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ieee symposium Series on Computational Intelligence

作者： Chang Liu Yi Lu Murphey Department of Electrical and Computer Engineering University of Michigan - Dearborn Dearborn MI USA University of Michigan Dearborn Dearborn MI US

In this paper, we present two solutions for achieving the optimal control of PHEVs on short trips. We prove, mathematically, that a greedy control policy is optimal for those short trips where the battery State-of-Charge (SoC) will not drop below its minimum threshold level. A closed-form greedy control solution is derived from the PHEV powertrain model. Furthermore, we provide a Q-learning based approach which has the capability of in-vehicle learning and is model-free. Our algorithm, combining the Neuro-dynamic programming (NDP) with estimated future trip information, can robustly converge to the optimal policy on both fixed and randomly selected drive cycles.

关键词： Plug-in Hybrid Electric Vehicles (PHEVs) Power Management Energy Optimization reinforcement learning Q-learning

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS Special Section on Deep reinforcement learning and adaptive dynamic programming

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ieee Transactions on Neural Networks and learning Systems 2016年第12期27卷 2776-2776页

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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ieee transactions on neural networks and learning systems special section on deep reinforcement learning and adaptive dynamic programming

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ieee Transactions on Neural Networks and learning Systems 2016年第11期27卷 2454-2454页

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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dynamic Energy Management System for a Smart Microgrid

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2016年第8期27卷 1643-1656页

作者： Venayagamoorthy, Ganesh Kumar Sharma, Ratnesh K. Gautam, Prajwal K. Ahmadi, Afshin Clemson Univ Real Time Power & Intelligent Syst Lab Clemson SC 29634 USA Univ KwaZulu Natal Eskom Ctr Excellence HVDC Engn ZA-4041 Durban South Africa NEC Labs Amer Inc Energy Management Dept Cupertino CA 95014 USA Spirae Inc Ft Collins CO 80524 USA Clemson Univ Dept Elect & Comp Engn Clemson SC 29634 USA

This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar;however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid's system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.

关键词： adaptive dynamic programming dynamic energy management system (DEMS) evolutionary computing microgrid neural networks reinforcement learning renewable energy

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Enhancing supervisory training signals with environmental reinforcement learning using adaptive dynamic programming and artificial neural networks

Enhancing supervisory training signals with environmental re...

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ieee International Conference on Cognitive Informatics

作者： Niklas Melton Donald C. Wunsch Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla Missouri USA

ISBN: (纸本)9781509038473

A method for hybridizing supervised learning with adaptive dynamic programming was developed to increase the speed, quality, and robustness of on-line neural network learning from an imperfect teacher. reinforcement learning is used to modify and enhance the original supervisory signal before learning occurs. This paper describes the method of hybridization and presents a model problem in which a human supervisor teaches a simulated car to drive around a race track. Simulation results show successful learning and improvements in convergence time, error rate, and stability over either component method alone.

关键词： Training Mathematical model Convergence Automobiles learning (artificial intelligence) Neural networks Testing

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reinforcement learning of adaptive Energy Management With Transition Probability for a Hybrid Electric Tracked Vehicle

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ieee TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2015年第12期62卷 7837-7846页

作者： Liu, Teng Zou, Yuan Liu, Dexing Sun, Fengchun Beijing Inst Technol Beijing Collaborat & Innovat Ctr Elect Vehicles Beijing 100081 Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

A reinforcement learning-based adaptive energy management (RLAEM) is proposed for a hybrid electric tracked vehicle (HETV) in this paper. A control oriented model of the HETV is first established, in which the state-of-charge (SOC) of battery and the speed of generator are the state variables, and the engine's torque is the control variable. Subsequently, a transition probability matrix is learned from a specific driving schedule of the HETV. The proposed RLAEM decides appropriate power split between the battery and engine-generator set (EGS) to minimize the fuel consumption over different driving schedules. With the RLAEM, not only is driver's power requirement guaranteed, but also the fuel economy is improved as well. Finally, the RLAEM is compared with the stochastic dynamic programming (SDP)-based energy management for different driving schedules. The simulation results demonstrate the adaptability, optimality, and learning ability of the RLAEM and its capacity of reducing the computation time.

