploited population, and rocket trajectories. An original feature is the amount of space devoted to the important and fascinating subject of optimal control. The work is divided into two parts. Part one deals with the control of linear time-continuous systems, using both transfer fun
A Feedback Optimal Control Algorithm with Optimal Measurement Time Points
Directory of Open Access Journals (Sweden)
Felix Jost
2017-02-01
Full Text Available Nonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To reduce the uncertainty it has also been suggested to include optimal experimental design into the sequential process of estimation and control calculation. Most of the focus so far was on dual control approaches, i.e., on using the controls to simultaneously excite the system dynamics (learning as well as minimizing a given objective (performing. We propose a new algorithm, which sequentially solves robust optimal control, optimal experimental design, state and parameter estimation problems. Thus, we decouple the control and the experimental design problems. This has the advantages that we can analyze the impact of measurement timing (sampling independently, and is practically relevant for applications with either an ethical limitation on system excitation (e.g., chemotherapy treatment or the need for fast feedback. The algorithm shows promising results with a 36% reduction of parameter uncertainties for the Lotka-Volterra fishing benchmark example.
Directory of Open Access Journals (Sweden)
Meng Xiong
2015-08-01
Full Text Available Energy storage devices are expected to be more frequently implemented in wind farms in near future. In this paper, both pumped hydro and fly wheel storage systems are used to assist a wind farm to smooth the power fluctuations. Due to the significant difference in the response speeds of the two storages types, the wind farm coordination with two types of energy storage is a problem. This paper presents two methods for the coordination problem: a two-level hierarchical model predictive control (MPC method and a single-level MPC method. In the single-level MPC method, only one MPC controller coordinates the wind farm and the two storage systems to follow the grid scheduling. Alternatively, in the two-level MPC method, two MPC controllers are used to coordinate the wind farm and the two storage systems. The structure of two level MPC consists of outer level and inner level MPC. They run alternatively to perform real-time scheduling and then stop, thus obtaining long-term scheduling results and sending some results to the inner level as input. The single-level MPC method performs both long- and short-term scheduling tasks in each interval. The simulation results show that the methods proposed can improve the utilization of wind power and reduce wind power spillage. In addition, the single-level MPC and the two-level MPC are not interchangeable. The single-level MPC has the advantage of following the grid schedule while the two-level MPC can reduce the optimization time by 60%.
Optimization and Optimal Control in Automotive Systems
Waschl, H.; Kolmanovsky, I.V.; Steinbuch, M.; Re, del L.
2014-01-01
This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier approaches, based on some degree of heuristics, to the use of more and
Directory of Open Access Journals (Sweden)
Yebai Qi
2017-12-01
Full Text Available Unlike some thermostatically controlled appliances (TCAs with small capacities, Central Air-conditioner (CAC has huge potential for demand response because of its large capacity. This paper presents a new CAC control strategy under multiple constraints. The CAC is modeled by three main modules: CAC central unit, water pumps, and temperature simulation of terminal users. The CAC’s power consumption is mainly determined by users’ load ratio. As the information and communication system have become the central nervous system of the smart grid, big data analysis is of great significance. Assuming that reliable two-way communication systems are preset, an integrated parameter priority list (IPPL control strategy is used to control and monitor CAC. A new intelligent algorithm, Space Exploration and Unimodal Region Elimination (SEUMRE algorithm, is introduced for solving the optimization problem of demand response targets generation under multiple constraints with the help of big data analysis. In this paper, influences and constrain factors, such as price and users’ comfortable levels are taken into account to satisfy the need of actual situation. Simulation results show that the proposed approach, when comparing with other typical optimization algorithms, yields better performances and efficiency.
Optimal, real-time control--colliders
International Nuclear Information System (INIS)
Spencer, J.E.
1991-05-01
With reasonable definitions, optimal control is possible for both classical and quantal systems with new approaches called PISC(Parallel) and NISC(Neural) from analogy with RISC (Reduced Instruction Set Computing). If control equals interaction, observation and comparison to some figure of merit with interaction via external fields, then optimization comes from varying these fields to give design or operating goals. Structural stability can then give us tolerance and design constraints. But simulations use simplified models, are not in real-time and assume fixed or stationary conditions, so optimal control goes far beyond convergence rates of algorithms. It is inseparable from design and this has many implications for colliders. 12 refs., 3 figs
Directory of Open Access Journals (Sweden)
Junqiu Liu
2018-04-01
Full Text Available In order to mitigate environmental and ecological impacts resulting from groundwater overexploitation, we developed a multiple-iterated dual control model consisting of four modules for groundwater exploitation and water level. First, a water resources allocation model integrating calculation module of groundwater allowable withdrawal was built to predict future groundwater recharge and discharge. Then, the results were input into groundwater numerical model to simulate water levels. Groundwater exploitation was continuously optimized using the critical groundwater level as the feedback, and a groundwater multiple-iterated technique was applied to the feedback process. The proposed model was successfully applied to a typical region in Shenyang in northeast China. Results showed the groundwater numerical model was verified in simulating water levels, with a mean absolute error of 0.44 m, an average relative error of 1.33%, and a root-mean-square error of 0.46 m. The groundwater exploitation reduced from 290.33 million m3 to 116.76 million m3 and the average water level recovered from 34.27 m to 34.72 m in planning year. Finally, we proposed the strategies for water resources management in which the water levels should be controlled within the critical groundwater level. The developed model provides a promising approach for water resources allocation and sustainable groundwater management, especially for those regions with overexploited groundwater.
Defending against the Advanced Persistent Threat: An Optimal Control Approach
Directory of Open Access Journals (Sweden)
Pengdeng Li
2018-01-01
Full Text Available The new cyberattack pattern of advanced persistent threat (APT has posed a serious threat to modern society. This paper addresses the APT defense problem, that is, the problem of how to effectively defend against an APT campaign. Based on a novel APT attack-defense model, the effectiveness of an APT defense strategy is quantified. Thereby, the APT defense problem is modeled as an optimal control problem, in which an optimal control stands for a most effective APT defense strategy. The existence of an optimal control is proved, and an optimality system is derived. Consequently, an optimal control can be figured out by solving the optimality system. Some examples of the optimal control are given. Finally, the influence of some factors on the effectiveness of an optimal control is examined through computer experiments. These findings help organizations to work out policies of defending against APTs.
Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints
Kmet', Tibor; Kmet'ová, Mária
2009-09-01
A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.
Optimal Investment Control of Macroeconomic Systems
Institute of Scientific and Technical Information of China (English)
ZHAO Ke-jie; LIU Chuan-zhe
2006-01-01
Economic growth is always accompanied by economic fluctuation. The target of macroeconomic control is to keep a basic balance of economic growth, accelerate the optimization of economic structures and to lead a rapid, sustainable and healthy development of national economies, in order to propel society forward. In order to realize the above goal, investment control must be regarded as the most important policy for economic stability. Readjustment and control of investment includes not only control of aggregate investment, but also structural control which depends on economic-technology relationships between various industries of a national economy. On the basis of the theory of a generalized system, an optimal investment control model for government has been developed. In order to provide a scientific basis for government to formulate a macroeconomic control policy, the model investigates the balance of total supply and aggregate demand through an adjustment in investment decisions realizes a sustainable and stable growth of the national economy. The optimal investment decision function proposed by this study has a unique and specific expression, high regulating precision and computable characteristics.
Risk modelling in portfolio optimization
Lam, W. H.; Jaaman, Saiful Hafizah Hj.; Isa, Zaidi
2013-09-01
Risk management is very important in portfolio optimization. The mean-variance model has been used in portfolio optimization to minimize the investment risk. The objective of the mean-variance model is to minimize the portfolio risk and achieve the target rate of return. Variance is used as risk measure in the mean-variance model. The purpose of this study is to compare the portfolio composition as well as performance between the optimal portfolio of mean-variance model and equally weighted portfolio. Equally weighted portfolio means the proportions that are invested in each asset are equal. The results show that the portfolio composition of the mean-variance optimal portfolio and equally weighted portfolio are different. Besides that, the mean-variance optimal portfolio gives better performance because it gives higher performance ratio than the equally weighted portfolio.
Model Risk in Portfolio Optimization
Directory of Open Access Journals (Sweden)
David Stefanovits
2014-08-01
Full Text Available We consider a one-period portfolio optimization problem under model uncertainty. For this purpose, we introduce a measure of model risk. We derive analytical results for this measure of model risk in the mean-variance problem assuming we have observations drawn from a normal variance mixture model. This model allows for heavy tails, tail dependence and leptokurtosis of marginals. The results show that mean-variance optimization is seriously compromised by model uncertainty, in particular, for non-Gaussian data and small sample sizes. To mitigate these shortcomings, we propose a method to adjust the sample covariance matrix in order to reduce model risk.
HCCI Engine Optimization and Control
Energy Technology Data Exchange (ETDEWEB)
Rolf D. Reitz
2005-09-30
The goal of this project was to develop methods to optimize and control Homogeneous-Charge Compression Ignition (HCCI) engines, with emphasis on diesel-fueled engines. HCCI offers the potential of nearly eliminating IC engine NOx and particulate emissions at reduced cost over Compression Ignition Direct Injection engines (CIDI) by controlling pollutant emissions in-cylinder. The project was initiated in January, 2002, and the present report is the final report for work conducted on the project through December 31, 2004. Periodic progress has also been reported at bi-annual working group meetings held at USCAR, Detroit, MI, and at the Sandia National Laboratories. Copies of these presentation materials are available on CD-ROM, as distributed by the Sandia National Labs. In addition, progress has been documented in DOE Advanced Combustion Engine R&D Annual Progress Reports for FY 2002, 2003 and 2004. These reports are included as the Appendices in this Final report.
Reference-shaping adaptive control by using gradient descent optimizers.
Directory of Open Access Journals (Sweden)
Baris Baykant Alagoz
Full Text Available This study presents a model reference adaptive control scheme based on reference-shaping approach. The proposed adaptive control structure includes two optimizer processes that perform gradient descent optimization. The first process is the control optimizer that generates appropriate control signal for tracking of the controlled system output to a reference model output. The second process is the adaptation optimizer that performs for estimation of a time-varying adaptation gain, and it contributes to improvement of control signal generation. Numerical update equations derived for adaptation gain and control signal perform gradient descent optimization in order to decrease the model mismatch errors. To reduce noise sensitivity of the system, a dead zone rule is applied to the adaptation process. Simulation examples show the performance of the proposed Reference-Shaping Adaptive Control (RSAC method for several test scenarios. An experimental study demonstrates application of method for rotor control.
Hogiri, Tomoharu; Tamashima, Hiroshi; Nishizawa, Akitoshi; Okamoto, Masahiro
2018-02-01
To optimize monoclonal antibody (mAb) production in Chinese hamster ovary cell cultures, culture pH should be temporally controlled with high resolution. In this study, we propose a new pH-dependent dynamic model represented by simultaneous differential equations including a minimum of six system component, depending on pH value. All kinetic parameters in the dynamic model were estimated using an evolutionary numerical optimization (real-coded genetic algorithm) method based on experimental time-course data obtained at different pH values ranging from 6.6 to 7.2. We determined an optimal pH-shift schedule theoretically. We validated this optimal pH-shift schedule experimentally and mAb production increased by approximately 40% with this schedule. Throughout this study, it was suggested that the culture pH-shift optimization strategy using a pH-dependent dynamic model is suitable to optimize any pH-shift schedule for CHO cell lines used in mAb production projects. Copyright © 2017 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.
A study on an optimal movement model
Energy Technology Data Exchange (ETDEWEB)
Feng Jianfeng [COGS, Sussex University, Brighton BN1 9QH, UK (United Kingdom); Zhang, Kewei [SMS, Sussex University, Brighton BN1 9QH (United Kingdom); Luo Yousong [Department of Mathematics and Statistics, RMIT University, GOP Box 2476V, Melbourne, Vic 3001 (Australia)
2003-07-11
We present an analytical and rigorous study on a TOPS (task optimization in the presence of signal-dependent noise) model with a hold-on or an end-point control. Optimal control signals are rigorously obtained, which enables us to investigate various issues about the model including its trajectories, velocities, control signals, variances and the dependence of these quantities on various model parameters. With the hold-on control, we find that the optimal control can be implemented with an almost 'nil' hold-on period. The optimal control signal is a linear combination of two sub-control signals. One of the sub-control signals is positive and the other is negative. With the end-point control, the end-point variance is dramatically reduced, in comparison with the hold-on control. However, the velocity is not symmetric (bell shape). Finally, we point out that the velocity with a hold-on control takes the bell shape only within a limited parameter region.
Modeling, Analysis, and Optimization Issues for Large Space Structures
Pinson, L. D. (Compiler); Amos, A. K. (Compiler); Venkayya, V. B. (Compiler)
1983-01-01
Topics concerning the modeling, analysis, and optimization of large space structures are discussed including structure-control interaction, structural and structural dynamics modeling, thermal analysis, testing, and design.
Energy Technology Data Exchange (ETDEWEB)
Kortela, U.; Mononen, J.; Leppaekoski, K.; Hiltunen, J.; Jouppila, M.; Karppinen, R. [Oulu Univ. (Finland). Systems Engineering Lab.
1997-10-01
The aims of the project are to develop the combustion control strategies and to minimize the flue gas emissions. The common goal of the studies has been the reduction of flue gas emissions by using advanced control and optimization methods. The behaviour of different kind of boilers and fuels has been modelled using experimental data from fullscale plants, such as a 42 MW bubbling fluidized bed boiler, 23 MW bubbling fluidized bed boiler and a 300 MW circulating fluidized bed boiler. Many of the individual observations and modelled correlations between control variables and flue gas emissions have lead to operation instructions and/or re-organized control schemes which help to control total emissions. The most part of this knowledge can be formed to the standard IF- THEN - type rules which contain some uncertainty or fuzziness. Rule-based instruction system for the reduction of flue gas emissions is under work. (orig.)
Near optimal decentralized H_inf control
DEFF Research Database (Denmark)
Stoustrup, J.; Niemann, Hans Henrik
It is shown that foir a class of decentralized control problems there does not exist a sequence of controllers of bounded order which obtains near optimal control. Neither does there exist an infinity dimentional optimal controller. Using the insight of the line of proof of these results, a heuri......It is shown that foir a class of decentralized control problems there does not exist a sequence of controllers of bounded order which obtains near optimal control. Neither does there exist an infinity dimentional optimal controller. Using the insight of the line of proof of these results...
Hsia, Wei-Shen
1986-01-01
In the Control Systems Division of the Systems Dynamics Laboratory of the NASA/MSFC, a Ground Facility (GF), in which the dynamics and control system concepts being considered for Large Space Structures (LSS) applications can be verified, was designed and built. One of the important aspects of the GF is to design an analytical model which will be as close to experimental data as possible so that a feasible control law can be generated. Using Hyland's Maximum Entropy/Optimal Projection Approach, a procedure was developed in which the maximum entropy principle is used for stochastic modeling and the optimal projection technique is used for a reduced-order dynamic compensator design for a high-order plant.
Recent developments in cooperative control and optimization
Murphey, Robert; Pardalos, Panos
2004-01-01
Over the past several years, cooperative control and optimization has un questionably been established as one of the most important areas of research in the military sciences. Even so, cooperative control and optimization tran scends the military in its scope -having become quite relevant to a broad class of systems with many exciting, commercial, applications. One reason for all the excitement is that research has been so incredibly diverse -spanning many scientific and engineering disciplines. This latest volume in the Cooperative Systems book series clearly illustrates this trend towards diversity and creative thought. And no wonder, cooperative systems are among the hardest systems control science has endeavored to study, hence creative approaches to model ing, analysis, and synthesis are a must! The definition of cooperation itself is a slippery issue. As you will see in this and previous volumes, cooperation has been cast into many different roles and therefore has assumed many diverse meanings. P...
Spinu, V.; Oliveri, A.; Lazar, M.; Storace, M.
2012-01-01
This paper proposes a method for FPGA implementation of explicit, piecewise af¿ne (PWA) model predictive control (MPC) laws for non-inverting buck-boost DC-DC converters. A novel approach to obtain a PWA model of the power converter is proposed and two explicit MPC laws are derived, i.e., one based
Manning, Robert M.
1990-01-01
A static and dynamic rain-attenuation model is presented which describes the statistics of attenuation on an arbitrarily specified satellite link for any location for which there are long-term rainfall statistics. The model may be used in the design of the optimal stochastic control algorithms to mitigate the effects of attenuation and maintain link reliability. A rain-statistics data base is compiled, which makes it possible to apply the model to any location in the continental U.S. with a resolution of 0-5 degrees in latitude and longitude. The model predictions are compared with experimental observations, showing good agreement.
Directory of Open Access Journals (Sweden)
Douglas Halamay
2014-09-01
Full Text Available This paper demonstrates the use of model-based predictive control for energy storage systems to improve the dispatchability of wind power plants. Large-scale wind penetration increases the variability of power flow on the grid, thus increasing reserve requirements. Large energy storage systems collocated with wind farms can improve dispatchability of the wind plant by storing energy during generation over-the-schedule and sourcing energy during generation under-the-schedule, essentially providing on-site reserves. Model predictive control (MPC provides a natural framework for this application. By utilizing an accurate energy storage system model, control actions can be planned in the context of system power and state-of-charge limitations. MPC also enables the inclusion of predicted wind farm performance over a near-term horizon that allows control actions to be planned in anticipation of fast changes, such as wind ramps. This paper demonstrates that model-based predictive control can improve system performance compared with a standard non-predictive, non-model-based control approach. It is also demonstrated that secondary objectives, such as reducing the rate of change of the wind plant output (i.e., ramps, can be considered and successfully implemented within the MPC framework. Specifically, it is shown that scheduling error can be reduced by 81%, reserve requirements can be improved by up to 37%, and the number of ramp events can be reduced by 74%.
Clean coal technology optimization model
International Nuclear Information System (INIS)
Laseke, B.A.; Hance, S.B.
1992-01-01
Title IV of the Clean Air Act Amendments (CAAA) of 1990 contains provisions for the mitigation of acid rain precipitation through reductions in the annual emission of the acid rain precursors of sulfur dioxide (SO 2 ) and nitrogen oxide (NO x ). These provisions will affect primarily existing coal-fired power-generating plants by requiring nominal reductions of 5 millon and 10 million tons of SO 2 by the years 1995 and 2000, respectively, and 2 million tons of NO x by the year 2000 relative to the 1980 and 1985-87 reference period. The 1990 CAAA Title IV provisions are extremely complex in that they establish phased regulatory milestones, unit-level emission allowances and caps, a mechanism for inter-utility trading of emission allowances, and a system of emission allowance credits based on selection of control option and timing of its implementation. The net result of Title IV of the 1990 CAAA is that approximately 147 gigawatts (GW) of generating capacity is eligible to retrofit SO 2 controls by the year 2000. A number of options are available to bring affected boilers into compliance with Title IV. Market sharewill be influenced by technology performance and costs. These characteristics can be modeled through a bottom-up technology cost and performance optimization exercise to show their impact on the technology's potential market share. Such a model exists in the form of an integrated data base-model software system. This microcomputer (PC)-based software system consists of a unit (boiler)-level data base (ACIDBASE), a cost and performance engineering model (IAPCS), and a market forecast model (ICEMAN)
International Nuclear Information System (INIS)
Bourbon, F.
1993-03-01
The present thesis treats of the modeling, of the numerical simulation and equilibrium optimal control and configuration evolution of the plasma in a tokamak. In the first chapter, we establish the problem of equilibrium of the plasma by the finite elements method and a Newton algorithm. Then, we investigate the problem of equilibrium control: determination of currents in circuits of the tokamak to reach a given configuration. In the third chapter, we develop a model for evolution of the plasma configuration during a discharge. We solve this question by a Newton algorithm; with the fourth chapter, we treat the monitoring of plasma configuration evolution
International Nuclear Information System (INIS)
Tiwari, Manish Kumar; Mukhopadhyay, Achintya; Sanyal, Dipankar
2005-01-01
A model based control structure for heat treating a 0.5% C steel slab in a batch furnace with low NO x radiant tube burner is designed and tested for performance to yield optimal parameter values using the model developed in the companion paper. Combustion is considered in a highly preheated and product gas diluted mode. Controlled combustion with a proposed arrangement for preheating and diluting the air by recirculating the exhaust gas that can be retrofitted with an existing burner yields satisfactory performance and emission characteristics. Finally, the effect of variable property considerations are presented and critically analyzed
Constrained Optimization and Optimal Control for Partial Differential Equations
Leugering, Günter; Griewank, Andreas
2012-01-01
This special volume focuses on optimization and control of processes governed by partial differential equations. The contributors are mostly participants of the DFG-priority program 1253: Optimization with PDE-constraints which is active since 2006. The book is organized in sections which cover almost the entire spectrum of modern research in this emerging field. Indeed, even though the field of optimal control and optimization for PDE-constrained problems has undergone a dramatic increase of interest during the last four decades, a full theory for nonlinear problems is still lacking. The cont
Control parameter optimization for AP1000 reactor using Particle Swarm Optimization
International Nuclear Information System (INIS)
Wang, Pengfei; Wan, Jiashuang; Luo, Run; Zhao, Fuyu; Wei, Xinyu
2016-01-01
Highlights: • The PSO algorithm is applied for control parameter optimization of AP1000 reactor. • Key parameters of the MSHIM control system are optimized. • Optimization results are evaluated though simulations and quantitative analysis. - Abstract: The advanced mechanical shim (MSHIM) core control strategy is implemented in the AP1000 reactor for core reactivity and axial power distribution control simultaneously. The MSHIM core control system can provide superior reactor control capabilities via automatic rod control only. This enables the AP1000 to perform power change operations automatically without the soluble boron concentration adjustments. In this paper, the Particle Swarm Optimization (PSO) algorithm has been applied for the parameter optimization of the MSHIM control system to acquire better reactor control performance for AP1000. System requirements such as power control performance, control bank movement and AO control constraints are reflected in the objective function. Dynamic simulations are performed based on an AP1000 reactor simulation platform in each iteration of the optimization process to calculate the fitness values of particles in the swarm. The simulation platform is developed in Matlab/Simulink environment with implementation of a nodal core model and the MSHIM control strategy. Based on the simulation platform, the typical 10% step load decrease transient from 100% to 90% full power is simulated and the objective function used for control parameter tuning is directly incorporated in the simulation results. With successful implementation of the PSO algorithm in the control parameter optimization of AP1000 reactor, four key parameters of the MSHIM control system are optimized. It has been demonstrated by the calculation results that the optimized MSHIM control system parameters can improve the reactor power control capability and reduce the control rod movement without compromising AO control. Therefore, the PSO based optimization
Modelling and Optimizing Mathematics Learning in Children
Käser, Tanja; Busetto, Alberto Giovanni; Solenthaler, Barbara; Baschera, Gian-Marco; Kohn, Juliane; Kucian, Karin; von Aster, Michael; Gross, Markus
2013-01-01
This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalculia or difficulties in learning mathematics. The student model consists of a dynamic…
Directory of Open Access Journals (Sweden)
Yue Hu
2018-02-01
Full Text Available Bipedal locomotion remains one of the major open challenges of humanoid robotics. The common approaches are based on simple reduced model dynamics to generate walking trajectories, often neglecting the whole-body dynamics of the robots. As motions in nature are often considered as optimal with respect to certain criteria, in this work, we present an optimal control-based approach that allows us to generate optimized walking motions using a precise whole-body dynamic model of the robot, in contrast with the common approaches. The optimal control problem is formulated to minimize a set of desired objective functions with respect to physical constraints of the robot and contact constraints of the walking phases; the problem is then solved with a direct multiple shooting method. We apply the formulation with combinations of different objective criteria to the model of a reduced version of the iCub humanoid robot of 15 internal DOF. The obtained trajectories are executed on the real robot, and we carry out a discussion on the differences between the outcomes of this approach with the classic approaches.
