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An Online Energy Management Control for Hybrid Electric Vehicles Based on neuro-dynamic programming

ALGORITHMS 2018年第3期11卷 33-33页

作者： Qin, Feiyan Li, Weimin Hu, Yue Xu, Guoqing Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Shenzhen 518055 Peoples R China Chinese Acad Sci Jining Inst Adv Technol Jining 272000 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

Hybrid electric vehicles are a compromise between traditional vehicles and pure electric vehicles and can be part of the solution to the energy shortage problem. Energy management strategies (EMSs) are highly related to energy utilization in HEVs' fuel economy. In this research, we have employed a neuro-dynamic programming (NDP) method to simultaneously optimize fuel economy and battery state of charge (SOC). In this NDP method, the critic network is a multi-resolution wavelet neural network based on the Meyer wavelet function, and the action network is a conventional wavelet neural network based on the Morlet function. The weights and parameters of both networks are obtained by an algorithm of backpropagation type. The NDP-based EMS has been applied to a parallel HEV and compared with a previously reported NDP EMS and a stochastic dynamic programing-based method. Simulation results under ADVISOR2002 have shown that the proposed NDP approach achieves better performance than both the methods. These indicate that the proposed NDP EMS, and the CWNN and MRWNN, are effective in approximating a nonlinear system.

关键词： parallel hybrid electric vehicle energy management neuro-dynamic programming wavelet neural network multi resolution analysis

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A neuro-dynamic programming approach to synthesize optimal dispatching rules in logistics

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IFAC Proceedings Volumes 2011年第1期44卷 9518-9523页

作者： Mauro Boccadoro Francesco Martinelli DIEI University of Perugia Italy DISP University of Rome “Tor Vergata” Italy

Abstract A dynamic model of a logistic node of a transportation network is presented and a performance index comprising transportation and inventory costs is considered. The optimization problem is solved considering a class of policies called full capacity. The derivation of optimal dispatching rules by integer programming or dynamic programming is usually intractable in realistic scenarios. For this reason, a neuro-dynamic programming (NDP) approach is pursued. Several simulation results are discussed, in particular for what concerns the role of transportation costs in the characterization of the optimal policies.

关键词： Logistics neuro-dynamic programming Transportation networks

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Joint Admission Control and Routing Via neuro-dynamic programming for Streaming Video over SDN 13

Joint Admission Control and Routing Via Neuro-Dynamic Progra...

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13th International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Zhu, Kunjie Ran, Yongyi Yang, Enzhong Yang, Jian Univ Sci & Technol China Dept Automat Hefei Anhui Peoples R China

ISBN: (纸本)9781509043729

This paper solves the joint problem of admission control and routing for the video transmission in software-defined networking (SDN). We utilize next generation network called SDN technology to construct an architecture for the proposed combined optimization problem. Our heuristic algorithm of the proposed combined optimization problem can be deployed on this architecture. Based on this background, we formulate the combined optimization problem into Markov Decision Process (MDP) with the aim of maximizing the average reward. In allusion to the challenge of the curses of dimensionality, an online learning framework is designed by employing neuro-dynamic programming (NDP) method. We construct an emulation platform based on POX controller and Mininet to confirm high efficiency of our solution. Experiment results show that our NDP based algorithm has an observably performance improvement compared with OSPF based benchmark algorithm.

关键词： Admission control routing algorithm software-defined networking neuro-dynamic programming video streaming service

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Efficient and Fair Scheduling of PHEV Charging With neuro dynamic programming

Efficient and Fair Scheduling of PHEV Charging With Neuro Dy...

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8th International ICST Conference on Communications and Networking in China (CHINACOM)

作者： Feng, Bowei Yu, Rong Lai, Yue Guangdong Univ Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781479914067

Plug-in hybrid electric vehicles (PHEVs) are a midterm solution to reduce the nation's dependency on oil. The batteries of these PHEVs can be recharged in the charging stations. However, with the extensive adoption of PHEVs, the charging load of PHEVs will pose considerable challenges to the grid because these PHEVs consume a large amount of electrical power. Therefore, it is important to implement coordinated charging. In this paper, an optimal charging scheduling of the PHEVs is proposed by minimizing the power losses and guaranteeing the fairness of PHEV charging. We employ a neuro-dynamic programming(NDP), which has in-built capability of studying from outside environment and adapting system parameters based on the performance requirements. Simulation results indicate that NDP outperforms two scheduling algorithms and simultaneously considers: (i) charging efficiency, (ii) charging fairness, and (iii) less power losses.

关键词： Plug-in hybrid electric vehicles coordinated charging neuro-dynamic programming fair charging

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Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

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Chinese Physics B 2015年第3期24卷 87-94页

作者：魏庆来刘德荣徐延才 The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

A policy iteration algorithm of adaptive dynamic programming（ADP） is developed to solve the optimal tracking control for a class of discrete-time chaotic systems. By system transformations, the optimal tracking problem is transformed into an optimal regulation one. The policy iteration algorithm for discrete-time chaotic systems is first described. Then,the convergence and admissibility properties of the developed policy iteration algorithm are presented, which show that the transformed chaotic system can be stabilized under an arbitrary iterative control law and the iterative performance index function simultaneously converges to the optimum. By implementing the policy iteration algorithm via neural networks,the developed optimal tracking control scheme for chaotic systems is verified by a simulation.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming neuro-dynamic programming

