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neuro-dynamic programming for optimal control of macroscopic fundamental diagram systems

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2020年 116卷 102628-102628页

作者： Su, Z. C. Chow, Andy H. F. Zheng, N. Huang, Y. P. Liang, E. M. Zhong, R. X. City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China Monash Univ Dept Civil Engn Melbourne Vic Australia Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China

The macroscopic fundamental diagram (MFD) can effectively reduce the spatial dimension involved in dynamic optimization of traffic performance for large-scale networks. Solving the Hamilton-Jacobi-Bellman (HJB) equation takes center stage in yielding solutions to the optimal control problem. At the core of solving the HJB equation is the value function that represents choosing a sequence of actions to optimize the system performance. However, this problem generally becomes intractable for possible discontinuities in the solution and the curse of dimensionality for systems with all but modest dimension. To address these challenges, a neural network is used to approximate the value function to obtain the optimal controls through policy iteration. Furthermore, a saturated operator is embedded in the neural network approximator to handle the difficulty caused by the control and state constraints. This policy iteration can be implemented as an iterative data-driven technique that integrates with the model-based optimal design based on real-time observations. Numerical experiments are conducted to show that the neuro-dynamic programming approach can achieve optimization goals while stabilizing the system by regulating the traffic state to the desired uncongested equilibrium.

关键词： Macroscopic fundamental diagram Hamilton-Jacobi-Bellman equation neuro-dynamic programming Policy iteration Saturated state and input

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neuro-dynamic programming for designing water reservoir network management policies

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CONTROL ENGINEERING PRACTICE 2007年第8期15卷 1031-1038页

作者： Castelletti, A. de Rigo, D. Rizzoli, A. E. Soncini-Sessa, R. Weber, E. Politecn Milan Dipartimento Elettron & Informat I-20133 Milan Italy IDSIA CH-6928 Manno Lugano Switzerland

Stochastic dynamic programming (SDP) can improve the management of a multipurpose water reservoir by generating management policies which are efficient with respect to the management objectives (flood protection, water supply for irrigation, hydropower generation, etc.). The improvement in efficiency is even more remarkable for networks of reservoirs. Unfortunately, SDP is affected by the well-known 'curse of dimensionality', i.e. computational time and computer memory occupation increase exponentially with the dimension of the problem (number of reservoirs), and the problem rapidly becomes intractable. neuro-dynamic programming (NDP) can sensibly mitigate this limitation by approximating Bellman functions with artificial neural networks (ANNs). In this paper the application of NDP to the problem of the management of reservoir networks is introduced. Results obtained in a real-world case study are finally presented. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： water reservoir network reservoir management stochastic dynamic programming neuro-dynamic programming

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neuro-dynamic programming in Control of the Ball and Beam System

Neuro-Dynamic Programming in Control of the Ball and Beam Sy...

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Symposium on Mechatronics Systems, Mechanics and Materials

作者： Burghardt, Andrzej Szuster, Marcin Rzeszow Univ Technol Dept Appl Mech & Robot PL-35959 Rzeszow Poland

ISBN: (纸本)9783037859209

This paper presents a new approach to the control problem of the ball and beam system, with a neuro-dynamic programming algorithm implemented as the main part of the control system. The controlled system is included in the group of underactuated systems, which are nonlinear dynamical objects with the number of control signals smaller than the number of degrees of freedom. This results in problems in the formulation of a stable control algorithm, that guarantees stabilization of the ball in the desired position on the beam. The type of ball and beam material has a noticeable influence on the difficulties in stabilization of the ball, because of a smaller rolling friction and big inertia of the used metallic ball in comparison to other, for example made of nonmetallic materials. The main part of the proposed discrete control system is the neuro-dynamic programming algorithm in a Dual-Heuristic dynamic programming configuration, realized in a form of two neural networks: the actor and the critic. neuro-dynamic programming algorithms use the Reinforcement Learning idea for adaptation of artificial neural network weights. Additional elements of the control system are the PD controller and the supervisory term, that ensures stability of the closed system loop. The control algorithm works on-line and does not require a preliminary learning phase of the neural network weights. Performance of the control algorithm was verified using the physical system controlled by the dSpace digital signal processing board.

关键词： ball and beam system neuro-dynamic programming neural networks underactuated system

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neuro-dynamic programming for adaptive fusion complexity control

Neuro-Dynamic Programming for adaptive fusion complexity con...