Presentation of Malaria Epidemics Using Multiple Optimal Controls
Directory of Open Access Journals (Sweden)
Abid Ali Lashari
2012-01-01
Full Text Available An existing model is extended to assess the impact of some antimalaria control measures, by re-formulating the model as an optimal control problem. This paper investigates the fundamental role of three type of controls, personal protection, treatment, and mosquito reduction strategies in controlling the malaria. We work in the nonlinear optimal control framework. The existence and the uniqueness results of the solution are discussed. A characterization of the optimal control via adjoint variables is established. The optimality system is solved numerically by a competitive Gauss-Seidel-like implicit difference method. Finally, numerical simulations of the optimal control problem, using a set of reasonable parameter values, are carried out to investigate the effectiveness of the proposed control measures.
Fuzzy logic control and optimization system
Lou, Xinsheng [West Hartford, CT
2012-04-17
A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
Control Methods Utilizing Energy Optimizing Schemes in Refrigeration Systems
DEFF Research Database (Denmark)
Larsen, L.S; Thybo, C.; Stoustrup, Jakob
2003-01-01
The potential energy savings in refrigeration systems using energy optimal control has been proved to be substantial. This however requires an intelligent control that drives the refrigeration systems towards the energy optimal state. This paper proposes an approach for a control, which drives th...... the condenser pressure towards an optimal state. The objective of this is to present a feasible method that can be used for energy optimizing control. A simulation model of a simple refrigeration system will be used as basis for testing the control method....
Automatic Synthesis of Robust and Optimal Controllers
DEFF Research Database (Denmark)
Cassez, Franck; Jessen, Jan Jacob; Larsen, Kim Guldstrand
2009-01-01
In this paper, we show how to apply recent tools for the automatic synthesis of robust and near-optimal controllers for a real industrial case study. We show how to use three different classes of models and their supporting existing tools, Uppaal-TiGA for synthesis, phaver for verification......, and Simulink for simulation, in a complementary way. We believe that this case study shows that our tools have reached a level of maturity that allows us to tackle interesting and relevant industrial control problems....
Chen, Y. W.; Chang, L. C.
2012-04-01
Typhoons which normally bring a great amount of precipitation are the primary natural hazard in Taiwan during flooding season. Because the plentiful rainfall quantities brought by typhoons are normally stored for the usage of the next draught period, the determination of release strategies for flood operation of reservoirs which is required to simultaneously consider not only the impact of reservoir safety and the flooding damage in plain area but also for the water resource stored in the reservoir after typhoon becomes important. This study proposes a two-steps study process. First, this study develop an optimal flood operation model (OFOM) for the planning of flood control and also applies the OFOM on Tseng-wun reservoir and the downstream plain related to the reservoir. Second, integrating a typhoon event database with the OFOM mentioned above makes the proposed planning model have ability to deal with a real-time flood control problem and names as real-time flood operation model (RTFOM). Three conditions are considered in the proposed models, OFOM and RTFOM, include the safety of the reservoir itself, the reservoir storage after typhoons and the impact of flooding in the plain area. Besides, the flood operation guideline announced by government is also considered in the proposed models. The these conditions and the guideline can be formed as an optimization problem which is solved by the genetic algorithm (GA) in this study. Furthermore, a distributed runoff model, kinematic-wave geomorphic instantaneous unit hydrograph (KW-GIUH), and a river flow simulation model, HEC-RAS, are used to simulate the river water level of Tseng-wun basin in the plain area and the simulated level is shown as an index of the impact of flooding. Because the simulated levels are required to re-calculate iteratively in the optimization model, applying a recursive artificial neural network (recursive ANN) instead of the HEC-RAS model can significantly reduce the computational burden of
Optimal estimation and control in nuclear power plants
International Nuclear Information System (INIS)
Purviance, J.E.; Tylee, J.L.
1982-08-01
Optimal estimation and control theories offer the potential for more precise control and diagnosis of nuclear power plants. The important element of these theories is that a mathematical plant model is used in conjunction with the actual plant data to optimize some performance criteria. These criteria involve important plant variables and incorporate a sense of the desired plant performance. Several applications of optimal estimation and control to nuclear systems are discussed
Wind turbine optimal control during storms
International Nuclear Information System (INIS)
Petrović, V; Bottasso, C L
2014-01-01
This paper proposes a control algorithm that enables wind turbine operation in high winds. With this objective, an online optimization procedure is formulated that, based on the wind turbine state, estimates those extremal wind speed variations that would produce maximal allowable wind turbine loads. Optimization results are compared to the actual wind speed and, if there is a danger of excessive loading, the wind turbine power reference is adjusted to ensure that loads stay within allowed limits. This way, the machine can operate safely even above the cut-out wind speed, thereby realizing a soft envelope-protecting cut-out. The proposed control strategy is tested and verified using a high-fidelity aeroservoelastic simulation model
Energy Technology Data Exchange (ETDEWEB)
Kurosaki, Yuzuru, E-mail: kurosaki.yuzuru@jaea.go.jp [Quantum Beam Science Directorate, Tokai Research and Development Center, Japan Atomic Energy Agency, Tokai, Ibaraki 319-1195 (Japan); Ho, Tak-San, E-mail: tsho@Princeton.EDU [Department of Chemistry, Princeton University, Princeton, NJ 08544 (United States); Rabitz, Herschel, E-mail: hrabitz@Princeton.EDU [Department of Chemistry, Princeton University, Princeton, NJ 08544 (United States)
2016-05-01
The prospect of performing the open → cyclic ozone isomerization has attracted much research attention. Here we explore this consideration theoretically by performing quantum optimal control calculations to demonstrate the important role that excited-state dissociation channels could play in the isomerization transformation. In the calculations we use a three-state, one-dimensional dynamical model constructed from the lowest five {sup 1}A′ potential energy curves obtained with high-level ab initio calculations. Besides the laser field-dipole couplings between all three states, this model also includes the diabatic coupling between the two excited states at an avoided crossing leading to competing dissociation channels that can further hinder the isomerization process. The present three-state optimal control simulations examine two possible control pathways previously considered in a two-state model, and reveal that only one of the pathways is viable, achieving a robust ∼95% yield to the cyclic target in the three-state model. This work represents a step towards an ultimate model for the open → cyclic ozone transformation capable of giving adequate guidance about the necessary experimental control field resources as well as an estimate of the ro-vibronic spectral character of cyclic ozone as a basis for an appropriate probe of its formation.
The neural optimal control hierarchy for motor control
DeWolf, T.; Eliasmith, C.
2011-10-01
Our empirical, neuroscientific understanding of biological motor systems has been rapidly growing in recent years. However, this understanding has not been systematically mapped to a quantitative characterization of motor control based in control theory. Here, we attempt to bridge this gap by describing the neural optimal control hierarchy (NOCH), which can serve as a foundation for biologically plausible models of neural motor control. The NOCH has been constructed by taking recent control theoretic models of motor control, analyzing the required processes, generating neurally plausible equivalent calculations and mapping them on to the neural structures that have been empirically identified to form the anatomical basis of motor control. We demonstrate the utility of the NOCH by constructing a simple model based on the identified principles and testing it in two ways. First, we perturb specific anatomical elements of the model and compare the resulting motor behavior with clinical data in which the corresponding area of the brain has been damaged. We show that damaging the assigned functions of the basal ganglia and cerebellum can cause the movement deficiencies seen in patients with Huntington's disease and cerebellar lesions. Second, we demonstrate that single spiking neuron data from our model's motor cortical areas explain major features of single-cell responses recorded from the same primate areas. We suggest that together these results show how NOCH-based models can be used to unify a broad range of data relevant to biological motor control in a quantitative, control theoretic framework.
Modeling and optimization of HVAC energy consumption
Energy Technology Data Exchange (ETDEWEB)
Kusiak, Andrew; Li, Mingyang; Tang, Fan [Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, IA 52242 - 1527 (United States)
2010-10-15
A data-driven approach for minimization of the energy to air condition a typical office-type facility is presented. Eight data-mining algorithms are applied to model the nonlinear relationship among energy consumption, control settings (supply air temperature and supply air static pressure), and a set of uncontrollable parameters. The multiple-linear perceptron (MLP) ensemble outperforms other models tested in this research, and therefore it is selected to model a chiller, a pump, a fan, and a reheat device. These four models are integrated into an energy optimization model with two decision variables, the setpoint of the supply air temperature and the static pressure in the air handling unit. The model is solved with a particle swarm optimization algorithm. The optimization results have demonstrated the total energy consumed by the heating, ventilation, and air-conditioning system is reduced by over 7%. (author)
Optimal coordination and control of posture and movements.
Johansson, Rolf; Fransson, Per-Anders; Magnusson, Måns
2009-01-01
This paper presents a theoretical model of stability and coordination of posture and locomotion, together with algorithms for continuous-time quadratic optimization of motion control. Explicit solutions to the Hamilton-Jacobi equation for optimal control of rigid-body motion are obtained by solving an algebraic matrix equation. The stability is investigated with Lyapunov function theory and it is shown that global asymptotic stability holds. It is also shown how optimal control and adaptive control may act in concert in the case of unknown or uncertain system parameters. The solution describes motion strategies of minimum effort and variance. The proposed optimal control is formulated to be suitable as a posture and movement model for experimental validation and verification. The combination of adaptive and optimal control makes this algorithm a candidate for coordination and control of functional neuromuscular stimulation as well as of prostheses. Validation examples with experimental data are provided.
International Nuclear Information System (INIS)
Osgouee, Ahmad
2010-01-01
many advanced control methods proposed for the control of nuclear SG water level, operators are still experiencing difficulties especially at low powers. Therefore, it seems that a suitable controller to replace the manual operations is still needed. In this paper optimization of SGL set-points and designing a robust control for SGL control system using will be discussed
Optimal control of evaporator and washer plants
International Nuclear Information System (INIS)
Niemi, A.J.
1989-01-01
Tests with radioactive tracers were used for experimental analysis of a multiple-effect evaporator plant. The residence time distribution of the liquor in each evaporator was described by one or two perfect mixers with time delay and by-pass flow terms. The theoretical model of a single evaporator unit was set up on the basis of its instantaneous heat and mass balances and such models were fitted to the test data. The results were interpreted in terms of physical structures of the evaporators. Further model parameters were evaluated by conventional step tests and by measurements of process variables at one or more steady states. Computer simulation and comparison with the experimental results showed that the model produces a satisfactory response to solids concentration input and could be extended to cover the steam feed and liquor flow inputs. An optimal feedforward control algorithm was developed for a two unit, co-current evaporator plant. The control criterion comprised the deviations of the final solids content of liquor and the consumption of fresh steam, from their optimal steady-state values. In order to apply the algorithm, the model of the solids in liquor was reduced to two nonlinear differential equations. (author)
Directory of Open Access Journals (Sweden)
Jianjun Hu
2018-03-01
Full Text Available The strict operational condition of driving motors for vehicles propels the development of more complicated configurations in pure electric vehicles (PEVs. Multi-power-source powertrain configurations are one of the efficient technologies to reduce the manufacturing difficulty of driving motors. However, most of the existing studies are predominantly focused on optimal designs of powertrains and power distribution between the engine and motor of hybrid electric vehicles, which are not appropriate for PEVs. This paper proposes a novel dual-motor coupling-propulsion powertrain system that improves the dynamic and economic performance of the powertrain system in PEVs. The proposed powertrain system can realize both the single-motor driving mode and dual-motor coupling driving mode. The driving modes are divided and a power distribution strategy for the different driving modes based on an optimal system efficiency rule is employed, which enhances the performance of the proposed system. Further, a mode-switching strategy that ensures driving comfort by preventing jerk during mode switching is incorporated into the system. The results of comparative evaluations that were conducted using a dual-motor electric vehicle model implemented in MATLAB/Simulink, indicate that the mileage and dynamic performance of the proposed powertrain system are significantly better than those of the traditional single-motor powertrain system.
Hierarchical Control for Optimal and Distributed Operation of Microgrid Systems
DEFF Research Database (Denmark)
Meng, Lexuan
manages the power flow with external grids, while the economic and optimal operation of MGs is not guaranteed by applying the existing schemes. Accordingly, this project dedicates to the study of real-time optimization methods for MGs, including the review of optimization algorithms, system level...... mathematical modeling, and the implementation of real-time optimization into existing hierarchical control schemes. Efficiency enhancement in DC MGs and optimal unbalance compensation in AC MGs are taken as the optimization objectives in this project. Necessary system dynamic modeling and stability analysis......, a discrete-time domain modeling method is proposed to establish an accurate system level model. Taking into account the different sampling times of real world plant, digital controller and communication devices, the system is modeled with these three parts separately, and with full consideration...
Pyomo optimization modeling in Python
Hart, William E; Watson, Jean-Paul; Woodruff, David L; Hackebeil, Gabriel A; Nicholson, Bethany L; Siirola, John D
2017-01-01
This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. This second edition provides an expanded presentation of Pyomo’s modeling capabilities, providing a broader description of the software that will enable the user to develop and optimize models. Introductory chapters have been revised to extend tutorials; chapters that discuss advanced features now include the new functionalities added to Pyomo since the first edition including generalized disjunctive programming, mathematical programming with equilibrium constraints, and bilevel programming. Pyomo is an open source software package fo...
DEFF Research Database (Denmark)
Koch-Ciobotaru, Cosmin; Isleifsson, Fridrik Rafn; Gehrke, Oliver
2012-01-01
This paper presents a model predictive controller developed in order to minimize the cost of grid energy consumption and maximize the amount of energy consumed from a local photovoltaic (PV) installation. The usage of as much locally produced renewable energy sources (RES) as possible, diminishes...... the effects of their large penetration in the distribution grid and reduces overloading the grid capacity, which is an increasing problem for the power system. The controller uses 24 hour prediction data for the ambient temperature, the solar irradiance, and for the PV output power. Simulation results...
Combined Optimal Sizing and Control for a Hybrid Tracked Vehicle
Directory of Open Access Journals (Sweden)
Huei Peng
2012-11-01
Full Text Available The optimal sizing and control of a hybrid tracked vehicle is presented and solved in this paper. A driving schedule obtained from field tests is used to represent typical tracked vehicle operations. Dynamics of the diesel engine-permanent magnetic AC synchronous generator set, the lithium-ion battery pack, and the power split between them are modeled and validated through experiments. Two coupled optimizations, one for the plant parameters, forming the outer optimization loop and one for the control strategy, forming the inner optimization loop, are used to achieve minimum fuel consumption under the selected driving schedule. The dynamic programming technique is applied to find the optimal controller in the inner loop while the component parameters are optimized iteratively in the outer loop. The results are analyzed, and the relationship between the key parameters is observed to keep the optimal sizing and control simultaneously.
Venturi scrubber modelling and optimization
Energy Technology Data Exchange (ETDEWEB)
Viswanathan, S [National Univ., La Jolla, CA (United States). School of Engineering and Technology; Ananthanarayanan, N.V. [National Univ. of Singapore (Singapore). Dept. of Chemical and Environmental Engineering; Azzopardi, B.J. [Nottingham Univ., Nottingham (United Kingdom). Dept. of Chemical Engineering
2005-04-01
This study presented a method to maintain the efficiency of venturi scrubbers in removing fine particulates during gas clean operations while minimizing pressure drop. Venturi scrubbers meet stringent emission standards. In order to choose the optimal method for predicting pressure drop, 4 established models were compared for their accuracy of prediction and simplicity in application. The enhanced algorithm optimizes Pease-Anthony type venturi scrubber performance by predicting the minimum pressure drop required to achieve the desired collection efficiency. This was accomplished by optimizing the key operating and design parameters such as liquid-to-gas ratio, throat gas velocity, number of nozzles, nozzle diameter and throat aspect ratio. Two of the 4 established models were expanded by providing an empirical algorithm to better predict pressure drop in the venturi throat. Model results were validated with experimental data. The optimization algorithm considers the non-uniformity in liquid distribution. It can be applied to cylindrical and rectangular Pease-Anthony type scrubbers. It offers an effective, systematic and accurate method to optimize the performance of new and existing scrubbers. 54 refs., 5 figs.
Adaptive optimization and control using neural networks
Energy Technology Data Exchange (ETDEWEB)
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
International Nuclear Information System (INIS)
Liu, C.-T.; Lee, S.-C.
2006-01-01
A detailed approach for analyzing magnetic field distributions of a single-sided axial-flux permanent magnet motor with center poles will be provided. Based on the devised flux model, the related position-dependent torque and axial force of the motor can be systematically developed. By incorporating adequate control designs, the optimal operational performance of the motor system can be conveniently achieved. Results showed that not only the motor structure is suitable for related military and transportation applications, but also the magnetic field model can provide appropriate mathematical basis for relative operational realizations
Directory of Open Access Journals (Sweden)
Ruisheng Sun
2016-01-01
Full Text Available This paper presents a new parametric optimization approach based on a modified particle swarm optimization (PSO to design a class of impulsive-correction projectiles with discrete, flexible-time interval, and finite-energy control. In terms of optimal control theory, the task is described as the formulation of minimum working number of impulses and minimum control error, which involves reference model linearization, boundary conditions, and discontinuous objective function. These result in difficulties in finding the global optimum solution by directly utilizing any other optimization approaches, for example, Hp-adaptive pseudospectral method. Consequently, PSO mechanism is employed for optimal setting of impulsive control by considering the time intervals between two neighboring lateral impulses as design variables, which makes the briefness of the optimization process. A modification on basic PSO algorithm is developed to improve the convergence speed of this optimization through linearly decreasing the inertial weight. In addition, a suboptimal control and guidance law based on PSO technique are put forward for the real-time consideration of the online design in practice. Finally, a simulation case coupled with a nonlinear flight dynamic model is applied to validate the modified PSO control algorithm. The results of comparative study illustrate that the proposed optimal control algorithm has a good performance in obtaining the optimal control efficiently and accurately and provides a reference approach to handling such impulsive-correction problem.
Zika virus transmission dynamic model and its optimal control%Zika病毒传播动力学模型及其最优控制
Institute of Scientific and Technical Information of China (English)
丁春晓; 朱元国
2016-01-01
In order to investigate the transimission law of Zika virus, we construct a Zika virus transmission dynamics model with Logistic growth rate. The existence of the disease free equilibrium and endemic equilibrium of the model are proved. The basic reproduction number is calculated to demonstrate the threshold of the disease outbreak. Based on the model,we present the corresponding optimal control problem,propose the necessary condition for the existence of the solution,and obtain the optimal solution. Finally, numerical simulations of different control strategies give the effectiveness of the model and the rationality of control strategies.%为了探究Zika病毒传播规律，创建了具有Logistic增长率的Zika病毒传播动力学模型。证明了模型无病平衡点和地方病平衡点的存在性，计算了疾病爆发阈值———基本再生数。随后，提出了基于模型的最优控制问题，给出了最优解存在的必要条件并求得了最优解。最后通过数值仿真例子，对比不同控制策略，证明了模型的有效性和控制策略的合理性。
Energy Technology Data Exchange (ETDEWEB)
Matoba, Y. [Sumitomo Metal Industries, Ltd., Osaka (Japan); Otsuka, K.
1998-07-01
A mathematical model and an optimal control system of hot-blast stove process are described. A precise mathematical simulation model of the hot-blast stove was developed and the accuracy of the model has been confirmed. An optimal control system of the thermal conditions of the hot-blast stoves in staggered parallel operation was also developed. By the use of the multivariable optimal regulator and the feedforward compensations for the change of the aimed blast temperature and blast volume, the system is able to control the hot blast temperature and the brick temperature efficiently. The system has been applied to Kashima works. The variations of the blast temperature and the silica brick temperature have been decreased. The ultimate low heat level operations have been realized and the thermal efficiency furthermore has been raised by about 1%. 8 refs., 14 figs., 1 tab.