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Partially observed Stochastic shortest path problems with approximate solution by neurodynamic programming

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2007年第5期37卷 710-720页

作者： Patek, Stephen D. Univ Virginia Dept Syst & Informat Engn Charlottesville VA 22904 USA

We analyze a class of Markov decision processes with imperfect state information that evolve on an infinite time horizon and have a total cost criterion. In particular, we are interested in problems with stochastic shortest path structure, assuming the following: 1) the existence of a policy that guarantees termination with probability one and 2) the property that any policy that fails to guarantee termination has infinite expected cost from some initial state. We also assume that termination is perfectly recognized. In this paper, we clarify and expand upon arguments (given in an earlier paper) for establishing the existence, uniqueness, and characterization of stationary optimal policies, and the convergence of value and policy iteration. We also present an illustrative example, involving the search for a partially observed target that moves randomly on a grid, and we develop a simulation-based algorithm (based on neurodynamic programming techniques) for computing policies that approximately minimize the expected number of stages to complete the search.

关键词： markov decision process neuro-dynamic programming stochastic shortest path

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Feature-based methods for large scale dynamic programming

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MACHINE LEARNING 1996年第1-3期22卷 59-94页

作者： Tsitsiklis, JN VanRoy, B Laboratory for Information and Decision Systems Massachusetts Institute of Technology

We develop a methodological framework and present a few different ways in which dynamic programming and compact representations can be combined to solve large scale stochastic control problems. In particular, we develop algorithms that employ two types of feature-based compact representations;that is, representations that involve feature extraction and a relatively simple approximation architecture. We prove the convergence of these algorithms and provide bounds on the approximation error. As an example, one of these algorithms is used to generate a strategy for the game of Tetris. Furthermore, we provide a counterexample illustrating the difficulties of integrating compact representations with dynamic programming, which exemplifies the shortcomings of certain simple approaches.

关键词： compact representation curse of dimensionality dynamic programming features function approximation neuro-dynamic programming reinforcement learning

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Adaptive dynamic programming for Control: A Survey and Recent Advances

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第1期51卷 142-160页

作者： Liu, Derong Xue, Shan Zhao, Bo Luo, Biao Wei, Qinglai Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

This article reviews the recent development of adaptive dynamic programming (ADP) with applications in control. First, its applications in optimal regulation are introduced, and some skilled and efficient algorithms are presented. Next, the use of ADP to solve game problems, mainly nonzero-sum game problems, is elaborated. It is followed by applications in large-scale systems. Note that although the functions presented in this article are based on continuous-time systems, various applications of ADP in discrete-time systems are also analyzed. Moreover, in each section, not only some existing techniques are discussed, but also possible directions for future work are pointed out. Finally, some overall prospects for the future are given, followed by conclusions of this article. Through a comprehensive and complete investigation of its applications in many existing fields, this article fully demonstrates that the ADP intelligent control method is promising in today's artificial intelligence era. Furthermore, it also plays a significant role in promoting economic and social development.

关键词： Adaptive critic designs (ACDs) adaptive dynamic programming approximate dynamic programming intelligent control learning control neural dynamic programming neuro-dynamic programming optimal control reinforcement learning (RL)

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Discrete-Time Local Value Iteration Adaptive dynamic programming: Convergence Analysis

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2018年第6期48卷 875-891页

作者： Wei, Qinglai Lewis, Frank L. Liu, Derong Song, Ruizhuo Lin, Hanquan Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Texas Arlington UTA Res Inst Arlington TX 76118 USA Northeastern Univ Shenyang 110036 Liaoning Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

In this paper, convergence properties are established for the newly developed discrete-time local value iteration adaptive dynamic programming (ADP) algorithm. The present local iterative ADP algorithm permits an arbitrary positive semidefinite function to initialize the algorithm. Employing a state-dependent learning rate function, for the first time, the iterative value function and iterative control law can be updated in a subset of the state space instead of the whole state space, which effectively relaxes the computational burden. A new analysis method for the convergence property is developed to prove that the iterative value functions will converge to the optimum under some mild constraints. Monotonicity of the local value iteration ADP algorithm is presented, which shows that under some special conditions of the initial value function and the learning rate function, the iterative value function can monotonically converge to the optimum. Finally, three simulation examples and comparisons are given to illustrate the performance of the developed algorithm.

关键词： Adaptive critic designs adaptive dynamic programming (ADP) approximate dynamic programming local iteration neural networks neuro-dynamic programming nonlinear systems optimal control

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Value Iteration Adaptive dynamic programming for Optimal Control of Discrete-Time Nonlinear Systems

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IEEE TRANSACTIONS ON CYBERNETICS 2016年第3期46卷 840-853页

作者： Wei, Qinglai Liu, Derong Lin, Hanquan Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

In this paper, a value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. A novel convergence analysis is developed to guarantee that the iterative value function converges to the optimal performance index function. Initialized by different initial functions, it is proven that the iterative value function will be monotonically nonincreasing, monotonically nondecreasing, or nonmonotonic and will converge to the optimum. In this paper, for the first time, the admissibility properties of the iterative control laws are developed for value iteration algorithms. It is emphasized that new termination criteria are established to guarantee the effectiveness of the iterative control laws. Neural networks are used to approximate the iterative value function and compute the iterative control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.

关键词： Adaptive critic designs adaptive dynamic programming (ADP) approximate dynamic programming neural networks neuro-dynamic programming optimal control reinforcement learning value iteration

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