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Conference on Sensor Fusion - Architectures, Algorithms, and Applications III

作者： Ross, KN Chaney, RD ALPHATECH Inc Burlington MA 01803 USA

ISBN: (纸本)0819431931

The prodigious amount of information provided by surveillance systems and other information sources has created unprecedented opportunities for achieving situation awareness. Because the mission's and user's needs are constantly evolving, fusion control strategics must adapt to these changing requirements. However, the optimal control problem with the desired adaptive control capabilities is enormously complex. Therefore, we solve the adaptive fusion control problem approximately using a methodology called neuro-dynamic programming (NDP) that combines elements of dynamic programming, simulation-based reinforcement learning, and statistical inference techniques. This work demonstrates the promise of using NDP for adaptive fusion control by using it to allocate computational resources to Bayesian belief networks that use a variety of data types (e.g., SAR, MTI, ELINT, and terrain databases) to track and identify clusters of vehicles. We have significantly extended previous work by using NDP to adapt the fusion process itself in addition to deciding which clusters should get their inference updated. Fusion within the Bayesian networks was adapted by using NDP to select the subset of available data to be used when updating the inference. We also extended previous work by using a dynamic scenario with moving vehicles for training and testing models.

关键词： adaptive fusion control neuro-dynamic programming Bayesian networks reinforcement learning

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neuro-dynamic programming as a new framework for decision support for deficit irrigation sytems

Neuro-dynamic programming as a new framework for decision su...

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International Congress on Modelling and Simulation (MODSIM07)

作者： Schuetze, Niels Schmitz, Gerd H. Tech Univ Dresden Inst Hydrol & Meteorol D-01187 Dresden Germany

ISBN: (纸本)9780975840047

The great challenge of the agricultural sector is to produce more food and/or more revenue from less water, which can be achieved by optimal irrigation management. A task of primary importance is the problem of intraseasonal irrigation scheduling under limited seasonal water supply. On the intraseasonal level, a limited amount of water is to be distributed over a number of irrigations, taking into account the crops' response to water stress at different stages during the growing season. dynamic programming (DP) has been extensively used for optimization of irrigation scheduling problems (Bras and Cordova, 1981;Rao et al., 1988;Sunantara and Ramirez, 1997;Prasad et al., 2006). An alternative approach to calculate optimal irrigation schedules is provided by static optimization techniques such as linear and nonlinear programming (Shang and Mao, 2006;Gorantiwar et al., 2006). dynamic optimization is a closed-loop optimization strategy designed for obtaining an optimal look up table for selecting - at each stage of the atmosphere-plant-soil system during a growing season - the optimal irrigation decision for each possible state of the system. The popularity and success of this technique can be attributed to the fact that nonlinear and stochastic features of scheduling problems can be handled by DP (Bertsekas, 2000). However, it is well known that computational requirements of DP become overwhelming when the number of state and control variables is too large (Bellman and Dreyfus, 1962). For this reason all the studies applying DP for optimal irrigation scheduling have their limitations (Bras and Cordova, 1981;Rao et al., 1988;Sunantara and Ramirez, 1997). A second disadvantage of the classical DP optimization strategies lies in the necessary discretization of the state variables of the water balance models. This limits the predictive reliability of the models significantly which, in turn, affects the computation of the optimal schedules. A neuro-dynamic programming techni

关键词： Deficit irrigation dynamic optimization neuro-dynamic programming

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A neuro-dynamic programming-based optimal controller for tomato seedling growth in greenhouse systems

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NEURAL PROCESSING LETTERS 2006年第3期24卷 241-260页

作者： Pucheta, J. Patino, H. Fullana, R. Schugurensky, C. Kuchen, B. Univ Nacl San Juan Inst Automat Fac Ingn RA-5400 San Juan Argentina

This work proposes a neuro-dynamic programming-based optimal controller to guide the growth of tomato seedling crops by manipulating its environmental conditions in a greenhouse. The neurocontroller manages the growth development of the crop, while minimizing a predefined cost function that considers the operative costs and the final state errors under physical constraints on process variables and actuator signals. The aim is to guide the growth of tomato seedlings by controlling the microclimate of the greenhouse. The design process of the neurocontroller considers the nonlinear dynamic behavior of the crop-greenhouse system model and the real climate data. Simulations of the proposed approach allow for contrasting its performance against those of other strategies for tomato seedling crop development subject to various climatic conditions.