ON THE OPTIMAL CONTROL OF A PROBLEM OF ENVIRONMENTAL POLLUTION
Directory of Open Access Journals (Sweden)
José Dávalos Chuquipoma
2016-06-01
Full Text Available This article is studied the optimal control of distributed parameter systems applied to an environmental pollution problem. The model consists of a differential equation partial parabolic modeling of a pollutant transport in a fluid. The model is considered the speed with which the pollutant spreads in the environment and degradation that suffers the contaminant by the presence of a factor biological inhibitor, which breaks the contaminant at a rate that is not dependent on space and time. Using the method of Lagrange multipliers is possible to prove the existence solving the problem of control and obtaining optimality conditions for optimal control.
Dynamic optimization deterministic and stochastic models
Hinderer, Karl; Stieglitz, Michael
2016-01-01
This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.
Quantum optimal control of ozone isomerization
International Nuclear Information System (INIS)
Artamonov, Maxim; Ho, Tak-San; Rabitz, Herschel
2004-01-01
We present a feasibility study of ozone isomerization based on a recent ab initio potential energy surface and a model Hamiltonian constructed by holding the bond lengths constant and using the valence angle as the isomerization coordinate. Optimal control theory is used to find an electric field that drives isomerization with a yield of 95% to the symmetric metastable triangular form of ozone. A frequency filter is applied as an additional spectral constraint limiting the field bandwidth. A post-facto analysis is performed showing a degree of inherent robustness of the isomerization yield to field noise
Time-optimal control with finite bandwidth
Hirose, M.; Cappellaro, P.
2018-04-01
Time-optimal control theory provides recipes to achieve quantum operations with high fidelity and speed, as required in quantum technologies such as quantum sensing and computation. While technical advances have achieved the ultrastrong driving regime in many physical systems, these capabilities have yet to be fully exploited for the precise control of quantum systems, as other limitations, such as the generation of higher harmonics or the finite response time of the control apparatus, prevent the implementation of theoretical time-optimal control. Here we present a method to achieve time-optimal control of qubit systems that can take advantage of fast driving beyond the rotating wave approximation. We exploit results from time-optimal control theory to design driving protocols that can be implemented with realistic, finite-bandwidth control fields, and we find a relationship between bandwidth limitations and achievable control fidelity.
Optimization of boundary controls of string vibrations
Energy Technology Data Exchange (ETDEWEB)
Il' in, V A; Moiseev, E I [Department of Computing Mathematics and Cybernetics, M.V. Lomonosov Moscow State University, Moscow (Russian Federation)
2005-12-31
For a large time interval T boundary controls of string vibrations are optimized in the following seven boundary-control problems: displacement control at one end (with the other end fixed or free); displacement control at both ends; elastic force control at one end (with the other end fixed or free); elastic force control at both ends; combined control (displacement control at one end and elastic force control at the other). Optimal boundary controls in each of these seven problems are sought as functions minimizing the corresponding boundary-energy integral under the constraints following from the initial and terminal conditions for the string at t=0 and t=T, respectively. For all seven problems, the optimal boundary controls are written out in closed analytic form.
Optimal control of a wave energy converter
Hendrikx, R.W.M.; Leth, J.; Andersen, P; Heemels, W.P.M.H.
2017-01-01
The optimal control strategy for a wave energy converter (WEC) with constraints on the control torque is investigated. The goal is to optimize the total energy delivered to the electricity grid. Using Pontryagin's maximum principle, the solution is found to be singular-bang. Using higher order
An optimal control problem for controlling the cell volume in dehydration and rehydration process
Energy Technology Data Exchange (ETDEWEB)
Chenghung Huang; Tetsung Chen [National Cheng Kung Univ., Dept. of Systems and Naval Mechatronic Engineering, Tainan (Taiwan)
2004-08-01
An optimal control algorithm utilizing the conjugate gradient method (CGM) of minimization is applied successfully in the present study in determining the optimal boundary control function for a diffusion-limited cell model based on the desired cell volume. The validity of the present optimal control analysis is examined by means of numerical experiments. Different desired cell volume for dehydration, rehydration and their combination are given in three test cases with different weighting coefficients and the corresponding optimal control functions are determined. The results show that the optimal boundary control functions can be obtained with an arbitrary initial guess within one second CPU time on a Pentium III-600 MHz PC. (Author)
Two optimal control methods for PWR core control
International Nuclear Information System (INIS)
Karppinen, J.; Blomsnes, B.; Versluis, R.M.
1976-01-01
The Multistage Mathematical Programming (MMP) and State Variable Feedback (SVF) methods for PWR core control are presented in this paper. The MMP method is primarily intended for optimization of the core behaviour with respect to xenon induced power distribution effects in load cycle operation. The SVF method is most suited for xenon oscillation damping in situations where the core load is unpredictable or expected to stay constant. Results from simulation studies in which the two methods have been applied for control of simple PWR core models are presented. (orig./RW) [de
Anita, Sebastian; Capasso, Vincenzo
2011-01-01
Combining control theory and modeling, this textbook introduces and builds on methods for simulating and tackling concrete problems in a variety of applied sciences. Emphasizing "learning by doing," the authors focus on examples and applications to real-world problems. An elementary presentation of advanced concepts, proofs to introduce new ideas, and carefully presented MATLAB(R) programs help foster an understanding of the basics, but also lead the way to new, independent research. With minimal prerequisites and exercises in each chapter, this work serves as an excellent textbook a
Neural Networks for Optimal Control
DEFF Research Database (Denmark)
Sørensen, O.
1995-01-01
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....
Optimal switching using coherent control
DEFF Research Database (Denmark)
Kristensen, Philip Trøst; Heuck, Mikkel; Mørk, Jesper
2013-01-01
that the switching time, in general, is not limited by the cavity lifetime. Therefore, the total energy required for switching is a more relevant figure of merit than the switching speed, and for a particular two-pulse switching scheme we use calculus of variations to optimize the switching in terms of input energy....
Modeling, simulation and optimization of bipedal walking
Berns, Karsten
2013-01-01
The model-based investigation of motions of anthropomorphic systems is an important interdisciplinary research topic involving specialists from many fields such as Robotics, Biomechanics, Physiology, Orthopedics, Psychology, Neurosciences, Sports, Computer Graphics and Applied Mathematics. This book presents a study of basic locomotion forms such as walking and running is of particular interest due to the high demand on dynamic coordination, actuator efficiency and balance control. Mathematical models and numerical simulation and optimization techniques are explained, in combination with experimental data, which can help to better understand the basic underlying mechanisms of these motions and to improve them. Example topics treated in this book are Modeling techniques for anthropomorphic bipedal walking systems Optimized walking motions for different objective functions Identification of objective functions from measurements Simulation and optimization approaches for humanoid robots Biologically inspired con...
Optimal control of stochastic difference Volterra equations an introduction
Shaikhet, Leonid
2015-01-01
This book showcases a subclass of hereditary systems, that is, systems with behaviour depending not only on their current state but also on their past history; it is an introduction to the mathematical theory of optimal control for stochastic difference Volterra equations of neutral type. As such, it will be of much interest to researchers interested in modelling processes in physics, mechanics, automatic regulation, economics and finance, biology, sociology and medicine for all of which such equations are very popular tools. The text deals with problems of optimal control such as meeting given performance criteria, and stabilization, extending them to neutral stochastic difference Volterra equations. In particular, it contrasts the difference analogues of solutions to optimal control and optimal estimation problems for stochastic integral Volterra equations with optimal solutions for corresponding problems in stochastic difference Volterra equations. Optimal Control of Stochastic Difference Volterra Equation...
Optimal Power Flow Control by Rotary Power Flow Controller
Directory of Open Access Journals (Sweden)
KAZEMI, A.
2011-05-01
Full Text Available This paper presents a new power flow model for rotary power flow controller (RPFC. RPFC injects a series voltage into the transmission line and provides series compensation and phase shifting simultaneously. Therefore, it is able to control the transmission line impedance and the active power flow through it. An RPFC is composed mainly of two rotary phase shifting transformers (RPST and two conventional (series and shunt transformers. Structurally, an RPST consists of two windings (stator and rotor windings. The rotor windings of the two RPSTs are connected in parallel and their stator windings are in series. The injected voltage is proportional to the vector sum of the stator voltages and so its amplitude and angle are affected by the rotor position of the two RPSTs. This paper, describes the steady state operation and single-phase equivalent circuit of the RPFC. Also in this paper, a new power flow model, based on power injection model of flexible ac transmission system (FACTS controllers, suitable for the power flow analysis is introduced. Proposed model is used to solve optimal power flow (OPF problem in IEEE standard test systems incorporating RPFC and the optimal settings and location of the RPFC is determined.
Asgharnia, Amirhossein; Shahnazi, Reza; Jamali, Ali
2018-05-11
The most studied controller for pitch control of wind turbines is proportional-integral-derivative (PID) controller. However, due to uncertainties in wind turbine modeling and wind speed profiles, the need for more effective controllers is inevitable. On the other hand, the parameters of PID controller usually are unknown and should be selected by the designer which is neither a straightforward task nor optimal. To cope with these drawbacks, in this paper, two advanced controllers called fuzzy PID (FPID) and fractional-order fuzzy PID (FOFPID) are proposed to improve the pitch control performance. Meanwhile, to find the parameters of the controllers the chaotic evolutionary optimization methods are used. Using evolutionary optimization methods not only gives us the unknown parameters of the controllers but also guarantees the optimality based on the chosen objective function. To improve the performance of the evolutionary algorithms chaotic maps are used. All the optimization procedures are applied to the 2-mass model of 5-MW wind turbine model. The proposed optimal controllers are validated using simulator FAST developed by NREL. Simulation results demonstrate that the FOFPID controller can reach to better performance and robustness while guaranteeing fewer fatigue damages in different wind speeds in comparison to FPID, fractional-order PID (FOPID) and gain-scheduling PID (GSPID) controllers. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Optimal control of a harmonic oscillator: Economic interpretations
Janová, Jitka; Hampel, David
2013-10-01
Optimal control is a popular technique for modelling and solving the dynamic decision problems in economics. A standard interpretation of the criteria function and Lagrange multipliers in the profit maximization problem is well known. On a particular example, we aim to a deeper understanding of the possible economic interpretations of further mathematical and solution features of the optimal control problem: we focus on the solution of the optimal control problem for harmonic oscillator serving as a model for Phillips business cycle. We discuss the economic interpretations of arising mathematical objects with respect to well known reasoning for these in other problems.
Advanced Process Control Application and Optimization in Industrial Facilities
Directory of Open Access Journals (Sweden)
Howes S.
2015-01-01
Full Text Available This paper describes application of the new method and tool for system identification and PID tuning/advanced process control (APC optimization using the new 3G (geometric, gradient, gravity optimization method. It helps to design and implement control schemes directly inside the distributed control system (DCS or programmable logic controller (PLC. Also, the algorithm helps to identify process dynamics in closed-loop mode, optimizes controller parameters, and helps to develop adaptive control and model-based control (MBC. Application of the new 3G algorithm for designing and implementing APC schemes is presented. Optimization of primary and advanced control schemes stabilizes the process and allows the plant to run closer to process, equipment and economic constraints. This increases production rates, minimizes operating costs and improves product quality.
Optimal Vibration Control for Tracked Vehicle Suspension Systems
Directory of Open Access Journals (Sweden)
Yan-Jun Liang
2013-01-01
Full Text Available Technique of optimal vibration control with exponential decay rate and simulation for vehicle active suspension systems is developed. Mechanical model and dynamic system for a class of tracked vehicle suspension vibration control is established and the corresponding system of state space form is described. In order to prolong the working life of suspension system and improve ride comfort, based on the active suspension vibration control devices and using optimal control approach, an optimal vibration controller with exponential decay rate is designed. Numerical simulations are carried out, and the control effects of the ordinary optimal controller and the proposed controller are compared. Numerical simulation results illustrate the effectiveness of the proposed technique.
Optimal control of switched systems arising in fermentation processes
Liu, Chongyang
2014-01-01
The book presents, in a systematic manner, the optimal controls under different mathematical models in fermentation processes. Variant mathematical models – i.e., those for multistage systems; switched autonomous systems; time-dependent and state-dependent switched systems; multistage time-delay systems and switched time-delay systems – for fed-batch fermentation processes are proposed and the theories and algorithms of their optimal control problems are studied and discussed. By putting forward novel methods and innovative tools, the book provides a state-of-the-art and comprehensive systematic treatment of optimal control problems arising in fermentation processes. It not only develops nonlinear dynamical system, optimal control theory and optimization algorithms, but can also help to increase productivity and provide valuable reference material on commercial fermentation processes.
Existence theory in optimal control
International Nuclear Information System (INIS)
Olech, C.
1976-01-01
This paper treats the existence problem in two main cases. One case is that of linear systems when existence is based on closedness or compactness of the reachable set and the other, non-linear case refers to a situation where for the existence of optimal solutions closedness of the set of admissible solutions is needed. Some results from convex analysis are included in the paper. (author)
Optimal control of a waste water cleaning plant
Directory of Open Access Journals (Sweden)
Ellina V. Grigorieva
2010-09-01
Full Text Available In this work, a model of a waste water treatment plant is investigated. The model is described by a nonlinear system of two differential equations with one bounded control. An optimal control problem of minimizing concentration of the polluted water at the terminal time T is stated and solved analytically with the use of the Pontryagin Maximum Principle. Dependence of the optimal solution on the initial conditions is established. Computer simulations of a model of an industrial waste water treatment plant show the advantage of using our optimal strategy. Possible applications are discussed.
Optimal control of a qubit in an optical cavity
International Nuclear Information System (INIS)
Deffner, Sebastian
2014-01-01
We study quantum information processing by means of optimal control theory. To this end, we analyze the damped Jaynes–Cummings model, and derive optimal control protocols that minimize the heating or energy dispersion rates, and controls that drive the system at the quantum speed limit. Special emphasis is put on analyzing the subtleties of optimal control theory for our system. In particular, it is shown how two fundamentally different approaches to the quantum speed limit can be reconciled by carefully formulating the problem. (paper)
2017-03-21
Monitoring and Verification Protocol LCDR Lieutenant Commander ME Mechanical Engineering MPC Model Predictive Control: a model-based system...Activity PE Professional Engineer PG&E Pacific Gas & Electric : a California utility company PI Proportional + Integral: a common software element...INTRODUCTION The Department of Defense (DoD) spends approximately $4 billion per year on facility energy consumption to power and fuel over 500
Optimization of nonlinear controller with an enhanced biogeography approach
Directory of Open Access Journals (Sweden)
Mohammed Salem
2014-07-01
Full Text Available This paper is dedicated to the optimization of nonlinear controllers basing of an enhanced Biogeography Based Optimization (BBO approach. Indeed, The BBO is combined to a predator and prey model where several predators are used with introduction of a modified migration operator to increase the diversification along the optimization process so as to avoid local optima and reach the optimal solution quickly. The proposed approach is used in tuning the gains of PID controller for nonlinear systems. Simulations are carried out over a Mass spring damper and an inverted pendulum and has given remarkable results when compared to genetic algorithm and BBO.
Optimal Control and Forecasting of Complex Dynamical Systems
Grigorenko, Ilya
2006-01-01
This important book reviews applications of optimization and optimal control theory to modern problems in physics, nano-science and finance. The theory presented here can be efficiently applied to various problems, such as the determination of the optimal shape of a laser pulse to induce certain excitations in quantum systems, the optimal design of nanostructured materials and devices, or the control of chaotic systems and minimization of the forecast error for a given forecasting model (for example, artificial neural networks). Starting from a brief review of the history of variational calcul
Optimal Control of Evolution Mixed Variational Inclusions
Energy Technology Data Exchange (ETDEWEB)
Alduncin, Gonzalo, E-mail: alduncin@geofisica.unam.mx [Universidad Nacional Autónoma de México, Departamento de Recursos Naturales, Instituto de Geofísica (Mexico)
2013-12-15
Optimal control problems of primal and dual evolution mixed variational inclusions, in reflexive Banach spaces, are studied. The solvability analysis of the mixed state systems is established via duality principles. The optimality analysis is performed in terms of perturbation conjugate duality methods, and proximation penalty-duality algorithms to mixed optimality conditions are further presented. Applications to nonlinear diffusion constrained problems as well as quasistatic elastoviscoplastic bilateral contact problems exemplify the theory.
Optimal Control of Evolution Mixed Variational Inclusions
International Nuclear Information System (INIS)
Alduncin, Gonzalo
2013-01-01
Optimal control problems of primal and dual evolution mixed variational inclusions, in reflexive Banach spaces, are studied. The solvability analysis of the mixed state systems is established via duality principles. The optimality analysis is performed in terms of perturbation conjugate duality methods, and proximation penalty-duality algorithms to mixed optimality conditions are further presented. Applications to nonlinear diffusion constrained problems as well as quasistatic elastoviscoplastic bilateral contact problems exemplify the theory
Role of controllability in optimizing quantum dynamics
International Nuclear Information System (INIS)
Wu Rebing; Hsieh, Michael A.; Rabitz, Herschel
2011-01-01
This paper reveals an important role that controllability plays in the complexity of optimizing quantum control dynamics. We show that the loss of controllability generally leads to multiple locally suboptimal controls when gate fidelity in a quantum control system is maximized, which does not happen if the system is controllable. Such local suboptimal controls may attract an optimization algorithm into a local trap when a global optimal solution is sought, even if the target gate can be perfectly realized. This conclusion results from an analysis of the critical topology of the corresponding quantum control landscape, which refers to the gate fidelity objective as a functional of the control fields. For uncontrollable systems, due to SU(2) and SU(3) dynamical symmetries, the control landscape corresponding to an implementable target gate is proven to possess multiple locally optimal critical points, and its ruggedness can be further increased if the target gate is not realizable. These results imply that the optimization of quantum dynamics can be seriously impeded when operating with local search algorithms under these conditions, and thus full controllability is demanded.
Optimal inventory management and order book modeling
Baradel, Nicolas
2018-02-16
We model the behavior of three agent classes acting dynamically in a limit order book of a financial asset. Namely, we consider market makers (MM), high-frequency trading (HFT) firms, and institutional brokers (IB). Given a prior dynamic of the order book, similar to the one considered in the Queue-Reactive models [14, 20, 21], the MM and the HFT define their trading strategy by optimizing the expected utility of terminal wealth, while the IB has a prescheduled task to sell or buy many shares of the considered asset. We derive the variational partial differential equations that characterize the value functions of the MM and HFT and explain how almost optimal control can be deduced from them. We then provide a first illustration of the interactions that can take place between these different market participants by simulating the dynamic of an order book in which each of them plays his own (optimal) strategy.
Optimal Speed Control for Cruising
DEFF Research Database (Denmark)
Blanke, M.
1994-01-01
With small profit margins in merchant shipping and more than eighty percent of sailing time being cross ocean voyages, speed control is crucial for vessel profitability......With small profit margins in merchant shipping and more than eighty percent of sailing time being cross ocean voyages, speed control is crucial for vessel profitability...
In-flight performance optimization for rotorcraft with redundant controls
Ozdemir, Gurbuz Taha
A conventional helicopter has limits on performance at high speeds because of the limitations of main rotor, such as compressibility issues on advancing side or stall issues on retreating side. Auxiliary lift and thrust components have been suggested to improve performance of the helicopter substantially by reducing the loading on the main rotor. Such a configuration is called the compound rotorcraft. Rotor speed can also be varied to improve helicopter performance. In addition to improved performance, compound rotorcraft and variable RPM can provide a much larger degree of control redundancy. This additional redundancy gives the opportunity to further enhance performance and handling qualities. A flight control system is designed to perform in-flight optimization of redundant control effectors on a compound rotorcraft in order to minimize power required and extend range. This "Fly to Optimal" (FTO) control law is tested in simulation using the GENHEL model. A model of the UH-60, a compound version of the UH-60A with lifting wing and vectored thrust ducted propeller (VTDP), and a generic compound version of the UH-60A with lifting wing and propeller were developed and tested in simulation. A model following dynamic inversion controller is implemented for inner loop control of roll, pitch, yaw, heave, and rotor RPM. An outer loop controller regulates airspeed and flight path during optimization. A Golden Section search method was used to find optimal rotor RPM on a conventional helicopter, where the single redundant control effector is rotor RPM. The FTO builds off of the Adaptive Performance Optimization (APO) method of Gilyard by performing low frequency sweeps on a redundant control for a fixed wing aircraft. A method based on the APO method was used to optimize trim on a compound rotorcraft with several redundant control effectors. The controller can be used to optimize rotor RPM and compound control effectors through flight test or simulations in order to
Directory of Open Access Journals (Sweden)
Carlos Villaseñor
2017-12-01
Full Text Available Nowadays, there are several meta-heuristics algorithms which offer solutions for multi-variate optimization problems. These algorithms use a population of candidate solutions which explore the search space, where the leadership plays a big role in the exploration-exploitation equilibrium. In this work, we propose to use a Germinal Center Optimization algorithm (GCO which implements temporal leadership through modeling a non-uniform competitive-based distribution for particle selection. GCO is used to find an optimal set of parameters for a neural inverse optimal control applied to all-terrain tracked robot. In the Neural Inverse Optimal Control (NIOC scheme, a neural identifier, based on Recurrent High Orden Neural Network (RHONN trained with an extended kalman filter algorithm, is used to obtain a model of the system, then, a control law is design using such model with the inverse optimal control approach. The RHONN identifier is developed without knowledge of the plant model or its parameters, on the other hand, the inverse optimal control is designed for tracking velocity references. Applicability of the proposed scheme is illustrated using simulations results as well as real-time experimental results with an all-terrain tracked robot.
IMPORTANCE OF KINETIC MEASURES IN TRAJECTORY PREDICTION WITH OPTIMAL CONTROL
Directory of Open Access Journals (Sweden)
Ömer GÜNDOĞDU
2001-02-01
Full Text Available A two-dimensional sagittally symmetric human-body model was established to simulate an optimal trajectory for manual material handling tasks. Nonlinear control techniques and genetic algorithms were utilized in the optimizations to explore optimal lifting patterns. The simulation results were then compared with the experimental data. Since the kinetic measures such as joint reactions and moments are vital parameters in injury determination, the importance of comparing kinetic measures rather than kinematical ones was emphasized.