关键词： Adaptability energy management hybrid electric tracked vehicle (HETV) Q-learning algorithm state of charge (SOC) stochastic dynamic programming (SDP)

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Generalized Policy Iteration adaptive dynamic programming for Discrete-Time Nonlinear Systems

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ieee TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2015年第12期45卷 1577-1591页

作者： Liu, Derong Wei, Qinglai Yan, Pengfei Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper is concerned with a novel generalized policy iteration algorithm for solving optimal control problems for discrete-time nonlinear systems. The idea is to use an iterative adaptive dynamic programming algorithm to obtain iterative control laws which make the iterative value functions converge to the optimum. Initialized by an admissible control law, it is shown that the iterative value functions are monotonically nonincreasing and converge to the optimal solution of Hamilton-Jacobi-Bellman equation, under the assumption that a perfect function approximation is employed. The admissibility property is analyzed, which shows that any of the iterative control laws can stabilize the nonlinear system. Neural networks are utilized to implement the generalized policy iteration algorithm, by approximating the iterative value function and computing the iterative control law, respectively, to achieve approximate optimal control. Finally, numerical examples are presented to verify the effectiveness of the present generalized policy iteration algorithm.

关键词： adaptive critic designs adaptive dynamic programming (ADP) approximate dynamic programming generalized policy iteration neural networks neuro-dynamic programming nonlinear systems optimal control reinforcement learning

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Model-Free Dual Heuristic dynamic programming

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2015年第8期26卷 1834-1839页

作者： Ni, Zhen He, Haibo Zhong, Xiangnan Prokhorov, Danil V. Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Toyota Tech Ctr Toyota Res Inst North Amer Ann Arbor MI 48105 USA

Model-based dual heuristic dynamic programming (MB-DHP) is a popular approach in approximating optimal solutions in control problems. Yet, it usually requires offline training for the model network, and thus resulting in extra computational cost. In this brief, we propose a model-free DHP (MF-DHP) design based on finite-difference technique. In particular, we adopt multilayer perceptron with one hidden layer for both the action and the critic networks design, and use delayed objective functions to train both the action and the critic networks online over time. We test both the MF-DHP and MB-DHP approaches with a discrete time example and a continuous time example under the same parameter settings. Our simulation results demonstrate that the MF-DHP approach can obtain a control performance competitive with that of the traditional MB-DHP approach while requiring less computational resources.

关键词： Action-dependent dual heuristic dynamic programming (DHP) adaptive critic designs (ACDs) adaptive dynamic programming (ADP) online learning reinforcement learning

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Intelligent Control of Grid-Connected Microgrids: An adaptive Critic-Based Approach

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ieee JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2015年第2期3卷 493-504页

作者： Seidi, Sima Bakhshai, Alireza Queens Univ Queens Ctr Energy & Power Elect Res Kingston ON K7L 3N6 Canada Queens Univ Dept Elect & Comp Engn Kingston ON K7L 3N6 Canada

This paper presents an adaptive and intelligent power control approach for microgrid systems in the gridconnected operation mode. The proposed critic-based adaptive control system contains a neuro-fuzzy controller and a fuzzy critic agent. The fuzzy critic agent employs a reinforcement learning algorithm based on neuro-dynamic programming. The system feedback is made available to the critic agent's input as the controller's action in the previous state. The evaluation or reinforcement signal produced by the critic agent together with the back-propagation of error is then used for online tuning of the output layer weights of the neuro-fuzzy controller. The proposed controller shows superior results compared with the traditional PI control. The transient response time is significantly reduced, power oscillations are eliminated, and fast convergence is achieved. The simple design and improved dynamic behavior of the proposed controller make it a promising nominee for power control of microgrid systems.

关键词： Critic-based learning microgrids neuro-fuzzy control synchronous reference frame voltage sourced converters (VSCs)

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adaptive learning solution of the nonzero-sum differential game with unknown dynamics using adaptive dynamic programming

Adaptive learning solution of the nonzero-sum differential g...

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第28届中国控制与决策会议

作者： Chunbin Qin Hongfei Sun Xianxing Liu Jiaqi Chen The School of Computer and Information Engineering Henan University The College of Environment and Planning Henan University The School of Software Henan University

ISBN: (纸本)9781467397155

In this paper,a novel partially model-free adaptive dynamic programming(ADP) algorithm is presented to solve online the nonzero-sum differential games of continuous-time linear systems with unknown drift ***,by using the integral reinforcement learning technique,the partially model-free ADP algorithm is developed to solve online the set of coupled algebraic Riccati equation(ARE) underlying the game problem without the requirement of the complete knowledge of the system *** then,the convergence of the partially model-free ADP algorithm is proved by demonstrating that it is mathematically equivalent to the extended Kleiman's algorithm,previously proposed in the literature,that solves in an offline sense the set of coupled algebraic Riccati equation using the complete knowledge of the system ***,one example is given to demonstrate the efficiency of the proposed algorithm.

关键词： Nonzero-sum differential game adaptive dynamic programming Unknown drift dynamics

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