关键词： greenhouse control systems intelligent agriculture neuro-dynamic programming neurocontrollers optimal control

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Event-triggered control for input constrained non-affine nonlinear systems based on neuro-dynamic programming

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neuroCOMPUTING 2021年 440卷 175-184页

作者： Zhang, Shunchao Zhao, Bo Zhang, Yongwei Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China

In this paper, a neuro-dynamic programming (NDP)-based event-triggered control (ETC) method is proposed for unknown non-affine nonlinear systems with input constraints. A neural network-based identifier is established with measurable input and output data to learn the unknown system dynamics. Then, a critic neural network is employed to approximate the value function for solving the event triggered Hamilton-Jacobi-Bellman equation. Furthermore, an NDP-based ETC scheme is developed, which samples the states and updates the control law when the triggering condition is violated. Compared with the traditional time-triggered control methods, the ETC method can reduce computational burden, communication cost and bandwidth. In addition, the stability of the closed-loop system and the weight error convergence of the critic neural network are provided based on the Lyapunov?s direct method. The intersamling time is proved to be bounded by a positive constant, which excludes the Zeno behavior. Finally, two case studies are provided to verify the effectiveness of the developed ETC method. CO 2021 Elsevier B.V. All rights reserved.

关键词： neuro-dynamic programming Adaptive dynamic programming Event-triggered control Neural networks Non-affine nonlinear systems Input constraints

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Computationally efficient neuro-dynamic programming approximation method for the capacitated re-entrant line scheduling problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2012年第8期50卷 2353-2362页

作者： Choi, Jin Young Kim, Seoung Bum Korea Univ Sch Ind Management Engn Seoul South Korea Ajou Univ Div Ind & Informat Syst Engn Suwon 441749 South Korea

This paper presents a computationally efficient neuro-dynamic programming approximation method for the capacitated re-entrant line scheduling problem by reducing the number of feature functions. The method is based on a statistical assessment of the significance of the various feature functions. This assessment can be made by combining the weighted principal components with a thresholding algorithm. The efficacy of the new feature functions selected is tested by numerical experiments. The results indicate that the feature selection method presented here can extract a small number of significant features with the potential capability of providing a compact representation of the target value function in a neuro-dynamic programming framework. Moreover, the linear parametric architecture considered holds considerable promise as a way to provide effective and computationally efficient approximations for an optimal scheduling policy that consistently outperforms the heuristics typically employed.

关键词： capacitated re-entrant line data mining feature selection scheduling neuro-dynamic programming principal component analysis

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Zero-sum differential game guidance law for missile interception engagement via neuro-dynamic programming

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2023年第14期237卷 3352-3366页

作者： Xi, A-xing Cai, Yuan-li Deng, Yi-fan Jiang, Hao-nan Xi An Jiao Tong Univ Fac Elect & Informat Engn Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xianning West Rd 28 Xian 710049 Shaanxi Peoples R China

In this paper, for solving the nonlinear control problem of the missile intercepting the maneuvering target, a novel nonlinear zero-sum differential game guidance law is proposed via the neuro-dynamic programming approach. First, the continuous-time nonlinear differential game problem is transformed into solving the nonlinear Hamilton-Jacobi-Isaacs (HJI) equation. Then, a critic neural network is designed to solve the corresponding nonlinear HJI equation. An adaptive weight tuning law for the critic weights is proposed, where an additional term is added to ensure the stability of the closed-loop nonlinear system. Furthermore, the uniform ultimate boundedness of the closed-loop system and the critic NN weights estimation error are proved with the Lyapunov approach. Finally, some simulation results are presented to demonstrate the effectiveness of the proposed differential game guidance law for nonlinear interception.

关键词： Guidance law neuro-dynamic programming differential game nonlinear system Nash equilibrium

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Missile defense and interceptor allocation by neuro-dynamic programming

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2000年第1期30卷 42-51页

作者： Bertsekas, DP Homer, ML Logan, DA Patek, SD Sandell, NR MIT Informat & Decis Syst Lab Cambridge MA 02139 USA Biogen Inc Cambridge MA 02141 USA Alphatech Inc Burlington MA 01803 USA Univ Virginia Dept Syst Engn Charlottesville VA 22903 USA

The purpose of this paper is to propose a solution methodology for a missile defense problem involving the sequential allocation of defensive resources over a series of engagements. The problem is cast as a dynamic programming/Markovian decision problem, which is computationally intractable by exact methods because of its large number of states and its complex modeling issues. We have employed a neuro-dynamic programming (NDP) framework, whereby the cost-to-go function is approximated using neural network architectures that are trained on simulated data. We report on the performance obtained using several different training methods, and we compare this performance with the optimal.

关键词： dynamic programming neuro-dynamic programming reinforcement learning theater missile defense

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