Parameters control in GAs for dynamic optimization
Directory of Open Access Journals (Sweden)
Khalid Jebari
2013-02-01
Full Text Available The Control of Genetic Algorithms parameters allows to optimize the search process and improves the performance of the algorithm. Moreover it releases the user to dive into a game process of trial and failure to find the optimal parameters.
Optimal Control Design for a Solar Greenhouse
Ooteghem, van R.J.C.
2010-01-01
Abstract: An optimal climate control has been designed for a solar greenhouse to achieve optimal crop production with sustainable instead of fossil energy. The solar greenhouse extends a conventional greenhouse with an improved roof cover, ventilation with heat recovery, a heat pump, a heat
Optimization and control of metal forming processes
Havinga, Gosse Tjipke
2016-01-01
Inevitable variations in process and material properties limit the accuracy of metal forming processes. Robust optimization methods or control systems can be used to improve the production accuracy. Robust optimization methods are used to design production processes with low sensitivity to the
Optimal control and the calculus of variations
Pinch, Enid R
1993-01-01
This introduction to optimal control theory is intended for undergraduate mathematicians and for engineers and scientists with some knowledge of classical analysis. It includes sections on classical optimization and the calculus of variations. All the important theorems are carefully proved. There are many worked examples and exercises for the reader to attempt.
Direct Optimal Control of Duffing Dynamics
Oz, Hayrani; Ramsey, John K.
2002-01-01
The "direct control method" is a novel concept that is an attractive alternative and competitor to the differential-equation-based methods. The direct method is equally well applicable to nonlinear, linear, time-varying, and time-invariant systems. For all such systems, the method yields explicit closed-form control laws based on minimization of a quadratic control performance measure. We present an application of the direct method to the dynamics and optimal control of the Duffing system where the control performance measure is not restricted to a quadratic form and hence may include a quartic energy term. The results we present in this report also constitute further generalizations of our earlier work in "direct optimal control methodology." The approach is demonstrated for the optimal control of the Duffing equation with a softening nonlinear stiffness.
HCCI engine control and optimization
Killingsworth, Nicholas J.
2007-01-01
Homogeneous charge compression ignition (HCCI) engines have the benefit of high efficiency with low emissions of nitrogen oxides and particulates. These benefits are due to the autoignition process of the dilute mixture of fuel and air during compression. However, because there is no direct ignition trigger, control of ignition is inherently more difficult than in standard internal combustion engines. This difficulty necessitates that a feedback controller be used to keep the engine at a desi...
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels
2004-01-01
In the present work a framework for optimizing the design of boilers for dynamic operation has been developed. A cost function to be minimized during the optimization has been formulated and for the present design variables related to the Boiler Volume and the Boiler load Gradient (i.e. ring rate...... on the boiler) have been dened. Furthermore a number of constraints related to: minimum and maximum boiler load gradient, minimum boiler size, Shrinking and Swelling and Steam Space Load have been dened. For dening the constraints related to the required boiler volume a dynamic model for simulating the boiler...... performance has been developed. Outputs from the simulations are shrinking and swelling of water level in the drum during for example a start-up of the boiler, these gures combined with the requirements with respect to allowable water level uctuations in the drum denes the requirements with respect to drum...
Computer models for optimizing radiation therapy
International Nuclear Information System (INIS)
Duechting, W.
1998-01-01
The aim of this contribution is to outline how methods of system analysis, control therapy and modelling can be applied to simulate normal and malignant cell growth and to optimize cancer treatment as for instance radiation therapy. Based on biological observations and cell kinetic data, several types of models have been developed describing the growth of tumor spheroids and the cell renewal of normal tissue. The irradiation model is represented by the so-called linear-quadratic model describing the survival fraction as a function of the dose. Based thereon, numerous simulation runs for different treatment schemes can be performed. Thus, it is possible to study the radiation effect on tumor and normal tissue separately. Finally, this method enables a computer-assisted recommendation for an optimal patient-specific treatment schedule prior to clinical therapy. (orig.) [de
Multiobjective optimization of low impact development stormwater controls
Eckart, Kyle; McPhee, Zach; Bolisetti, Tirupati
2018-07-01
Green infrastructure such as Low Impact Development (LID) controls are being employed to manage the urban stormwater and restore the predevelopment hydrological conditions besides improving the stormwater runoff water quality. Since runoff generation and infiltration processes are nonlinear, there is a need for identifying optimal combination of LID controls. A coupled optimization-simulation model was developed by linking the U.S. EPA Stormwater Management Model (SWMM) to the Borg Multiobjective Evolutionary Algorithm (Borg MOEA). The coupled model is capable of performing multiobjective optimization which uses SWMM simulations as a tool to evaluate potential solutions to the optimization problem. The optimization-simulation tool was used to evaluate low impact development (LID) stormwater controls. A SWMM model was developed, calibrated, and validated for a sewershed in Windsor, Ontario and LID stormwater controls were tested for three different return periods. LID implementation strategies were optimized using the optimization-simulation model for five different implementation scenarios for each of the three storm events with the objectives of minimizing peak flow in the stormsewers, reducing total runoff, and minimizing cost. For the sewershed in Windsor, Ontario, the peak run off and total volume of the runoff were found to reduce by 13% and 29%, respectively.
Optimal control of inverted pendulum system using PID controller, LQR and MPC
Varghese, Elisa Sara; Vincent, Anju K.; Bagyaveereswaran, V.
2017-11-01
Inverted pendulum is a highly nonlinear system. Here we propose an optimal control technique for the control of an inverted Pendulum - cart system. The system is modeled, linearized and controlled. Here, the control objective is to control the system such that when the cart reaches a desired position the inverted pendulum stabilizes in the upright position. Initially PID controller is used to control the system. Later, Linear Quadratic Regulator (LQR) a well-known optimal control technique which makes use of the states of the dynamical system and control input to frame the optimal control decision is used. Various combinations of both PID and LQR controllers are implemented. To validate the robustness of the controller, the system is simulated with and without disturbance. Finally the system is also controlled using Model Predictive controller (MPC). MPC has well predictive ability to calculate future events and implement necessary control actions. The performance of the system is compared and analyzed.
Economics-based optimal control of greenhouse tomato crop production
Tap, F.
2000-01-01
The design and testing of an optimal control algorithm, based on scientific models of greenhouse and tomato crop and an economic criterion (goal function), to control greenhouse climate, is described. An important characteristic of this control is that it aims at maximising an economic
Optimal Excitation Controller Design for Wind Turbine Generator
Directory of Open Access Journals (Sweden)
A. K. Boglou
2011-01-01
Full Text Available An optimal excitation controller design based on multirate-output controllers (MROCs having a multirate sampling mechanismwith different sampling period in each measured output of the system is presented. The proposed H∞ -control techniqueis applied to the discrete linear open-loop system model which represents a wind turbine generator supplying an infinite busthrough a transmission line.
Numerical optimization of circulation control airfoils
Tai, T. C.; Kidwell, G. H., Jr.; Vanderplaats, G. N.
1981-01-01
A numerical procedure for optimizing circulation control airfoils, which consists of the coupling of an optimization scheme with a viscous potential flow analysis for blowing jet, is presented. The desired airfoil is defined by a combination of three baseline shapes (cambered ellipse, and cambered ellipse with drooped and spiralled trailing edges). The coefficients of these shapes are used as design variables in the optimization process. Under the constraints of lift augmentation and lift-to-drag ratios, the optimal airfoils are found to lie between those of cambered ellipse and the drooped trailing edge, towards the latter as the angle of attack increases. Results agree qualitatively with available experimental data.
Development and Optimization of controlled drug release ...
African Journals Online (AJOL)
The aim of this study is to develop and optimize an osmotically controlled drug delivery system of diclofenac sodium. Osmotically controlled oral drug delivery systems utilize osmotic pressure for controlled delivery of active drugs. Drug delivery from these systems, to a large extent, is independent of the physiological factors ...
Control and Optimization Methods for Electric Smart Grids
Ilić, Marija
2012-01-01
Control and Optimization Methods for Electric Smart Grids brings together leading experts in power, control and communication systems,and consolidates some of the most promising recent research in smart grid modeling,control and optimization in hopes of laying the foundation for future advances in this critical field of study. The contents comprise eighteen essays addressing wide varieties of control-theoretic problems for tomorrow’s power grid. Topics covered include: Control architectures for power system networks with large-scale penetration of renewable energy and plug-in vehicles Optimal demand response New modeling methods for electricity markets Control strategies for data centers Cyber-security Wide-area monitoring and control using synchronized phasor measurements. The authors present theoretical results supported by illustrative examples and practical case studies, making the material comprehensible to a wide audience. The results reflect the exponential transformation that today’s grid is going...
5th International Conference on Optimization and Control with Applications
Teo, Kok; Zhang, Yi
2014-01-01
This book presents advances in state-of-the-art solution methods and their applications to real life practical problems in optimization, control and operations research. Contributions from world-class experts in the field are collated here in two parts, dealing first with optimization and control theory and then with techniques and applications. Topics covered in the first part include control theory on infinite dimensional Banach spaces, history-dependent inclusion and linear programming complexity theory. Chapters also explore the use of approximations of Hamilton-Jacobi-Bellman inequality for solving periodic optimization problems and look at multi-objective semi-infinite optimization problems, and production planning problems. In the second part, the authors address techniques and applications of optimization and control in a variety of disciplines, such as chaos synchronization, facial expression recognition and dynamic input-output economic models. Other applications considered here include image retr...
Ke, Chih-Kun; Lin, Zheng-Hua
2015-09-01
The progress of information and communication technologies (ICT) has promoted the development of healthcare which has enabled the exchange of resources and services between organizations. Organizations want to integrate mobile devices into their hospital information systems (HIS) due to the convenience to employees who are then able to perform specific healthcare processes from any location. The collection and merage of healthcare data from discrete mobile devices are worth exploring possible ways for further use, especially in remote districts without public data network (PDN) to connect the HIS. In this study, we propose an optimal mobile service which automatically synchronizes the telecare file resources among discrete mobile devices. The proposed service enforces some technical methods. The role-based access control model defines the telecare file resources accessing mechanism; the symmetric data encryption method protects telecare file resources transmitted over a mobile peer-to-peer network. The multi-criteria decision analysis method, ELECTRE (Elimination Et Choice Translating Reality), evaluates multiple criteria of the candidates' mobile devices to determine a ranking order. This optimizes the synchronization of telecare file resources among discrete mobile devices. A prototype system is implemented to examine the proposed mobile service. The results of the experiment show that the proposed mobile service can automatically and effectively synchronize telecare file resources among discrete mobile devices. The contribution of this experiment is to provide an optimal mobile service that enhances the security of telecare file resource synchronization and strengthens an organization's mobility.
Hierarchical models and iterative optimization of hybrid systems
Energy Technology Data Exchange (ETDEWEB)
Rasina, Irina V. [Ailamazyan Program Systems Institute, Russian Academy of Sciences, Peter One str. 4a, Pereslavl-Zalessky, 152021 (Russian Federation); Baturina, Olga V. [Trapeznikov Control Sciences Institute, Russian Academy of Sciences, Profsoyuznaya str. 65, 117997, Moscow (Russian Federation); Nasatueva, Soelma N. [Buryat State University, Smolina str.24a, Ulan-Ude, 670000 (Russian Federation)
2016-06-08
A class of hybrid control systems on the base of two-level discrete-continuous model is considered. The concept of this model was proposed and developed in preceding works as a concretization of the general multi-step system with related optimality conditions. A new iterative optimization procedure for such systems is developed on the base of localization of the global optimality conditions via contraction the control set.
Euler's fluid equations: Optimal control vs optimization
Energy Technology Data Exchange (ETDEWEB)
Holm, Darryl D., E-mail: d.holm@ic.ac.u [Department of Mathematics, Imperial College London, SW7 2AZ (United Kingdom)
2009-11-23
An optimization method used in image-processing (metamorphosis) is found to imply Euler's equations for incompressible flow of an inviscid fluid, without requiring that the Lagrangian particle labels exactly follow the flow lines of the Eulerian velocity vector field. Thus, an optimal control problem and an optimization problem for incompressible ideal fluid flow both yield the same Euler fluid equations, although their Lagrangian parcel dynamics are different. This is a result of the gauge freedom in the definition of the fluid pressure for an incompressible flow, in combination with the symmetry of fluid dynamics under relabeling of their Lagrangian coordinates. Similar ideas are also illustrated for SO(N) rigid body motion.
Optimal Wentzell Boundary Control of Parabolic Equations
International Nuclear Information System (INIS)
Luo, Yousong
2017-01-01
This paper deals with a class of optimal control problems governed by an initial-boundary value problem of a parabolic equation. The case of semi-linear boundary control is studied where the control is applied to the system via the Wentzell boundary condition. The differentiability of the state variable with respect to the control is established and hence a necessary condition is derived for the optimal solution in the case of both unconstrained and constrained problems. The condition is also sufficient for the unconstrained convex problems. A second order condition is also derived.
Optimal Wentzell Boundary Control of Parabolic Equations
Energy Technology Data Exchange (ETDEWEB)
Luo, Yousong, E-mail: yousong.luo@rmit.edu.au [RMIT University, School of Mathematical and Geospatial Sciences (Australia)
2017-04-15
This paper deals with a class of optimal control problems governed by an initial-boundary value problem of a parabolic equation. The case of semi-linear boundary control is studied where the control is applied to the system via the Wentzell boundary condition. The differentiability of the state variable with respect to the control is established and hence a necessary condition is derived for the optimal solution in the case of both unconstrained and constrained problems. The condition is also sufficient for the unconstrained convex problems. A second order condition is also derived.
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, K.; Condra, T.; Houbak, Niels
2003-01-01
, and the total stress level (i.e. stresses introduced due to internal pressure plus stresses introduced due to temperature gradients) must always be kept below the allowable stress level. In this way, the increased water-/steam space that should allow for better dynamic performance, in the end causes limited...... freedom with respect to dynamic operation of the plant. By means of an objective function including as well the price of the plant as a quantification of the value of dynamic operation of the plant an optimization is carried out. The dynamic model of the boiler plant is applied to define parts...
Optimal control problem for the extended Fisher–Kolmogorov equation
Indian Academy of Sciences (India)
In this paper, the optimal control problem for the extended Fisher–Kolmogorov equation is studied. The optimal control under boundary condition is given, the existence of optimal solution to the equation is proved and the optimality system is established.
Energy Optimal Control of Induction Motor Drives
DEFF Research Database (Denmark)
Abrahamsen, Flemming
This thesis deals with energy optimal control of small and medium-size variable speed induction motor drives for especially Heating, Ventilation and Air-Condition (HVAC) applications. Optimized efficiency is achieved by adapting the magnetization level in the motor to the load, and the basic...... demonstrated that energy optimal control will sometimes improve and sometimes deteriorate the stability. Comparison of small and medium-size induction motor drives with permanent magnet motor drives indicated why, and in which applications, PM motors are especially good. Calculations of economical aspects...... improvement by energy optimal control for any standard induction motor drive between 2.2 kW and 90 kW. A simple method to evaluate the robustness against load disturbances was developed and used to compare the robustness of different motor types and sizes. Calculation of the oscillatory behavior of a motor...
Optimizing pipeline transportation using a fuzzy controller
Energy Technology Data Exchange (ETDEWEB)
Aramaki, Thiago L.; Correa, Joao L. L.; Montalvoa, Antonio F. F. [National Control and Operation Center Tranpetro, Rio de Janeiro, (Brazil)
2010-07-01
The optimization of pipeline transportation is a big concern for the transporter companies. This paper is the third of a series of three articles which investigated the application of a system to simulate the human ability to operate a pipeline in an optimized way. The present paper presents the development of a proportional integral (PI) fuzzy controller, in order to optimize pipeline transportation capacity. The fuzzy adaptive PI controller system was developed and tested with a hydraulic simulator. On-field data were used from the OSBRA pipeline. The preliminary tests showed that the performance of the software simulation was satisfactory. It varied the set-point of the conventional controller within the limits of flow meters. The transport capacity of the pipe was maximize without compromising the integrity of the commodities transported. The system developed proved that it can be easily deployed as a specialist optimizing system to be added to SCADA systems.
Integrated modeling of ozonation for optimization of drinking water treatment
van der Helm, A.W.C.
2007-01-01
Drinking water treatment plants automation becomes more sophisticated, more on-line monitoring systems become available and integration of modeling environments with control systems becomes easier. This gives possibilities for model-based optimization. In operation of drinking water treatment
Turnpike phenomenon and infinite horizon optimal control
Zaslavski, Alexander J
2014-01-01
This book is devoted to the study of the turnpike phenomenon and describes the existence of solutions for a large variety of infinite horizon optimal control classes of problems. Chapter 1 provides introductory material on turnpike properties. Chapter 2 studies the turnpike phenomenon for discrete-time optimal control problems. The turnpike properties of autonomous problems with extended-value intergrands are studied in Chapter 3. Chapter 4 focuses on large classes of infinite horizon optimal control problems without convexity (concavity) assumptions. In Chapter 5, the turnpike results for a class of dynamic discrete-time two-player zero-sum game are proven. This thorough exposition will be very useful for mathematicians working in the fields of optimal control, the calculus of variations, applied functional analysis, and infinite horizon optimization. It may also be used as a primary text in a graduate course in optimal control or as supplementary text for a variety of courses in other disciplines. Resea...
Optimal control of a CSTR process
Directory of Open Access Journals (Sweden)
A. Soukkou
2008-12-01
Full Text Available Designing an effective criterion and learning algorithm for find the best structure is a major problem in the control design process. In this paper, the fuzzy optimal control methodology is applied to the design of the feedback loops of an Exothermic Continuous Stirred Tank Reactor system. The objective of design process is to find an optimal structure/gains of the Robust and Optimal Takagi Sugeno Fuzzy Controller (ROFLC. The control signal thus obtained will minimize a performance index, which is a function of the tracking/regulating errors, the quantity of the energy of the control signal applied to the system, and the number of fuzzy rules. The genetic learning is proposed for constructing the ROFLC. The chromosome genes are arranged into two parts, the binary-coded part contains the control genes and the real-coded part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the ROFLC are compared to these found by the traditional PD controller with Genetic Optimization (PD_GO. Simulations demonstrate that the proposed ROFLC and PD_GO has successfully met the design specifications.
Galerkin approximations of nonlinear optimal control problems in Hilbert spaces
Directory of Open Access Journals (Sweden)
Mickael D. Chekroun
2017-07-01
Full Text Available Nonlinear optimal control problems in Hilbert spaces are considered for which we derive approximation theorems for Galerkin approximations. Approximation theorems are available in the literature. The originality of our approach relies on the identification of a set of natural assumptions that allows us to deal with a broad class of nonlinear evolution equations and cost functionals for which we derive convergence of the value functions associated with the optimal control problem of the Galerkin approximations. This convergence result holds for a broad class of nonlinear control strategies as well. In particular, we show that the framework applies to the optimal control of semilinear heat equations posed on a general compact manifold without boundary. The framework is then shown to apply to geoengineering and mitigation of greenhouse gas emissions formulated here in terms of optimal control of energy balance climate models posed on the sphere $\\mathbb{S}^2$.
Hinnen, K.; Verhaegen, M.; Doelman, N.
2005-01-01
Even though the wavefront distortion introduced by atmospheric turbulence is a dynamic process, its temporal evolution is usually neglected in the adaptive optics (AO) control design. Most AO control systems consider only the spatial correlation in a separate wavefront reconstruction step. By
Assuring robustness to noise in optimal quantum control experiments
International Nuclear Information System (INIS)
Bartelt, A.F.; Roth, M.; Mehendale, M.; Rabitz, H.
2005-01-01
Closed-loop optimal quantum control experiments operate in the inherent presence of laser noise. In many applications, attaining high quality results [i.e., a high signal-to-noise (S/N) ratio for the optimized objective] is as important as producing a high control yield. Enhancement of the S/N ratio will typically be in competition with the mean signal, however, the latter competition can be balanced by biasing the optimization experiments towards higher mean yields while retaining a good S/N ratio. Other strategies can also direct the optimization to reduce the standard deviation of the statistical signal distribution. The ability to enhance the S/N ratio through an optimized choice of the control is demonstrated for two condensed phase model systems: second harmonic generation in a nonlinear optical crystal and stimulated emission pumping in a dye solution
Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty
Nguyen, Nhan T.
2012-01-01
This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.
An example in linear quadratic optimal control
Weiss, George; Zwart, Heiko J.
1998-01-01
We construct a simple example of a quadratic optimal control problem for an infinite-dimensional linear system based on a shift semigroup. This system has an unbounded control operator. The cost is quadratic in the input and the state, and the weighting operators are bounded. Despite its extreme
Stochastic optimal control of single neuron spike trains
DEFF Research Database (Denmark)
Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë
2014-01-01
stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic...... origin. In particular, we allow for the noise to be of arbitrary intensity. The optimal control problem is solved using dynamic programming when the controller has access to the voltage (closed-loop control), and using a maximum principle for the transition density when the controller only has access...... to the spike times (open-loop control). Main results. We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy...
Process control and optimization with simple interval calculation method
DEFF Research Database (Denmark)
Pomerantsev, A.; Rodionova, O.; Høskuldsson, Agnar
2006-01-01
for the quality improvement in the course of production. The latter is an active quality optimization, which takes into account the actual history of the process. The advocate approach is allied to the conventional method of multivariate statistical process control (MSPC) as it also employs the historical process......Methods of process control and optimization are presented and illustrated with a real world example. The optimization methods are based on the PLS block modeling as well as on the simple interval calculation methods of interval prediction and object status classification. It is proposed to employ...... the series of expanding PLS/SIC models in order to support the on-line process improvements. This method helps to predict the effect of planned actions on the product quality and thus enables passive quality control. We have also considered an optimization approach that proposes the correcting actions...
Optimal Energy Control Strategy Design for a Hybrid Electric Vehicle
Directory of Open Access Journals (Sweden)
Yuan Zou
2013-01-01
Full Text Available A heavy-duty parallel hybrid electric truck is modeled, and its optimal energy control is studied in this paper. The fundamental architecture of the parallel hybrid electric truck is modeled feed-forwardly, together with necessary dynamic features of subsystem or components. Dynamic programming (DP technique is adopted to find the optimal control strategy including the gear-shifting sequence and the power split between the engine and the motor subject to a battery SOC-sustaining constraint. Improved control rules are extracted from the DP-based control solution, forming near-optimal control strategies. Simulation results demonstrate that a significant improvement on the fuel economy can be achieved in the heavy-duty vehicle cycle from the natural driving statistics.
On Optimal Input Design for Feed-forward Control
Hägg, Per; Wahlberg, Bo
2013-01-01
This paper considers optimal input design when the intended use of the identified model is to construct a feed-forward controller based on measurable disturbances. The objective is to find a minimum power excitation signal to be used in a system identification experiment, such that the corresponding model-based feed-forward controller guarantees, with a given probability, that the variance of the output signal is within given specifications. To start with, some low order model problems are an...
Optimal Control Surface Layout for an Aeroservoelastic Wingbox
Stanford, Bret K.
2017-01-01
This paper demonstrates a technique for locating the optimal control surface layout of an aeroservoelastic Common Research Model wingbox, in the context of maneuver load alleviation and active utter suppression. The combinatorial actuator layout design is solved using ideas borrowed from topology optimization, where the effectiveness of a given control surface is tied to a layout design variable, which varies from zero (the actuator is removed) to one (the actuator is retained). These layout design variables are optimized concurrently with a large number of structural wingbox sizing variables and control surface actuation variables, in order to minimize the sum of structural weight and actuator weight. Results are presented that demonstrate interdependencies between structural sizing patterns and optimal control surface layouts, for both static and dynamic aeroelastic physics.
An introduction to optimal control of FBSDE with incomplete information
Wang, Guangchen; Xiong, Jie
2018-01-01
This book focuses on maximum principle and verification theorem for incomplete information forward-backward stochastic differential equations (FBSDEs) and their applications in linear-quadratic optimal controls and mathematical finance. Lots of interesting phenomena arising from the area of mathematical finance can be described by FBSDEs. Optimal control problems of FBSDEs are theoretically important and practically relevant. A standard assumption in the literature is that the stochastic noises in the model are completely observed. However, this is rarely the case in real world situations. The optimal control problems under complete information are studied extensively. Nevertheless, very little is known about these problems when the information is not complete. The aim of this book is to fill this gap. This book is written in a style suitable for graduate students and researchers in mathematics and engineering with basic knowledge of stochastic process, optimal control and mathematical finance.
Verification and Optimization of a PLC Control Schedule
Brinksma, Hendrik; Mader, Angelika H.; Havelund, K.; Penix, J.; Visser, W.
We report on the use of the SPIN model checker for both the verification of a process control program and the derivation of optimal control schedules. This work was carried out as part of a case study for the EC VHS project (Verification of Hybrid Systems), in which the program for a Programmable
Optimal control applications in electric power systems
Christensen, G S; Soliman, S A
1987-01-01
Significant advances in the field of optimal control have been made over the past few decades. These advances have been well documented in numerous fine publications, and have motivated a number of innovations in electric power system engineering, but they have not yet been collected in book form. Our purpose in writing this book is to provide a description of some of the applications of optimal control techniques to practical power system problems. The book is designed for advanced undergraduate courses in electric power systems, as well as graduate courses in electrical engineering, applied mathematics, and industrial engineering. It is also intended as a self-study aid for practicing personnel involved in the planning and operation of electric power systems for utilities, manufacturers, and consulting and government regulatory agencies. The book consists of seven chapters. It begins with an introductory chapter that briefly reviews the history of optimal control and its power system applications and also p...
2016 Network Games, Control, and Optimization Conference
Jimenez, Tania; Solan, Eilon
2017-01-01
This contributed volume offers a collection of papers presented at the 2016 Network Games, Control, and Optimization conference (NETGCOOP), held at the University of Avignon in France, November 23-25, 2016. These papers highlight the increasing importance of network control and optimization in many networking application domains, such as mobile and fixed access networks, computer networks, social networks, transportation networks, and, more recently, electricity grids and biological networks. Covering a wide variety of both theoretical and applied topics in the areas listed above, the authors explore several conceptual and algorithmic tools that are needed for efficient and robust control operation, performance optimization, and better understanding the relationships between entities that may be acting cooperatively or selfishly in uncertain and possibly adversarial environments. As such, this volume will be of interest to applied mathematicians, computer scientists, engineers, and researchers in other relate...
Time-optimal control of reactor power
International Nuclear Information System (INIS)
Bernard, J.A.
1987-01-01
Control laws that permit adjustments in reactor power to be made in minimum time and without overshoot have been formulated and demonstrated. These control laws which are derived from the standard and alternate dynamic period equations, are closed-form expressions of general applicability. These laws were deduced by noting that if a system is subject to one or more operating constraints, then the time-optimal response is to move the system along these constraints. Given that nuclear reactors are subject to limitations on the allowed reactor period, a time-optimal control law would step the period from infinity to the minimum allowed value, hold the period at that value for the duration of the transient, and then step the period back to infinity. The change in reactor would therefore be accomplished in minimum time. The resulting control laws are superior to other forms of time-optimal control because they are general-purpose, closed-form expressions that are both mathematically tractable and readily implanted. Moreover, these laws include provisions for the use of feedback. The results of simulation studies and actual experiments on the 5 MWt MIT Research Reactor in which these time-optimal control laws were used successfully to adjust the reactor power are presented
Optimal control of epidemic information dissemination over networks.
Chen, Pin-Yu; Cheng, Shin-Ming; Chen, Kwang-Cheng
2014-12-01
Information dissemination control is of crucial importance to facilitate reliable and efficient data delivery, especially in networks consisting of time-varying links or heterogeneous links. Since the abstraction of information dissemination much resembles the spread of epidemics, epidemic models are utilized to characterize the collective dynamics of information dissemination over networks. From a systematic point of view, we aim to explore the optimal control policy for information dissemination given that the control capability is a function of its distribution time, which is a more realistic model in many applications. The main contributions of this paper are to provide an analytically tractable model for information dissemination over networks, to solve the optimal control signal distribution time for minimizing the accumulated network cost via dynamic programming, and to establish a parametric plug-in model for information dissemination control. In particular, we evaluate its performance in mobile and generalized social networks as typical examples.
Westerhuis, J.A; Coenegracht, P.M J; Lerk, C.F
1997-01-01
The process of tablet manufacturing with granulation is described as a two-step process. The first step comprises wet granulation of the powder mixture, and in the second step the granules are compressed into tablets. For the modelling of the pharmaceutical process of wet granulation and tableting,
Model Based Control of Refrigeration Systems
DEFF Research Database (Denmark)
Larsen, Lars Finn Sloth
for automation of these procedures, that is to incorporate some "intelligence" in the control system, this project was started up. The main emphasis of this work has been on model based methods for system optimizing control in supermarket refrigeration systems. The idea of implementing a system optimizing...... control is to let an optimization procedure take over the task of operating the refrigeration system and thereby replace the role of the operator in the traditional control structure. In the context of refrigeration systems, the idea is to divide the optimizing control structure into two parts: A part...... optimizing the steady state operation "set-point optimizing control" and a part optimizing dynamic behaviour of the system "dynamical optimizing control". A novel approach for set-point optimization will be presented. The general idea is to use a prediction of the steady state, for computation of the cost...
Moser, Elke; Grass, Dieter; Tragler, Gernot
Given the constantly raising world-wide energy demand and the accompanying increase in greenhouse gas emissions that pushes the progression of climate change, the possibly most important task in future is to find a carbon-low energy supply that finds the right balance between sustainability and energy security. For renewable energy generation, however, especially the second aspect turns out to be difficult as the supply of renewable sources underlies strong volatility. Further on, investment costs for new technologies are so high that competitiveness with conventional energy forms is hard to achieve. To address this issue, we analyze in this paper a non-autonomous optimal control model considering the optimal composition of a portfolio that consists of fossil and renewable energy and which is used to cover the energy demand of a small country. While fossil energy is assumed to be constantly available, the supply of the renewable resource fluctuates seasonally. We further on include learning effects for the renewable energy technology, which will underline the importance of considering the whole life span of such a technology for long-term energy planning decisions.
ECONOMIC MODELING STOCKS CONTROL SYSTEM: SIMULATION MODEL
Климак, М.С.; Войтко, С.В.
2016-01-01
Considered theoretical and applied aspects of the development of simulation models to predictthe optimal development and production systems that create tangible products andservices. It isproved that theprocessof inventory control needs of economicandmathematical modeling in viewof thecomplexity of theoretical studies. A simulation model of stocks control that allows make managementdecisions with production logistics
Alam, Fahad; Boet, Sylvain; Piquette, Dominique; Lai, Anita; Perkes, Christopher P; LeBlanc, Vicki R
2016-10-01
Enhanced podcasts increase learning, but evidence is lacking on how they should be designed to optimize their effectiveness. This study assessed the impact two learning instructional design methods (mental practice and modeling), either on their own or in combination, for teaching complex cognitive medical content when incorporated into enhanced podcasts. Sixty-three medical students were randomised to one of four versions of an airway management enhanced podcast: (1) control: narrated presentation; (2) modeling: narration with video demonstration of skills; (3) mental practice: narrated presentation with guided mental practice; (4) combined: modeling and mental practice. One week later, students managed a manikin-based simulated airway crisis. Knowledge acquisition was assessed by baseline and retention multiple-choice quizzes. Two blinded raters assessed all videos obtained from simulated crises to measure the students' skills using a key-elements scale, critical error checklist, and the Ottawa global rating scale (GRS). Baseline knowledge was not different between all four groups (p = 0.65). One week later, knowledge retention was significantly higher for (1) both the mental practice and modeling group than the control group (p = 0.01; p = 0.01, respectively) and (2) the combined mental practice and modeling group compared to all other groups (all ps = 0.01). Regarding skills acquisition, the control group significantly under-performed in comparison to all other groups on the key-events scale (all ps ≤ 0.05), the critical error checklist (all ps ≤ 0.05), and the Ottawa GRS (all ps ≤ 0.05). The combination of mental practice and modeling led to greater improvement on the key events checklist (p = 0.01) compared to either strategy alone. However, the combination of the two strategies did not result in any further learning gains on the two other measures of clinical performance (all ps > 0.05). The effectiveness of enhanced podcasts for
International Nuclear Information System (INIS)
Medeiros, Jose Antonio Carlos Canedo; Machado, Marcelo Dornellas; Lima, Alan Miranda M. de; Schirru, Roberto
2007-01-01
Predictive control systems are control systems that use a model of the controlled system (plant), used to predict the future behavior of the plant allowing the establishment of an anticipative control based on a future condition of the plant, and an optimizer that, considering a future time horizon of the plant output and a recent horizon of the control action, determines the controller's outputs to optimize a performance index of the controlled plant. The predictive control system does not require analytical models of the plant; the model of predictor of the plant can be learned from historical data of operation of the plant. The optimizer of the predictive controller establishes the strategy of the control: the minimization of a performance index (objective function) is done so that the present and future control actions are computed in such a way to minimize the objective function. The control strategy, implemented by the optimizer, induces the formation of an optimal control mechanism whose effect is to reduce the stabilization time, the 'overshoot' and 'undershoot', minimize the control actuation so that a compromise among those objectives is attained. The optimizer of the predictive controller is usually implemented using gradient-based algorithms. In this work we use the Particle Swarm Optimization algorithm (PSO) in the optimizer component of a predictive controller applied in the control of the xenon oscillation of a pressurized water reactor (PWR). The PSO is a stochastic optimization technique applied in several disciplines, simple and capable of providing a global optimal for high complexity problems and difficult to be optimized, providing in many cases better results than those obtained by other conventional and/or other artificial optimization techniques. (author)
Optimal design of distributed control and embedded systems
Çela, Arben; Li, Xu-Guang; Niculescu, Silviu-Iulian
2014-01-01
Optimal Design of Distributed Control and Embedded Systems focuses on the design of special control and scheduling algorithms based on system structural properties as well as on analysis of the influence of induced time-delay on systems performances. It treats the optimal design of distributed and embedded control systems (DCESs) with respect to communication and calculation-resource constraints, quantization aspects, and potential time-delays induced by the associated communication and calculation model. Particular emphasis is put on optimal control signal scheduling based on the system state. In order to render this complex optimization problem feasible in real time, a time decomposition is based on periodicity induced by the static scheduling is operated. The authors present a co-design approach which subsumes the synthesis of the optimal control laws and the generation of an optimal schedule of control signals on real-time networks as well as the execution of control tasks on a single processor. The a...
Optimal Acquisition and Inventory Control for a Remanufacturing System
Directory of Open Access Journals (Sweden)
Zhigang Jiang
2013-01-01
Full Text Available Optimal acquisition and inventory control can often make the difference between successful and unsuccessful remanufacturing. However, there is a greater degree of uncertainty and complexity in a remanufacturing system, which leads to a critical need for planning and control models designed to deal with this added uncertainty and complexity. In this paper, a method for optimal acquisition and inventory control of a remanufacturing system is presented. The method considers three inventories, one for returned item and the other for serviceable and recoverable items. Taking the holding cost for returns, recoverable and remanufactured products, remanufacturing cost, disposal cost, and the loss caused by backlog into account, the optimal inventory control model is established to minimize the total costs. Finally, a numerical example is provided to illustrate the proposed methods.
Optimal Control for Stochastic Delay Evolution Equations
Energy Technology Data Exchange (ETDEWEB)
Meng, Qingxin, E-mail: mqx@hutc.zj.cn [Huzhou University, Department of Mathematical Sciences (China); Shen, Yang, E-mail: skyshen87@gmail.com [York University, Department of Mathematics and Statistics (Canada)
2016-08-15
In this paper, we investigate a class of infinite-dimensional optimal control problems, where the state equation is given by a stochastic delay evolution equation with random coefficients, and the corresponding adjoint equation is given by an anticipated backward stochastic evolution equation. We first prove the continuous dependence theorems for stochastic delay evolution equations and anticipated backward stochastic evolution equations, and show the existence and uniqueness of solutions to anticipated backward stochastic evolution equations. Then we establish necessary and sufficient conditions for optimality of the control problem in the form of Pontryagin’s maximum principles. To illustrate the theoretical results, we apply stochastic maximum principles to study two examples, an infinite-dimensional linear-quadratic control problem with delay and an optimal control of a Dirichlet problem for a stochastic partial differential equation with delay. Further applications of the two examples to a Cauchy problem for a controlled linear stochastic partial differential equation and an optimal harvesting problem are also considered.
Optimal control of large space structures via generalized inverse matrix
Nguyen, Charles C.; Fang, Xiaowen
1987-01-01
Independent Modal Space Control (IMSC) is a control scheme that decouples the space structure into n independent second-order subsystems according to n controlled modes and controls each mode independently. It is well-known that the IMSC eliminates control and observation spillover caused when the conventional coupled modal control scheme is employed. The independent control of each mode requires that the number of actuators be equal to the number of modelled modes, which is very high for a faithful modeling of large space structures. A control scheme is proposed that allows one to use a reduced number of actuators to control all modeled modes suboptimally. In particular, the method of generalized inverse matrices is employed to implement the actuators such that the eigenvalues of the closed-loop system are as closed as possible to those specified by the optimal IMSC. Computer simulation of the proposed control scheme on a simply supported beam is given.
Optimization and Control of Electric Power Systems
Energy Technology Data Exchange (ETDEWEB)
Lesieutre, Bernard C. [Univ. of Wisconsin, Madison, WI (United States); Molzahn, Daniel K. [Univ. of Wisconsin, Madison, WI (United States)
2014-10-17
The analysis and optimization needs for planning and operation of the electric power system are challenging due to the scale and the form of model representations. The connected network spans the continent and the mathematical models are inherently nonlinear. Traditionally, computational limits have necessitated the use of very simplified models for grid analysis, and this has resulted in either less secure operation, or less efficient operation, or both. The research conducted in this project advances techniques for power system optimization problems that will enhance reliable and efficient operation. The results of this work appear in numerous publications and address different application problems include optimal power flow (OPF), unit commitment, demand response, reliability margins, planning, transmission expansion, as well as general tools and algorithms.
Infinite-horizon optimal control problems in economics
Energy Technology Data Exchange (ETDEWEB)
Aseev, Sergei M; Besov, Konstantin O; Kryazhimskii, Arkadii V
2012-04-30
This paper extends optimal control theory to a class of infinite-horizon problems that arise in studying models of optimal dynamic allocation of economic resources. In a typical problem of this sort the initial state is fixed, no constraints are imposed on the behaviour of the admissible trajectories at large times, and the objective functional is given by a discounted improper integral. We develop the method of finite-horizon approximations in a broad context and use it to derive complete versions of the Pontryagin maximum principle for such problems. We provide sufficient conditions for the normality of infinite-horizon optimal control problems and for the validity of the 'standard' limit transversality conditions with time going to infinity. As a meaningful example, we consider a new two-sector model of optimal economic growth subject to a random jump in prices. Bibliography: 53 titles.
Infinite-horizon optimal control problems in economics
International Nuclear Information System (INIS)
Aseev, Sergei M; Besov, Konstantin O; Kryazhimskii, Arkadii V
2012-01-01
This paper extends optimal control theory to a class of infinite-horizon problems that arise in studying models of optimal dynamic allocation of economic resources. In a typical problem of this sort the initial state is fixed, no constraints are imposed on the behaviour of the admissible trajectories at large times, and the objective functional is given by a discounted improper integral. We develop the method of finite-horizon approximations in a broad context and use it to derive complete versions of the Pontryagin maximum principle for such problems. We provide sufficient conditions for the normality of infinite-horizon optimal control problems and for the validity of the 'standard' limit transversality conditions with time going to infinity. As a meaningful example, we consider a new two-sector model of optimal economic growth subject to a random jump in prices. Bibliography: 53 titles.
Directory of Open Access Journals (Sweden)
Yuan Wang
2015-01-01
Full Text Available Our work is devoted to a class of optimal control problems of parabolic partial differential equations. Because of the partial differential equations constraints, it is rather difficult to solve the optimization problem. The gradient of the cost function can be found by the adjoint problem approach. Based on the adjoint problem approach, the gradient of cost function is proved to be Lipschitz continuous. An improved conjugate method is applied to solve this optimization problem and this algorithm is proved to be convergent. This method is applied to set-point values in continuous cast secondary cooling zone. Based on the real data in a plant, the simulation experiments show that the method can ensure the steel billet quality. From these experiment results, it is concluded that the improved conjugate gradient algorithm is convergent and the method is effective in optimal control problem of partial differential equations.
International Nuclear Information System (INIS)
Chen, Yizhong; He, Li; Li, Jing; Cheng, Xi; Lu, Hongwei
2016-01-01
Highlights: • Detailed model developed for power generation and pollutants mitigation. • Dynamic integration of bi-level programming with uncertainty analyses. • Application of the novel bi-level model for EPS in Fengtai District. • Development of renewable energy under different probability levels. - Abstract: In this study, an IBSOM (inexact bi-level simulation–optimization model) is developed for conjunctive regional renewable energy planning and air pollution control for EPS (electric power systems) under uncertainty. The IBSOM integrates techniques of CFMTVW (combined forecasting model with time-varying weights), ILP (interval linear programming), MIP (mixed integer programming), CCP (chance-constrained programming), as well as BLP (bi-level programming) into a general framework. In the IBSOM, uncertainties expressed as interval and stochastic parameters within multi-period and multi-option contexts can be effectively tackled. In addition, a leader-follower decision strategy is incorporated into the optimization process where two non-competitive objectives are sequentially proposed, with the environmental sector dominating the upper-level objective (leader’s one) and the energy sector providing the lower-level objective (follower’s one). To solve the proposed model, an improved bi-level interactive solution algorithm based on satisfactory degree is introduced into the decision-making process for balancing to what extent the constraints are met and the objective reaches its optima. Then, the IBSOM is applied to a real-world case study of EPS in Fengtai District, Beijing, China. Interval solutions associated with renewable energy development, electricity generation, facility-expansion scheme, as well as pollutants mitigation can be obtained under different system-violation risk. Results indicate that a higher violation risk would lead to a decreased strictness of the constraints or an expanded decision space, which results in the decreased system
Robust Optimal Adaptive Control Method with Large Adaptive Gain
Nguyen, Nhan T.
2009-01-01
In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient stability robustness. Simulations were conducted for a damaged generic transport aircraft with both standard adaptive control and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model while maintaining a sufficient time delay margin.
Optimal Control of Wind Power Generation
Directory of Open Access Journals (Sweden)
Pawel Pijarski
2018-03-01
Full Text Available Power system control is a complex task, which is strongly related to the number and kind of generating units as well as to the applied technologies, such as conventional coal fired power plants or wind and photovoltaic farms. Fast development of wind generation that is considered as unstable generation sets new strong requirements concerning remote control and data hubs cooperating with SCADA systems. Considering specific nature of the wind power generation, the authors analyze the problem of optimal control for wind power generation in farms located over a selected remote-controlled part of the Operator grid under advantageous wind conditions. This article presents an original stepwise method for tracing power flows that makes possible to eliminate current (power overloading of power grid branches. Its core idea is to consider the discussed problem as an optimization task.
Optimal traffic control in highway transportation networks using linear programming
Li, Yanning
2014-06-01
This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.
Efficient solution method for optimal control of nuclear systems
International Nuclear Information System (INIS)
Naser, J.A.; Chambre, P.L.
1981-01-01
To improve the utilization of existing fuel sources, the use of optimization techniques is becoming more important. A technique for solving systems of coupled ordinary differential equations with initial, boundary, and/or intermediate conditions is given. This method has a number of inherent advantages over existing techniques as well as being efficient in terms of computer time and space requirements. An example of computing the optimal control for a spatially dependent reactor model with and without temperature feedback is given. 10 refs
Augmented Lagrangian Method For Discretized Optimal Control ...
African Journals Online (AJOL)
In this paper, we are concerned with one-dimensional time invariant optimal control problem, whose objective function is quadratic and the dynamical system is a differential equation with initial condition .Since most real life problems are nonlinear and their analytical solutions are not readily available, we resolve to ...
Optimally Controlled Flexible Fuel Powertrain System
Energy Technology Data Exchange (ETDEWEB)
Hakan Yilmaz; Mark Christie; Anna Stefanopoulou
2010-12-31
The primary objective of this project was to develop a true Flex Fuel Vehicle capable of running on any blend of ethanol from 0 to 85% with reduced penalty in usable vehicle range. A research and development program, targeting 10% improvement in fuel economy using a direct injection (DI) turbocharged spark ignition engine was conducted. In this project a gasoline-optimized high-technology engine was considered and the hardware and configuration modifications were defined for the engine, fueling system, and air path. Combined with a novel engine control strategy, control software, and calibration this resulted in a highly efficient and clean FFV concept. It was also intended to develop robust detection schemes of the ethanol content in the fuel integrated with adaptive control algorithms for optimized turbocharged direct injection engine combustion. The approach relies heavily on software-based adaptation and optimization striving for minimal modifications to the gasoline-optimized engine hardware system. Our ultimate objective was to develop a compact control methodology that takes advantage of any ethanol-based fuel mixture and not compromise the engine performance under gasoline operation.
Hybrid vehicle energy management: singular optimal control
Delprat, S.; Hofman, T.; Paganelli, S.
2017-01-01
Hybrid vehicle energymanagement is often studied in simulation as an optimal control problem. Under strict convexity assumptions, a solution can be developed using Pontryagin’s minimum principle. In practice, however, many engineers do not formally check these assumptions resulting in the possible
Optimal control design for a solar greenhouse
Ooteghem, van R.J.C.
2007-01-01
The research of this thesis was part of a larger project aiming at the design of a greenhouse and an associated climate control that achieves optimal crop production with sustainable instead of fossil energy. This so called solar greenhouse design extends a conventional greenhouse with an improved
Efficient evolutionary algorithms for optimal control
López Cruz, I.L.
2002-01-01
If optimal control problems are solved by means of gradient based local search methods, convergence to local solutions is likely. Recently, there has been an increasing interest in the use
Optimization and Development of Swellable Controlled Porosity ...
African Journals Online (AJOL)
Purpose: To develop swellable controlled porosity osmotic pump tablet of theophylline and to define the formulation and process variables responsible for drug release by applying statistical optimization technique. Methods: Formulations were prepared based on Taguchi Orthogonal Array design and Fraction Factorial ...
Selecting Optimal Subset of Security Controls
Yevseyeva, I.; Basto-Fernandes, V.; Michael, Emmerich, T. M.; Moorsel, van, A.
2015-01-01
Open Access journal Choosing an optimal investment in information security is an issue most companies face these days. Which security controls to buy to protect the IT system of a company in the best way? Selecting a subset of security controls among many available ones can be seen as a resource allocation problem that should take into account conflicting objectives and constraints of the problem. In particular, the security of the system should be improved without hindering productivity, ...
Stochastic Linear Quadratic Optimal Control Problems
International Nuclear Information System (INIS)
Chen, S.; Yong, J.
2001-01-01
This paper is concerned with the stochastic linear quadratic optimal control problem (LQ problem, for short) for which the coefficients are allowed to be random and the cost functional is allowed to have a negative weight on the square of the control variable. Some intrinsic relations among the LQ problem, the stochastic maximum principle, and the (linear) forward-backward stochastic differential equations are established. Some results involving Riccati equation are discussed as well
Greenhouse Environmental Control Using Optimized MIMO PID Technique
Directory of Open Access Journals (Sweden)
Fateh BOUNAAMA
2011-10-01
Full Text Available Climate control for protected crops brings the added dimension of a biological system into a physical system control situation. The thermally dynamic nature of a greenhouse suggests that disturbance attenuation (load control of external temperature, humidity, and sunlight is far more important than is the case for controlling other types of buildings. This paper investigates the application of multi-inputs multi-outputs (MIMO PID controller to a MIMO greenhouse environmental model with actuation constraints. This method is based on decoupling the system at low frequency point. The optimal tuning values are determined using genetic algorithms optimization (GA. The inside outsides climate model of the environmental greenhouse, and the automatically collected data sets of Avignon, France are used to simulate and test this technique. The control objective is to maintain a highly coupled inside air temperature and relative humidity of strongly perturbed greenhouse, at specified set-points, by the ventilation/cooling and moisturizing operations.
Optimal Trajectory Tracking Control for a Wheeled Mobile Robot Using Fractional Order PID Controller
Directory of Open Access Journals (Sweden)
Ameer L. Saleh
2018-02-01
Full Text Available This paper present an optimal Fractional Order PID (FOPID controller based on Particle Swarm Optimization (PSO for controlling the trajectory tracking of Wheeled Mobile Robot(WMR.The issue of trajectory tracking with given a desired reference velocity is minimized to get the distance and deviation angle equal to zero, to realize the objective of trajectory tracking a two FOPID controllers are used for velocity control and azimuth control to implement the trajectory tracking control. A path planning and path tracking methodologies are used to give different desired tracking trajectories. PSO algorithm is using to find the optimal parameters of FOPID controllers. The kinematic and dynamic models of wheeled mobile robot for desired trajectory tracking with PSO algorithm are simulated in Simulink-Matlab. Simulation results show that the optimal FOPID controllers are more effective and has better dynamic performance than the conventional methods.
Rapid Optimal Generation Algorithm for Terrain Following Trajectory Based on Optimal Control
Institute of Scientific and Technical Information of China (English)
杨剑影; 张海; 谢邦荣; 尹健
2004-01-01
Based on the optimal control theory, a 3-dimensionnal direct generation algorithm is proposed for anti-ground low altitude penetration tasks under complex terrain. By optimizing the terrain following(TF) objective function,terrain coordinate system, missile dynamic model and control vector, the TF issue is turning into the improved optimal control problem whose mathmatical model is simple and need not solve the second order terrain derivative. Simulation results prove that this method is reasonable and feasible. The TF precision is in the scope from 0.3 m to 3.0 m,and the planning time is less than 30 min. This method have the strongpionts such as rapidness, precision and has great application value.
Multidimensional optimal droop control for wind resources in DC microgrids
Bunker, Kaitlyn J.
Two important and upcoming technologies, microgrids and electricity generation from wind resources, are increasingly being combined. Various control strategies can be implemented, and droop control provides a simple option without requiring communication between microgrid components. Eliminating the single source of potential failure around the communication system is especially important in remote, islanded microgrids, which are considered in this work. However, traditional droop control does not allow the microgrid to utilize much of the power available from the wind. This dissertation presents a novel droop control strategy, which implements a droop surface in higher dimension than the traditional strategy. The droop control relationship then depends on two variables: the dc microgrid bus voltage, and the wind speed at the current time. An approach for optimizing this droop control surface in order to meet a given objective, for example utilizing all of the power available from a wind resource, is proposed and demonstrated. Various cases are used to test the proposed optimal high dimension droop control method, and demonstrate its function. First, the use of linear multidimensional droop control without optimization is demonstrated through simulation. Next, an optimal high dimension droop control surface is implemented with a simple dc microgrid containing two sources and one load. Various cases for changing load and wind speed are investigated using simulation and hardware-in-the-loop techniques. Optimal multidimensional droop control is demonstrated with a wind resource in a full dc microgrid example, containing an energy storage device as well as multiple sources and loads. Finally, the optimal high dimension droop control method is applied with a solar resource, and using a load model developed for a military patrol base application. The operation of the proposed control is again investigated using simulation and hardware-in-the-loop techniques.
Surrogate Modeling for Geometry Optimization
DEFF Research Database (Denmark)
Rojas Larrazabal, Marielba de la Caridad; Abraham, Yonas; Holzwarth, Natalie
2009-01-01
A new approach for optimizing the nuclear geometry of an atomic system is described. Instead of the original expensive objective function (energy functional), a small number of simpler surrogates is used.......A new approach for optimizing the nuclear geometry of an atomic system is described. Instead of the original expensive objective function (energy functional), a small number of simpler surrogates is used....
Optimal centralized and decentralized velocity feedback control on a beam
International Nuclear Information System (INIS)
Engels, W P; Elliott, S J
2008-01-01
This paper considers the optimization of a velocity feedback controller with a collocated force actuator, to minimize the kinetic energy of a simply supported beam. If the beam is excited at a single location, the optimum feedback gain varies with the position of the control system. It is shown that this variation depends partly on the location of the control force relative to the exciting force. If a distributed excitation is assumed, that is random in both time and space, a unique optimum value of the feedback gain can be found for a given control location. The effect of the control location on performance and the optimal feedback gain can then be examined and is found to be limited provided the control locations are not close to the ends of the beam. The optimization can also be performed for a multichannel velocity feedback system. Both a centralized and a decentralized controller are considered. It is shown that the difference in performance between a centralized and a decentralized controller is small, unless the control locations are closely spaced. In this case the centralized controller effectively feeds back a moment proportional to angular velocity as well as a force proportional to a velocity. It is also shown that the optimal feedback gain can be approximated on the basis of a limited model and that similar results can be achieved
Helicopter trajectory planning using optimal control theory
Menon, P. K. A.; Cheng, V. H. L.; Kim, E.
1988-01-01
A methodology for optimal trajectory planning, useful in the nap-of-the-earth guidance of helicopters, is presented. This approach uses an adjoint-control transformation along with a one-dimensional search scheme for generating the optimal trajectories. In addition to being useful for helicopter nap-of-the-earth guidance, the trajectory planning solution is of interest in several other contexts, such as robotic vehicle guidance and terrain-following guidance for cruise missiles and aircraft. A distinguishing feature of the present research is that the terrain constraint and the threat envelopes are incorporated in the equations of motion. Second-order necessary conditions are examined.
Force control of a tri-layer conducting polymer actuator using optimized fuzzy logic control
International Nuclear Information System (INIS)
Itik, Mehmet; Sabetghadam, Mohammadreza; Alici, Gursel
2014-01-01
Conducting polymers actuators (CPAs) are potential candidates for replacing conventional actuators in various fields, such as robotics and biomedical engineering, due to their advantageous properties, which includes their low cost, light weight, low actuation voltage and biocompatibility. As these actuators are very suitable for use in micro-nano manipulation and in injection devices in which the magnitude of the force applied to the target is of crucial importance, the force generated by CPAs needs to be accurately controlled. In this paper, a fuzzy logic (FL) controller with a Mamdani inference system is designed to control the blocking force of a trilayer CPA with polypyrrole electrodes, which operates in air. The particle swarm optimization (PSO) method is employed to optimize the controller’s membership function parameters and therefore enhance the performance of the FL controller. An adaptive neuro-fuzzy inference system model, which can capture the nonlinear dynamics of the actuator, is utilized in the optimization process. The optimized Mamdani FL controller is then implemented on the CPA experimentally, and its performance is compared with a non-optimized fuzzy controller as well as with those obtained from a conventional PID controller. The results presented indicate that the blocking force at the tip of the CPA can be effectively controlled by the optimized FL controller, which shows excellent transient and steady state characteristics but increases the control voltage compared to the non-optimized fuzzy controllers. (paper)
Intelligent structural optimization: Concept, Model and Methods
International Nuclear Information System (INIS)
Lu, Dagang; Wang, Guangyuan; Peng, Zhang
2002-01-01
Structural optimization has many characteristics of Soft Design, and so, it is necessary to apply the experience of human experts to solving the uncertain and multidisciplinary optimization problems in large-scale and complex engineering systems. With the development of artificial intelligence (AI) and computational intelligence (CI), the theory of structural optimization is now developing into the direction of intelligent optimization. In this paper, a concept of Intelligent Structural Optimization (ISO) is proposed. And then, a design process model of ISO is put forward in which each design sub-process model are discussed. Finally, the design methods of ISO are presented
Time Optimal Control Laws for Bilinear Systems
Directory of Open Access Journals (Sweden)
Salim Bichiou
2018-01-01
Full Text Available The aim of this paper is to determine the feedforward and state feedback suboptimal time control for a subset of bilinear systems, namely, the control sequence and reaching time. This paper proposes a method that uses Block pulse functions as an orthogonal base. The bilinear system is projected along that base. The mathematical integration is transformed into a product of matrices. An algebraic system of equations is obtained. This system together with specified constraints is treated as an optimization problem. The parameters to determine are the final time, the control sequence, and the states trajectories. The obtained results via the newly proposed method are compared to known analytical solutions.
Robust Structured Control Design via LMI Optimization
DEFF Research Database (Denmark)
Adegas, Fabiano Daher; Stoustrup, Jakob
2011-01-01
This paper presents a new procedure for discrete-time robust structured control design. Parameter-dependent nonconvex conditions for stabilizable and induced L2-norm performance controllers are solved by an iterative linear matrix inequalities (LMI) optimization. A wide class of controller...... structures including decentralized of any order, ﬁxed-order dynamic output feedback, static output feedback can be designed robust to polytopic uncertainties. Stability is proven by a parameter-dependent Lyapunov function. Numerical examples on robust stability margins shows that the proposed procedure can...
Control strategies for wind farm power optimization: LES study
Ciri, Umberto; Rotea, Mario; Leonardi, Stefano
2017-11-01
Turbines in wind farms operate in off-design conditions as wake interactions occur for particular wind directions. Advanced wind farm control strategies aim at coordinating and adjusting turbine operations to mitigate power losses in such conditions. Coordination is achieved by controlling on upstream turbines either the wake intensity, through the blade pitch angle or the generator torque, or the wake direction, through yaw misalignment. Downstream turbines can be adapted to work in waked conditions and limit power losses, using the blade pitch angle or the generator torque. As wind conditions in wind farm operations may change significantly, it is difficult to determine and parameterize the variations of the coordinated optimal settings. An alternative is model-free control and optimization of wind farms, which does not require any parameterization and can track the optimal settings as conditions vary. In this work, we employ a model-free optimization algorithm, extremum-seeking control, to find the optimal set-points of generator torque, blade pitch and yaw angle for a three-turbine configuration. Large-Eddy Simulations are used to provide a virtual environment to evaluate the performance of the control strategies under realistic, unsteady incoming wind. This work was supported by the National Science Foundation, Grants No. 1243482 (the WINDINSPIRE project) and IIP 1362033 (I/UCRC WindSTAR). TACC is acknowledged for providing computational time.
Stochastic Optimal Control for Online Seller under Reputational Mechanisms
Directory of Open Access Journals (Sweden)
Milan Bradonjić
2015-12-01
Full Text Available In this work we propose and analyze a model which addresses the pulsing behavior of sellers in an online auction (store. This pulsing behavior is observed when sellers switch between advertising and processing states. We assert that a seller switches her state in order to maximize her profit, and further that this switch can be identified through the seller’s reputation. We show that for each seller there is an optimal reputation, i.e., the reputation at which the seller should switch her state in order to maximize her total profit. We design a stochastic behavioral model for an online seller, which incorporates the dynamics of resource allocation and reputation. The design of the model is optimized by using a stochastic advertising model from [1] and used effectively in the Stochastic Optimal Control of Advertising [2]. This model of reputation is combined with the effect of online reputation on sales price empirically verified in [3]. We derive the Hamilton-Jacobi-Bellman (HJB differential equation, whose solution relates optimal wealth level to a seller’s reputation. We formulate both a full model, as well as a reduced model with fewer parameters, both of which have the same qualitative description of the optimal seller behavior. Coincidentally, the reduced model has a closed form analytical solution that we construct.
Sivasundaram, Seenith
2016-07-01
The review paper [1] is devoted to the survey of different structures that have been developed for the modeling and analysis of various types of fibrosis. Biomathematics, bioinformatics, biomechanics and biophysics modeling have been treated by means of a brief description of the different models developed. The review is impressive and clearly written, addressed to a reader interested not only in the theoretical modeling but also in the biological description. The models have been described without recurring to technical statements or mathematical equations thus allowing the non-specialist reader to understand what framework is more suitable at a certain observation scale. The review [1] concludes with the possibility to develop a multiscale approach considering also the definition of a therapeutical strategy for pathological fibrosis. In particular the control and optimization of therapeutics action is an important issue and this article aims at commenting on this topic.
A Higher Harmonic Optimal Controller to Optimise Rotorcraft Aeromechanical Behaviour
Leyland, Jane Anne
1996-01-01
Three methods to optimize rotorcraft aeromechanical behavior for those cases where the rotorcraft plant can be adequately represented by a linear model system matrix were identified and implemented in a stand-alone code. These methods determine the optimal control vector which minimizes the vibration metric subject to constraints at discrete time points, and differ from the commonly used non-optimal constraint penalty methods such as those employed by conventional controllers in that the constraints are handled as actual constraints to an optimization problem rather than as just additional terms in the performance index. The first method is to use a Non-linear Programming algorithm to solve the problem directly. The second method is to solve the full set of non-linear equations which define the necessary conditions for optimality. The third method is to solve each of the possible reduced sets of equations defining the necessary conditions for optimality when the constraints are pre-selected to be either active or inactive, and then to simply select the best solution. The effects of maneuvers and aeroelasticity on the systems matrix are modelled by using a pseudo-random pseudo-row-dependency scheme to define the systems matrix. Cases run to date indicate that the first method of solution is reliable, robust, and easiest to use, and that it was superior to the conventional controllers which were considered.
Optimal Control of Polymer Flooding Based on Maximum Principle
Directory of Open Access Journals (Sweden)
Yang Lei
2012-01-01
Full Text Available Polymer flooding is one of the most important technologies for enhanced oil recovery (EOR. In this paper, an optimal control model of distributed parameter systems (DPSs for polymer injection strategies is established, which involves the performance index as maximum of the profit, the governing equations as the fluid flow equations of polymer flooding, and the inequality constraint as the polymer concentration limitation. To cope with the optimal control problem (OCP of this DPS, the necessary conditions for optimality are obtained through application of the calculus of variations and Pontryagin’s weak maximum principle. A gradient method is proposed for the computation of optimal injection strategies. The numerical results of an example illustrate the effectiveness of the proposed method.
Bulgakov, V. K.; Strigunov, V. V.
2009-05-01
The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.
Optimization in engineering models and algorithms
Sioshansi, Ramteen
2017-01-01
This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems. The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering ...
Quaternion error-based optimal control applied to pinpoint landing
Ghiglino, Pablo
Accurate control techniques for pinpoint planetary landing - i.e., the goal of achieving landing errors in the order of 100m for unmanned missions - is a complex problem that have been tackled in different ways in the available literature. Among other challenges, this kind of control is also affected by the well known trade-off in UAV control that for complex underlying models the control is sub-optimal, while optimal control is applied to simplifed models. The goal of this research has been the development new control algorithms that would be able to tackle these challenges and the result are two novel optimal control algorithms namely: OQTAL and HEX2OQTAL. These controllers share three key properties that are thoroughly proven and shown in this thesis; stability, accuracy and adaptability. Stability is rigorously demonstrated for both controllers. Accuracy is shown in results of comparing these novel controllers with other industry standard algorithms in several different scenarios: there is a gain in accuracy of at least 15% for each controller, and in many cases much more than that. A new tuning algorithm based on swarm heuristics optimisation was developed as well as part of this research in order to tune in an online manner the standard Proportional-Integral-Derivative (PID) controllers used for benchmarking. Finally, adaptability of these controllers can be seen as a combination of four elements: mathematical model extensibility, cost matrices tuning, reduced computation time required and finally no prior knowledge of the navigation or guidance strategies needed. Further simulations in real planetary landing trajectories has shown that these controllers have the capacity of achieving landing errors in the order of pinpoint landing requirements, making them not only very precise UAV controllers, but also potential candidates for pinpoint landing unmanned missions.
Linear systems optimal and robust control
Sinha, Alok
2007-01-01
Introduction Overview Contents of the Book State Space Description of a Linear System Transfer Function of a Single Input/Single Output (SISO) System State Space Realizations of a SISO System SISO Transfer Function from a State Space Realization Solution of State Space Equations Observability and Controllability of a SISO System Some Important Similarity Transformations Simultaneous Controllability and Observability Multiinput/Multioutput (MIMO) Systems State Space Realizations of a Transfer Function Matrix Controllability and Observability of a MIMO System Matrix-Fraction Description (MFD) MFD of a Transfer Function Matrix for the Minimal Order of a State Space Realization Controller Form Realization from a Right MFD Poles and Zeros of a MIMO Transfer Function Matrix Stability Analysis State Feedback Control and Optimization State Variable Feedback for a Single Input System Computation of State Feedback Gain Matrix for a Multiinput System State Feedback Gain Matrix for a Multi...
Optimal Control of Switching Linear Systems
Directory of Open Access Journals (Sweden)
Ali Benmerzouga
2004-06-01
Full Text Available A solution to the control of switching linear systems with input constraints was given in Benmerzouga (1997 for both the conventional enumeration approach and the new approach. The solution given there turned out to be not unique. The main objective in this work is to determine the optimal control sequences {Ui(k , i = 1,..., M ; k = 0, 1, ..., N -1} which transfer the system from a given initial state X0 to a specific target state XT (or to be as close as possible by using the same discrete time solution obtained in Benmerzouga (1997 and minimizing a running cost-to-go function. By using the dynamic programming technique, the optimal solution is found for both approaches given in Benmerzouga (1997. The computational complexity of the modified algorithm is also given.
Iterative learning control an optimization paradigm
Owens, David H
2016-01-01
This book develops a coherent theoretical approach to algorithm design for iterative learning control based on the use of optimization concepts. Concentrating initially on linear, discrete-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately because their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates that there are new algorithms that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference signals and also to support new algorithms for local convergence of nonlinear iterative control. Simulation and application studies are used to illustrate algorithm properties and performance in systems like gantry robots and other elect...
Optimal Control via Reinforcement Learning with Symbolic Policy Approximation
Kubalìk, Jiřì; Alibekov, Eduard; Babuska, R.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri
2017-01-01
Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value function and policy mappings. This paper
Optimization and control of a continuous polymerization reactor
Directory of Open Access Journals (Sweden)
L. A. Alvarez
2012-12-01
Full Text Available This work studies the optimization and control of a styrene polymerization reactor. The proposed strategy deals with the case where, because of market conditions and equipment deterioration, the optimal operating point of the continuous reactor is modified significantly along the operation time and the control system has to search for this optimum point, besides keeping the reactor system stable at any possible point. The approach considered here consists of three layers: the Real Time Optimization (RTO, the Model Predictive Control (MPC and a Target Calculation (TC that coordinates the communication between the two other layers and guarantees the stability of the whole structure. The proposed algorithm is simulated with the phenomenological model of a styrene polymerization reactor, which has been widely used as a benchmark for process control. The complete optimization structure for the styrene process including disturbances rejection is developed. The simulation results show the robustness of the proposed strategy and the capability to deal with disturbances while the economic objective is optimized.
Symbolic approximate time-optimal control
Mazo, Manuel; Tabuada, Paulo
There is an increasing demand for controller design techniques capable of addressing the complex requirements of today's embedded applications. This demand has sparked the interest in symbolic control where lower complexity models of control systems are used to cater for complex specifications given
Exploring quantum control landscapes: Topology, features, and optimization scaling
International Nuclear Information System (INIS)
Moore, Katharine W.; Rabitz, Herschel
2011-01-01
Quantum optimal control experiments and simulations have successfully manipulated the dynamics of systems ranging from atoms to biomolecules. Surprisingly, these collective works indicate that the effort (i.e., the number of algorithmic iterations) required to find an optimal control field appears to be essentially invariant to the complexity of the system. The present work explores this matter in a series of systematic optimizations of the state-to-state transition probability on model quantum systems with the number of states N ranging from 5 through 100. The optimizations occur over a landscape defined by the transition probability as a function of the control field. Previous theoretical studies on the topology of quantum control landscapes established that they should be free of suboptimal traps under reasonable physical conditions. The simulations in this work include nearly 5000 individual optimization test cases, all of which confirm this prediction by fully achieving optimal population transfer of at least 99.9% on careful attention to numerical procedures to ensure that the controls are free of constraints. Collectively, the simulation results additionally show invariance of required search effort to system dimension N. This behavior is rationalized in terms of the structural features of the underlying control landscape. The very attractive observed scaling with system complexity may be understood by considering the distance traveled on the control landscape during a search and the magnitude of the control landscape slope. Exceptions to this favorable scaling behavior can arise when the initial control field fluence is too large or when the target final state recedes from the initial state as N increases.
Optimal Hedging with the Vector Autoregressive Model
L. Gatarek (Lukasz); S.G. Johansen (Soren)
2014-01-01
markdownabstract__Abstract__ We derive the optimal hedging ratios for a portfolio of assets driven by a Cointegrated Vector Autoregressive model with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be
Optimal control of complex atomic quantum systems.
van Frank, S; Bonneau, M; Schmiedmayer, J; Hild, S; Gross, C; Cheneau, M; Bloch, I; Pichler, T; Negretti, A; Calarco, T; Montangero, S
2016-10-11
Quantum technologies will ultimately require manipulating many-body quantum systems with high precision. Cold atom experiments represent a stepping stone in that direction: a high degree of control has been achieved on systems of increasing complexity. However, this control is still sub-optimal. In many scenarios, achieving a fast transformation is crucial to fight against decoherence and imperfection effects. Optimal control theory is believed to be the ideal candidate to bridge the gap between early stage proof-of-principle demonstrations and experimental protocols suitable for practical applications. Indeed, it can engineer protocols at the quantum speed limit - the fastest achievable timescale of the transformation. Here, we demonstrate such potential by computing theoretically and verifying experimentally the optimal transformations in two very different interacting systems: the coherent manipulation of motional states of an atomic Bose-Einstein condensate and the crossing of a quantum phase transition in small systems of cold atoms in optical lattices. We also show that such processes are robust with respect to perturbations, including temperature and atom number fluctuations.
Optimal Control Problems for Partial Differential Equations on Reticulated Domains
Kogut, Peter I
2011-01-01
In the development of optimal control, the complexity of the systems to which it is applied has increased significantly, becoming an issue in scientific computing. In order to carry out model-reduction on these systems, the authors of this work have developed a method based on asymptotic analysis. Moving from abstract explanations to examples and applications with a focus on structural network problems, they aim at combining techniques of homogenization and approximation. Optimal Control Problems for Partial Differential Equations on Reticulated Domains is an excellent reference tool for gradu
Transmission Dynamics and Optimal Control of Malaria in Kenya
Directory of Open Access Journals (Sweden)
Gabriel Otieno
2016-01-01
Full Text Available This paper proposes and analyses a mathematical model for the transmission dynamics of malaria with four-time dependent control measures in Kenya: insecticide treated bed nets (ITNs, treatment, indoor residual spray (IRS, and intermittent preventive treatment of malaria in pregnancy (IPTp. We first considered constant control parameters and calculate the basic reproduction number and investigate existence and stability of equilibria as well as stability analysis. We proved that if R0≤1, the disease-free equilibrium is globally asymptotically stable in D. If R0>1, the unique endemic equilibrium exists and is globally asymptotically stable. The model also exhibits backward bifurcation at R0=1. If R0>1, the model admits a unique endemic equilibrium which is globally asymptotically stable in the interior of feasible region D. The sensitivity results showed that the most sensitive parameters are mosquito death rate and mosquito biting rates. We then consider the time-dependent control case and use Pontryagin’s Maximum Principle to derive the necessary conditions for the optimal control of the disease using the proposed model. The existence of optimal control problem is proved. Numerical simulations of the optimal control problem using a set of reasonable parameter values suggest that the optimal control strategy for malaria control in endemic areas is the combined use of treatment and IRS; for epidemic prone areas is the use of treatment and IRS; for seasonal areas is the use of treatment; and for low risk areas is the use of ITNs and treatment. Control programs that follow these strategies can effectively reduce the spread of malaria disease in different malaria transmission settings in Kenya.
Directory of Open Access Journals (Sweden)
Rashad O. Mastaliev
2016-12-01
Full Text Available The optimal control problem of nonlinear stochastic systems which mathematical model is given by Ito stochastic differential equation with delay argument is considered. Assuming that the concerned region is open for the control by the first and the second variation (classical sense of the quality functional we obtain the necessary optimality condition of the first and the second order. In the particular case we receive the stochastic analog of the Legendre—Clebsch condition and some constructively verified conclusions from the second order necessary condition. We investigate the Legendre–Clebsch conditions for the degeneration case and obtain the necessary conditions of optimality for a special control, in the classical sense.
Optimal control of hydroelectric facility incorporating pump storage
International Nuclear Information System (INIS)
Zhao, Guangzhi; Davison, Matt
2009-01-01
We consider a simple model of a pump-assisted hydroelectric facility operating in a market with time-varying but deterministic power prices and constant water inflows. The engineering details of the facility are described by a model containing several parameters. We present an algorithm for optimizing first the energy and then the profit produced by these plants. This algorithm allows us to describe the relationships between control trajectory and time, and between inflow and price. Remarkably, we see that under some reasonable choices of facility parameters and for power prices that are not extremely variable, the optimal profit operation of these facilities is not too different from their optimal energy operation, and the control is less affected by the price as the inflow rate increases. (author)
A hybrid iterative scheme for optimal control problems governed by ...
African Journals Online (AJOL)
MRT
KEY WORDS: Optimal control problem; Fredholm integral equation; ... control problems governed by Fredholm integral and integro-differential equations is given in (Brunner and Yan, ..... The exact optimal trajectory and control functions are. 2.
Alam, Fahad; Boet, Sylvain; Piquette, Dominique; Lai, Anita; Perkes, Christopher P.; LeBlanc, Vicki R.
2016-01-01
Enhanced podcasts increase learning, but evidence is lacking on how they should be designed to optimize their effectiveness. This study assessed the impact two learning instructional design methods (mental practice and modeling), either on their own or in combination, for teaching complex cognitive medical content when incorporated into enhanced…
Rethinking exchange market models as optimization algorithms
Luquini, Evandro; Omar, Nizam
2018-02-01
The exchange market model has mainly been used to study the inequality problem. Although the human society inequality problem is very important, the exchange market models dynamics until stationary state and its capability of ranking individuals is interesting in itself. This study considers the hypothesis that the exchange market model could be understood as an optimization procedure. We present herein the implications for algorithmic optimization and also the possibility of a new family of exchange market models
Strong stabilization servo controller with optimization of performance criteria.
Sarjaš, Andrej; Svečko, Rajko; Chowdhury, Amor
2011-07-01
Synthesis of a simple robust controller with a pole placement technique and a H(∞) metrics is the method used for control of a servo mechanism with BLDC and BDC electric motors. The method includes solving a polynomial equation on the basis of the chosen characteristic polynomial using the Manabe standard polynomial form and parametric solutions. Parametric solutions are introduced directly into the structure of the servo controller. On the basis of the chosen parametric solutions the robustness of a closed-loop system is assessed through uncertainty models and assessment of the norm ‖•‖(∞). The design procedure and the optimization are performed with a genetic algorithm differential evolution - DE. The DE optimization method determines a suboptimal solution throughout the optimization on the basis of a spectrally square polynomial and Šiljak's absolute stability test. The stability of the designed controller during the optimization is being checked with Lipatov's stability condition. Both utilized approaches: Šiljak's test and Lipatov's condition, check the robustness and stability characteristics on the basis of the polynomial's coefficients, and are very convenient for automated design of closed-loop control and for application in optimization algorithms such as DE. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.
Control strategy optimization of HVAC plants
Energy Technology Data Exchange (ETDEWEB)
Facci, Andrea Luigi; Zanfardino, Antonella [Department of Engineering, University of Napoli “Parthenope” (Italy); Martini, Fabrizio [Green Energy Plus srl (Italy); Pirozzi, Salvatore [SIAT Installazioni spa (Italy); Ubertini, Stefano [School of Engineering (DEIM) University of Tuscia (Italy)
2015-03-10
In this paper we present a methodology to optimize the operating conditions of heating, ventilation and air conditioning (HVAC) plants to achieve a higher energy efficiency in use. Semi-empiric numerical models of the plant components are used to predict their performances as a function of their set-point and the environmental and occupied space conditions. The optimization is performed through a graph-based algorithm that finds the set-points of the system components that minimize energy consumption and/or energy costs, while matching the user energy demands. The resulting model can be used with systems of almost any complexity, featuring both HVAC components and energy systems, and is sufficiently fast to make it applicable to real-time setting.
Control strategy optimization of HVAC plants
International Nuclear Information System (INIS)
Facci, Andrea Luigi; Zanfardino, Antonella; Martini, Fabrizio; Pirozzi, Salvatore; Ubertini, Stefano
2015-01-01
In this paper we present a methodology to optimize the operating conditions of heating, ventilation and air conditioning (HVAC) plants to achieve a higher energy efficiency in use. Semi-empiric numerical models of the plant components are used to predict their performances as a function of their set-point and the environmental and occupied space conditions. The optimization is performed through a graph-based algorithm that finds the set-points of the system components that minimize energy consumption and/or energy costs, while matching the user energy demands. The resulting model can be used with systems of almost any complexity, featuring both HVAC components and energy systems, and is sufficiently fast to make it applicable to real-time setting
Optimal resonant control of flexible structures
DEFF Research Database (Denmark)
Krenk, Steen; Høgsberg, Jan Becker
2009-01-01
When introducing a resonant controller for a particular vibration mode in a structure this mode splits into two. A design principle is developed for resonant control based oil equal damping of these two modes. First the design principle is developed for control of a system with a single degree...... of freedom, and then it is extended to multi-mode structures. A root locus analysis of the controlled single-mode structure identifies the equal modal damping property as a condition oil the linear and Cubic terms of the characteristic equation. Particular solutions for filtered displacement feedback...... and filtered acceleration feedback are developed by combining the root locus analysis with optimal properties of the displacement amplification frequency curve. The results are then extended to multi-mode structures by including a quasi-static representation of the background modes in the equations...
Applied optimal control theory of distributed systems
Lurie, K A
1993-01-01
This book represents an extended and substantially revised version of my earlierbook, Optimal Control in Problems ofMathematical Physics,originally published in Russian in 1975. About 60% of the text has been completely revised and major additions have been included which have produced a practically new text. My aim was to modernize the presentation but also to preserve the original results, some of which are little known to a Western reader. The idea of composites, which is the core of the modern theory of optimization, was initiated in the early seventies. The reader will find here its implementation in the problem of optimal conductivity distribution in an MHD-generatorchannel flow.Sincethen it has emergedinto an extensive theory which is undergoing a continuous development. The book does not pretend to be a textbook, neither does it offer a systematic presentation of the theory. Rather, it reflects a concept which I consider as fundamental in the modern approach to optimization of dis tributed systems. ...
Optimal control and quantum simulations in superconducting quantum devices
Energy Technology Data Exchange (ETDEWEB)
Egger, Daniel J.
2014-10-31
Quantum optimal control theory is the science of steering quantum systems. In this thesis we show how to overcome the obstacles in implementing optimal control for superconducting quantum bits, a promising candidate for the creation of a quantum computer. Building such a device will require the tools of optimal control. We develop pulse shapes to solve a frequency crowding problem and create controlled-Z gates. A methodology is developed for the optimisation towards a target non-unitary process. We show how to tune-up control pulses for a generic quantum system in an automated way using a combination of open- and closed-loop optimal control. This will help scaling of quantum technologies since algorithms can calibrate control pulses far more efficiently than humans. Additionally we show how circuit QED can be brought to the novel regime of multi-mode ultrastrong coupling using a left-handed transmission line coupled to a right-handed one. We then propose to use this system as an analogue quantum simulator for the Spin-Boson model to show how dissipation arises in quantum systems.
DEFF Research Database (Denmark)
Ghiglino, Christian; Tvede, Mich
for generations, through fiscal policy, i.e. monetary transfers and taxes. Both situations with and without time discounting are considered. It is shown that if the discount factor is suffciently close to one then the optimal policy stabilizes the economy, i.e. the equilibrium path has the turnpike property...
DEFF Research Database (Denmark)
Ghiglino, Christian; Tvede, Mich
2000-01-01
for generations, through fiscal policy, i.e., monetary transfers and taxes. Situations both with and without time discounting are considered. It is shown that if the discount factor is sufficiently close to one then the optimal policy stabilizes the economy, i.e. the equilibrium path has the turnpike property...
Model based development of engine control algorithms
Dekker, H.J.; Sturm, W.L.
1996-01-01
Model based development of engine control systems has several advantages. The development time and costs are strongly reduced because much of the development and optimization work is carried out by simulating both engine and control system. After optimizing the control algorithm it can be executed
Handbook on modelling for discrete optimization
Pitsoulis, Leonidas; Williams, H
2006-01-01
The primary objective underlying the Handbook on Modelling for Discrete Optimization is to demonstrate and detail the pervasive nature of Discrete Optimization. While its applications cut across an incredibly wide range of activities, many of the applications are only known to specialists. It is the aim of this handbook to correct this. It has long been recognized that "modelling" is a critically important mathematical activity in designing algorithms for solving these discrete optimization problems. Nevertheless solving the resultant models is also often far from straightforward. In recent years it has become possible to solve many large-scale discrete optimization problems. However, some problems remain a challenge, even though advances in mathematical methods, hardware, and software technology have pushed the frontiers forward. This handbook couples the difficult, critical-thinking aspects of mathematical modeling with the hot area of discrete optimization. It will be done in an academic handbook treatment...
Portfolio optimization with mean-variance model
Hoe, Lam Weng; Siew, Lam Weng
2016-06-01
Investors wish to achieve the target rate of return at the minimum level of risk in their investment. Portfolio optimization is an investment strategy that can be used to minimize the portfolio risk and can achieve the target rate of return. The mean-variance model has been proposed in portfolio optimization. The mean-variance model is an optimization model that aims to minimize the portfolio risk which is the portfolio variance. The objective of this study is to construct the optimal portfolio using the mean-variance model. The data of this study consists of weekly returns of 20 component stocks of FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI). The results of this study show that the portfolio composition of the stocks is different. Moreover, investors can get the return at minimum level of risk with the constructed optimal mean-variance portfolio.
2017-03-21
Tutorial. European Journal Of Control. Vol 13/2-3, pp 242–260. Parrish, K., J. Granderson, A. Mercado, P. Mathew. 2013. Improving Energy Efficiency...successfully, the project as a whole was not able to successfully demonstrate the technology. Anecdotal evidence, academic studies, and system simulations...Oceanography Center HVAC heating, ventilating, and air-conditioning NPS Naval Postgraduate School NRL U.S. Naval Research Laboratory NSAM Naval
The Optimization dispatching of Micro Grid Considering Load Control
Zhang, Pengfei; Xie, Jiqiang; Yang, Xiu; He, Hongli
2018-01-01
This paper proposes an optimization control of micro-grid system economy operation model. It coordinates the new energy and storage operation with diesel generator output, so as to achieve the economic operation purpose of micro-grid. In this paper, the micro-grid network economic operation model is transformed into mixed integer programming problem, which is solved by the mature commercial software, and the new model is proved to be economical, and the load control strategy can reduce the charge and discharge times of energy storage devices, and extend the service life of the energy storage device to a certain extent.
Hybrid vehicle optimal control : Linear interpolation and singular control
Delprat, S.; Hofman, T.
2015-01-01
Hybrid vehicle energy management can be formulated as an optimal control problem. Considering that the fuel consumption is often computed using linear interpolation over lookup table data, a rigorous analysis of the necessary conditions provided by the Pontryagin Minimum Principle is conducted. For
Adaptive hybrid optimal quantum control for imprecisely characterized systems.
Egger, D J; Wilhelm, F K
2014-06-20
Optimal quantum control theory carries a huge promise for quantum technology. Its experimental application, however, is often hindered by imprecise knowledge of the input variables, the quantum system's parameters. We show how to overcome this by adaptive hybrid optimal control, using a protocol named Ad-HOC. This protocol combines open- and closed-loop optimal control by first performing a gradient search towards a near-optimal control pulse and then an experimental fidelity estimation with a gradient-free method. For typical settings in solid-state quantum information processing, adaptive hybrid optimal control enhances gate fidelities by an order of magnitude, making optimal control theory applicable and useful.
Parametric optimal control of uncertain systems under an optimistic value criterion
Li, Bo; Zhu, Yuanguo
2018-01-01
It is well known that the optimal control of a linear quadratic model is characterized by the solution of a Riccati differential equation. In many cases, the corresponding Riccati differential equation cannot be solved exactly such that the optimal feedback control may be a complex time-oriented function. In this article, a parametric optimal control problem of an uncertain linear quadratic model under an optimistic value criterion is considered for simplifying the expression of optimal control. Based on the equation of optimality for the uncertain optimal control problem, an approximation method is presented to solve it. As an application, a two-spool turbofan engine optimal control problem is given to show the utility of the proposed model and the efficiency of the presented approximation method.
Optimized PID control of depth of hypnosis in anesthesia.
Padula, Fabrizio; Ionescu, Clara; Latronico, Nicola; Paltenghi, Massimiliano; Visioli, Antonio; Vivacqua, Giulio
2017-06-01
This paper addresses the use of proportional-integral-derivative controllers for regulating the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable. In fact, introducing an automatic control system might provide significant benefits for the patient in reducing the risk for under- and over-dosing. In this study, the controller parameters are obtained through genetic algorithms by solving a min-max optimization problem. A set of 12 patient models representative of a large population variance is used to test controller robustness. The worst-case performance in the considered population is minimized considering two different scenarios: the induction case and the maintenance case. Our results indicate that including a gain scheduling strategy enables optimal performance for induction and maintenance phases, separately. Using a single tuning to address both tasks may results in a loss of performance up to 102% in the induction phase and up to 31% in the maintenance phase. Further on, it is shown that a suitably designed low-pass filter on the controller output can handle the trade-off between the performance and the noise effect in the control variable. Optimally tuned PID controllers provide a fast induction time with an acceptable overshoot and a satisfactory disturbance rejection performance during maintenance. These features make them a very good tool for comparison when other control algorithms are developed. Copyright © 2017 Elsevier B.V. All rights reserved.
A combined stochastic programming and optimal control approach to personal finance and pensions
DEFF Research Database (Denmark)
Konicz, Agnieszka Karolina; Pisinger, David; Rasmussen, Kourosh Marjani
2015-01-01
The paper presents a model that combines a dynamic programming (stochastic optimal control) approach and a multi-stage stochastic linear programming approach (SLP), integrated into one SLP formulation. Stochastic optimal control produces an optimal policy that is easy to understand and implement....
Conflicting Multi-Objective Compatible Optimization Control
Xu, Lihong; Hu, Qingsong; Hu, Haigen; Goodman, Erik
2010-01-01
Based on ideas developed in addressing practical greenhouse environmental control, we propose a new multi-objective compatible control method. Several detailed algorithms are proposed to meet the requirements of different kinds of problem: 1) A two-layer MOCC framework is presented for problems with a precise model; 2) To deal with situations
Kinematically Optimal Robust Control of Redundant Manipulators
Galicki, M.
2017-12-01
This work deals with the problem of the robust optimal task space trajectory tracking subject to finite-time convergence. Kinematic and dynamic equations of a redundant manipulator are assumed to be uncertain. Moreover, globally unbounded disturbances are allowed to act on the manipulator when tracking the trajectory by the endeffector. Furthermore, the movement is to be accomplished in such a way as to minimize both the manipulator torques and their oscillations thus eliminating the potential robot vibrations. Based on suitably defined task space non-singular terminal sliding vector variable and the Lyapunov stability theory, we derive a class of chattering-free robust kinematically optimal controllers, based on the estimation of transpose Jacobian, which seem to be effective in counteracting both uncertain kinematics and dynamics, unbounded disturbances and (possible) kinematic and/or algorithmic singularities met on the robot trajectory. The numerical simulations carried out for a redundant manipulator of a SCARA type consisting of the three revolute kinematic pairs and operating in a two-dimensional task space, illustrate performance of the proposed controllers as well as comparisons with other well known control schemes.
International Nuclear Information System (INIS)
Sugny, D.; Bomble, L.; Ribeyre, T.; Dulieu, O.; Desouter-Lecomte, M.
2009-01-01
Implementation of quantum controlled-NOT (CNOT) gates in realistic molecular systems is studied using stimulated Raman adiabatic passage (STIRAP) techniques optimized in the time domain by genetic algorithms or coupled with optimal control theory. In the first case, with an adiabatic solution (a series of STIRAP processes) as starting point, we optimize in the time domain different parameters of the pulses to obtain a high fidelity in two realistic cases under consideration. A two-qubit CNOT gate constructed from different assignments in rovibrational states is considered in diatomic (NaCs) or polyatomic (SCCl 2 ) molecules. The difficulty of encoding logical states in pure rotational states with STIRAP processes is illustrated. In such circumstances, the gate can be implemented by optimal control theory and the STIRAP sequence can then be used as an interesting trial field. We discuss the relative merits of the two methods for rovibrational computing (structure of the control field, duration of the control, and efficiency of the optimization).
Optimal control of Rydberg lattice gases
Cui, Jian; van Bijnen, Rick; Pohl, Thomas; Montangero, Simone; Calarco, Tommaso
2017-09-01
We present optimal control protocols to prepare different many-body quantum states of Rydberg atoms in optical lattices. Specifically, we show how to prepare highly ordered many-body ground states, GHZ states as well as some superposition of symmetric excitation number Fock states, that inherit the translational symmetry from the Hamiltonian, within sufficiently short excitation times minimising detrimental decoherence effects. For the GHZ states, we propose a two-step detection protocol to experimentally verify the optimised preparation of the target state based only on standard measurement techniques. Realistic experimental constraints and imperfections are taken into account by our optimisation procedure making it applicable to ongoing experiments.
Optimal control of Rydberg lattice gases
DEFF Research Database (Denmark)
Cui, Jian; Bijnen, Rick van; Pohl, Thomas
2017-01-01
the translational symmetry from the Hamiltonian, within sufficiently short excitation times minimising detrimental decoherence effects. For the GHZ states, we propose a two-step detection protocol to experimentally verify the optimised preparation of the target state based only on standard measurement techniques....... Realistic experimental constraints and imperfections are taken into account by our optimisation procedure making it applicable to ongoing experiments.......We present optimal control protocols to prepare different many-body quantum states of Rydberg atoms in optical lattices. Specifically, we show how to prepare highly ordered many-body ground states, GHZ states as well as some superposition of symmetric excitation number Fock states, that inherit...
Modeling investor optimism with fuzzy connectives
Lovric, M.; Almeida, R.J.; Kaymak, U.; Spronk, J.; Carvalho, J.P.; Dubois, D.; Kaymak, U.; Sousa, J.M.C.
2009-01-01
Optimism or pessimism of investors is one of the important characteristics that determine the investment behavior in financial markets. In this paper, we propose a model of investor optimism based on a fuzzy connective. The advantage of the proposed approach is that the influence of different levels
Optimal control of batch emulsion polymerization of vinyl chloride
Energy Technology Data Exchange (ETDEWEB)
Damslora, Andre Johan
1998-12-31
The highly exothermic polymerization of vinyl chloride (VC) is carried out in large vessels where the heat removal represents a major limitation of the production rate. Many emulsion polymerization reactors are operated in such a way that a substantial part of the heat transfer capacity is left unused for a significant part of the total batch time. To increase the reaction rate so that it matches the heat removal capacity during the course of the reaction, this thesis proposes the use of a sufficiently flexible initiator system to obtain a reaction rate which is high throughout the reaction and real-time optimization to compute the addition policy for the initiator. This optimization based approach provides a basis for an interplay between design and control and between production and research. A simple model is developed for predicting the polymerization rate. The model is highly nonlinear and open-loop unstable and may serve as an interesting case for comparison of nonlinear control strategies. The model is fitted to data obtained in a laboratory scale reactor. Finally, the thesis discusses optimal control of the emulsion polymerization reactor. Reduction of the batch cycle time is of major economic importance, as long as the quality parameters are within their specifications. The control parameterization had a major influence on the performance. A differentiable spline parameterization was applied and the optimization is illustrated in a number of cases. The best performance is obtained when the reactor temperature is obtained when the optimization is combined with some form of closed-loop control of the reactor temperature. 112 refs., 48 figs., 4 tabs.
Optimal treatment cost allocation methods in pollution control
International Nuclear Information System (INIS)
Chen Wenying; Fang Dong; Xue Dazhi
1999-01-01
Total emission control is an effective pollution control strategy. However, Chinese application of total emission control lacks reasonable and fair methods for optimal treatment cost allocation, a critical issue in total emission control. The author considers four approaches to allocate treatment costs. The first approach is to set up a multiple-objective planning model and to solve the model using the shortest distance ideal point method. The second approach is to define degree of satisfaction for cost allocation results for each polluter and to establish a method based on this concept. The third is to apply bargaining and arbitration theory to develop a model. The fourth is to establish a cooperative N-person game model which can be solved using the Shapley value method, the core method, the Cost Gap Allocation method or the Minimum Costs-Remaining Savings method. These approaches are compared using a practicable case study
Optimization and Control of Cyber-Physical Vehicle Systems
Directory of Open Access Journals (Sweden)
Justin M. Bradley
2015-09-01
Full Text Available A cyber-physical system (CPS is composed of tightly-integrated computation, communication and physical elements. Medical devices, buildings, mobile devices, robots, transportation and energy systems can benefit from CPS co-design and optimization techniques. Cyber-physical vehicle systems (CPVSs are rapidly advancing due to progress in real-time computing, control and artificial intelligence. Multidisciplinary or multi-objective design optimization maximizes CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modeling errors and uncertainties. CPVS optimization occurs at design-time and at run-time. This paper surveys the run-time cooperative optimization or co-optimization of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilized in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimization and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined.
Optimization and Control of Cyber-Physical Vehicle Systems.
Bradley, Justin M; Atkins, Ella M
2015-09-11
A cyber-physical system (CPS) is composed of tightly-integrated computation, communication and physical elements. Medical devices, buildings, mobile devices, robots, transportation and energy systems can benefit from CPS co-design and optimization techniques. Cyber-physical vehicle systems (CPVSs) are rapidly advancing due to progress in real-time computing, control and artificial intelligence. Multidisciplinary or multi-objective design optimization maximizes CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modeling errors and uncertainties. CPVS optimization occurs at design-time and at run-time. This paper surveys the run-time cooperative optimization or co-optimization of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilized in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimization and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined.
Simulation, optimization and control of a thermal cracking furnace
International Nuclear Information System (INIS)
Masoumi, M.E.; Sadrameli, S.M.; Towfighi, J.; Niaei, A.
2006-01-01
The ethylene production process is one of the most important aspect of a petrochemical plant and the cracking furnace is the heart of the process. Since, ethylene is one of the raw materials in the chemical industry and the market situation of not only the feed and the product, but also the utility is rapidly changing, the optimal operation and control of the plant is important. A mathematical model, which describes the static and dynamic operations of a pilot plant furnace, was developed. The static simulation was used to predict the steady-state profiles of temperature, pressure and products yield. The dynamic simulation of the process was used to predict the transient behavior of thermal cracking reactor. Using a dynamic programming technique, an optimal temperature profile was developed along the reactor. Performances of temperature control loop were tested for different controller parameters and disturbances. The results of the simulation were tested experimentally in a computer control pilot plant
Turbine Control Strategies for Wind Farm Power Optimization
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Göçmen Bozkurt, Tuhfe; Giebel, Gregor
2015-01-01
In recent decades there has been increasing interest in green energies, of which wind energy is the most important one. In order to improve the competitiveness of the wind power plants, there are ongoing researches to decrease cost per energy unit and increase the efficiency of wind turbines...... and wind farms. One way of achieving these goals is to optimize the power generated by a wind farm. One optimization method is to choose appropriate operating points for the individual wind turbines in the farm. We have made three models of a wind farm based on three difference control strategies...... the generated power by changing the power reference of the individual wind turbines. We use the optimization setup to compare power production of the wind farm models. This paper shows that for the most frequent wind velocities (below and around the rated values), the generated powers of the wind farms...
Particle swarm optimization based PID controller tuning for level control of two tank system
Vincent, Anju K.; Nersisson, Ruban
2017-11-01
Automatic control plays a vital role in industrial operation. In process industries, in order to have an improved and stable control system, we need a robust tuning method. In this paper Particle Swarm Optimization (PSO) based algorithm is proposed for the optimization of a PID controller for level control process. A two tank system is considered. Initially a PID controller is designed using an Internal Model Control (IMC). The results are compared with the PSO based controller setting. The performance of the controller is compared and analyzed by time domain specification. In order to validate the robustness of PID controller, disturbance is imposed. The system is simulated using MATLAB. The results show that the proposed method provides better controller performance.
Relaxed error control in shape optimization that utilizes remeshing
CSIR Research Space (South Africa)
Wilke, DN
2013-02-01
Full Text Available Shape optimization strategies based on error indicators usually require strict error control for every computed design during the optimization run. The strict error control serves two purposes. Firstly, it allows for the accurate computation...
Modeling and optimization of laser cutting operations
Directory of Open Access Journals (Sweden)
Gadallah Mohamed Hassan
2015-01-01
Full Text Available Laser beam cutting is one important nontraditional machining process. This paper optimizes the parameters of laser beam cutting parameters of stainless steel (316L considering the effect of input parameters such as power, oxygen pressure, frequency and cutting speed. Statistical design of experiments is carried in three different levels and process responses such as average kerf taper (Ta, surface roughness (Ra and heat affected zones are measured accordingly. A response surface model is developed as a function of the process parameters. Responses predicted by the models (as per Taguchi’s L27OA are employed to search for an optimal combination to achieve desired process yield. Response Surface Models (RSMs are developed for mean responses, S/N ratio, and standard deviation of responses. Optimization models are formulated as single objective optimization problem subject to process constraints. Models are formulated based on Analysis of Variance (ANOVA and optimized using Matlab developed environment. Optimum solutions are compared with Taguchi Methodology results. As such, practicing engineers have means to model, analyze and optimize nontraditional machining processes. Validation experiments are carried to verify the developed models with success.
Reproducibility, controllability, and optimization of LENR experiments
Energy Technology Data Exchange (ETDEWEB)
Nagel, David J. [The George Washington University, Washington DC 20052 (United States)
2006-07-01
Low-energy nuclear reaction (LENR) measurements are significantly, and increasingly reproducible. Practical control of the production of energy or materials by LENR has yet to be demonstrated. Minimization of costly inputs and maximization of desired outputs of LENR remain for future developments. The paper concludes by underlying that it is now clearly that demands for reproducible experiments in the early years of LENR experiments were premature. In fact, one can argue that irreproducibility should be expected for early experiments in a complex new field. As emphasized in the paper and as often happened in the history of science, experimental and theoretical progress can take even decades. It is likely to be many years before investments in LENR experiments will yield significant returns, even for successful research programs. However, it is clearly that a fundamental understanding of the anomalous effects observed in numerous experiments will significantly increase reproducibility, improve controllability, enable optimization of processes, and accelerate the economic viability of LENR.
Reproducibility, controllability, and optimization of LENR experiments
International Nuclear Information System (INIS)
Nagel, David J.
2006-01-01
Low-energy nuclear reaction (LENR) measurements are significantly, and increasingly reproducible. Practical control of the production of energy or materials by LENR has yet to be demonstrated. Minimization of costly inputs and maximization of desired outputs of LENR remain for future developments. The paper concludes by underlying that it is now clearly that demands for reproducible experiments in the early years of LENR experiments were premature. In fact, one can argue that irreproducibility should be expected for early experiments in a complex new field. As emphasized in the paper and as often happened in the history of science, experimental and theoretical progress can take even decades. It is likely to be many years before investments in LENR experiments will yield significant returns, even for successful research programs. However, it is clearly that a fundamental understanding of the anomalous effects observed in numerous experiments will significantly increase reproducibility, improve controllability, enable optimization of processes, and accelerate the economic viability of LENR
Optimal Control of Solar Heating System
Huang, Bin-Juine
2017-02-21
Forced-circulation solar heating system has been widely used in process and domestic heating applications. Additional pumping power is required to circulate the water through the collectors to absorb the solar energy. The present study intends to develop a maximum-power point tracking control (MPPT) to obtain the minimum pumping power consumption at an optimal heat collection. The net heat energy gain Qnet (= Qs − Wp/ηe) was found to be the cost function for MPPT. The step-up-step-down controller was used in the feedback design of MPPT. The field test results show that the pumping power is 89 W at Qs = 13.7 kW and IT = 892 W/m2. A very high electrical COP of the solar heating system (Qs/Wp = 153.8) is obtained.
Optimal sensorimotor control in eye movement sequences.
Munuera, Jérôme; Morel, Pierre; Duhamel, Jean-René; Deneve, Sophie
2009-03-11
Fast and accurate motor behavior requires combining noisy and delayed sensory information with knowledge of self-generated body motion; much evidence indicates that humans do this in a near-optimal manner during arm movements. However, it is unclear whether this principle applies to eye movements. We measured the relative contributions of visual sensory feedback and the motor efference copy (and/or proprioceptive feedback) when humans perform two saccades in rapid succession, the first saccade to a visual target and the second to a memorized target. Unbeknownst to the subject, we introduced an artificial motor error by randomly "jumping" the visual target during the first saccade. The correction of the memory-guided saccade allowed us to measure the relative contributions of visual feedback and efferent copy (and/or proprioceptive feedback) to motor-plan updating. In a control experiment, we extinguished the target during the saccade rather than changing its location to measure the relative contribution of motor noise and target localization error to saccade variability without any visual feedback. The motor noise contribution increased with saccade amplitude, but remained <30% of the total variability. Subjects adjusted the gain of their visual feedback for different saccade amplitudes as a function of its reliability. Even during trials where subjects performed a corrective saccade to compensate for the target-jump, the correction by the visual feedback, while stronger, remained far below 100%. In all conditions, an optimal controller predicted the visual feedback gain well, suggesting that humans combine optimally their efferent copy and sensory feedback when performing eye movements.
Mathematical modeling and optimization of complex structures
Repin, Sergey; Tuovinen, Tero
2016-01-01
This volume contains selected papers in three closely related areas: mathematical modeling in mechanics, numerical analysis, and optimization methods. The papers are based upon talks presented on the International Conference for Mathematical Modeling and Optimization in Mechanics, held in Jyväskylä, Finland, March 6-7, 2014 dedicated to Prof. N. Banichuk on the occasion of his 70th birthday. The articles are written by well-known scientists working in computational mechanics and in optimization of complicated technical models. Also, the volume contains papers discussing the historical development, the state of the art, new ideas, and open problems arising in modern continuum mechanics and applied optimization problems. Several papers are concerned with mathematical problems in numerical analysis, which are also closely related to important mechanical models. The main topics treated include: * Computer simulation methods in mechanics, physics, and biology; * Variational problems and methods; minimiz...
Optimal Pricing and Advertising Policies for New Product Oligopoly Models
Gerald L. Thompson; Jinn-Tsair Teng
1984-01-01
In this paper our previous work on monopoly and oligopoly new product models is extended by the addition of pricing as well as advertising control variables. These models contain Bass's demand growth model, and the Vidale-Wolfe and Ozga advertising models, as well as the production learning curve model and an exponential demand function. The problem of characterizing an optimal pricing and advertising policy over time is an important question in the field of marketing as well as in the areas ...
An optimal control approach to manpower planning problem
Directory of Open Access Journals (Sweden)
H. W. J. Lee
2001-01-01
Full Text Available A manpower planning problem is studied in this paper. The model includes scheduling different types of workers over different tasks, employing and terminating different types of workers, and assigning different types of workers to various trainning programmes. The aim is to find an optimal way to do all these while keeping the time-varying demand for minimum number of workers working on each different tasks satisfied. The problem is posed as an optimal discrete-valued control problem in discrete time. A novel numerical scheme is proposed to solve the problem, and an illustrative example is provided.
Optimal Control of Hybrid Systems in Air Traffic Applications
Kamgarpour, Maryam
Growing concerns over the scalability of air traffic operations, air transportation fuel emissions and prices, as well as the advent of communication and sensing technologies motivate improvements to the air traffic management system. To address such improvements, in this thesis a hybrid dynamical model as an abstraction of the air traffic system is considered. Wind and hazardous weather impacts are included using a stochastic model. This thesis focuses on the design of algorithms for verification and control of hybrid and stochastic dynamical systems and the application of these algorithms to air traffic management problems. In the deterministic setting, a numerically efficient algorithm for optimal control of hybrid systems is proposed based on extensions of classical optimal control techniques. This algorithm is applied to optimize the trajectory of an Airbus 320 aircraft in the presence of wind and storms. In the stochastic setting, the verification problem of reaching a target set while avoiding obstacles (reach-avoid) is formulated as a two-player game to account for external agents' influence on system dynamics. The solution approach is applied to air traffic conflict prediction in the presence of stochastic wind. Due to the uncertainty in forecasts of the hazardous weather, and hence the unsafe regions of airspace for aircraft flight, the reach-avoid framework is extended to account for stochastic target and safe sets. This methodology is used to maximize the probability of the safety of aircraft paths through hazardous weather. Finally, the problem of modeling and optimization of arrival air traffic and runway configuration in dense airspace subject to stochastic weather data is addressed. This problem is formulated as a hybrid optimal control problem and is solved with a hierarchical approach that decouples safety and performance. As illustrated with this problem, the large scale of air traffic operations motivates future work on the efficient
Nonlinear Burn Control and Operating Point Optimization in ITER
Boyer, Mark; Schuster, Eugenio
2013-10-01
Control of the fusion power through regulation of the plasma density and temperature will be essential for achieving and maintaining desired operating points in fusion reactors and burning plasma experiments like ITER. In this work, a volume averaged model for the evolution of the density of energy, deuterium and tritium fuel ions, alpha-particles, and impurity ions is used to synthesize a multi-input multi-output nonlinear feedback controller for stabilizing and modulating the burn condition. Adaptive control techniques are used to account for uncertainty in model parameters, including particle confinement times and recycling rates. The control approach makes use of the different possible methods for altering the fusion power, including adjusting the temperature through auxiliary heating, modulating the density and isotopic mix through fueling, and altering the impurity density through impurity injection. Furthermore, a model-based optimization scheme is proposed to drive the system as close as possible to desired fusion power and temperature references. Constraints are considered in the optimization scheme to ensure that, for example, density and beta limits are avoided, and that optimal operation is achieved even when actuators reach saturation. Supported by the NSF CAREER award program (ECCS-0645086).
Synthesis of optimal digital controller of flocculant dosing
Directory of Open Access Journals (Sweden)
A.V. Pismenskiy
2013-06-01
Full Text Available Purpose. The task of automatic process control of the slime water thickening and flotation tailings clarification is the stabilization of thicken product density within the given range and keeping up the solids content in the overflow not above the permissible level with minimum use of the flocculants. In existing systems for automatic control the flocculant dosing is carried out according to the solids content in the device input (the principle of open-loop control. This leads to the excess consumption of the flocculants and increase the dispersion density of the overflow. To perform the synthesis of the optimal digital controller in order to minimize the deviations from the master control and ensure the specified quality of the transition process. Over controlling value should not exceed 5 %. To perform the system operation modeling in order to determine the quality of transient processes. Methodology. Synthesis of the optimal digital controller is based on the method of dynamic programming. Findings. A mathematical model of the object control is represented in the normal form of Cauchy and further in the form of differential equations. The optimum period of quantization as the function from specified error of control and the output coordinate change is calculated. The differential equation of Bellman is obtained and the condition for minimization of the quality functional. Bellman function is represented as a quadratic form from the variables of the system condition. In order to limit possible control, the weight coefficients of the functional are calculated based on maximum permitted values of the system condition variables and the control actions during the transient process. Practical value. Using the modeling of ACS of the flocculant dosing it was established that the over controlling amount is 3.5%, the transient process life 5.6 sec, the transient process is aperiodical, non-static control, which meets the requirements imposed on the
Maintenance Optimization of High Voltage Substation Model
Directory of Open Access Journals (Sweden)
Radim Bris
2008-01-01
Full Text Available The real system from practice is selected for optimization purpose in this paper. We describe the real scheme of a high voltage (HV substation in different work states. Model scheme of the HV substation 22 kV is demonstrated within the paper. The scheme serves as input model scheme for the maintenance optimization. The input reliability and cost parameters of all components are given: the preventive and corrective maintenance costs, the actual maintenance period (being optimized, the failure rate and mean time to repair - MTTR.
Wind turbine control and model predictive control for uncertain systems
DEFF Research Database (Denmark)
Thomsen, Sven Creutz
as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...
A useful framework for optimal replacement models
International Nuclear Information System (INIS)
Aven, Terje; Dekker, Rommert
1997-01-01
In this note we present a general framework for optimization of replacement times. It covers a number of models, including various age and block replacement models, and allows a uniform analysis for all these models. A relation to the marginal cost concept is described