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Online routing and battery reservations for electric vehicles with swappable batteries

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2014年 70卷 285-302页

作者： Adler, Jonathan D. Mirchandani, Pitu B. Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA

Electric vehicles are becoming a more popular form of transportation, however their limited range has proven problematic. Battery-exchange stations allow the vehicles to swap batteries during their trip, but if a vehicle arrives at a station without a full battery available it may have to wait an extended period of time to get one. The vehicles can be routed so that they avoid stations without available batteries or to keep batteries available for other vehicles that need them in the future. The batteries can also be reserved during the routing process so that each vehicle is ensured the battery it plans to use is available. This paper provides a method of online routing of electric vehicles and making battery reservations that minimizes the average delay of the all vehicles by occasionally detouring them to the benefit of future ones. The system is modeled as a Markov chance-decision process and the optimal policy is approximated using the approximate dynamic programming technique of temporal differencing with linear models. The solution algorithm provides a quick way for vehicles to be routed using onboard vehicle software connected to a central computer. Computational results for the algorithm are provided using data on the Arizona highway network. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Electric vehicles approximate dynamic programming Vehicle routing Markov chance-decision processes Linear temporal differencing

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Stable iterative adaptive dynamic programming algorithm with approximation errors for discrete-time nonlinear systems

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NEURAL COMPUTING & APPLICATIONS 2014年第6期24卷 1355-1367页

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

In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.

关键词： Adaptive dynamic programming approximate dynamic programming Adaptive critic designs Optimal control Neural networks Nonlinear systems

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Policy Iteration Adaptive dynamic programming Algorithm for Discrete-Time Nonlinear Systems

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2014年第3期25卷 621-634页

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

This paper is concerned with a new discrete-time policy iteration adaptive dynamic programming (ADP) method for solving the infinite horizon optimal control problem of nonlinear systems. The idea is to use an iterative ADP technique to obtain the iterative control law, which optimizes the iterative performance index function. The main contribution of this paper is to analyze the convergence and stability properties of policy iteration method for discrete-time nonlinear systems for the first time. It shows that the iterative performance index function is nonincreasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman equation. It is also proven that any of the iterative control laws can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control law, respectively, for facilitating the implementation of the iterative ADP algorithm, where the convergence of the weight matrices is analyzed. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method.

关键词： Adaptive critic designs adaptive dynamic programming (ADP) approximate dynamic programming discrete-time policy iteration neural networks neurodynamic programming nonlinear systems optimal control reinforcement learning

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Online Synchronous approximate Optimal Learning Algorithm for Multiplayer Nonzero-Sum Games With Unknown dynamics

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2014年第8期44卷 1015-1027页

作者： Liu, Derong Li, Hongliang Wang, Ding Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, we develop an online synchronous approximate optimal learning algorithm based on policy iteration to solve a multiplayer nonzero-sum game without the requirement of exact knowledge of dynamical systems. First, we prove that the online policy iteration algorithm for the nonzero-sum game is mathematically equivalent to the quasi-Newton's iteration in a Banach space. Then, a model neural network is established to identify the unknown continuous-time nonlinear system using input-output data. For each player, a critic neural network and an action neural network are used to approximate its value function and control policy, respectively. Our algorithm only needs to tune the weights of critic neural networks, so there will be less computational complexity during the learning process. All the neural network weights are updated online in real-time, continuously and synchronously. Furthermore, the uniform ultimate bounded stability of the closed-loop system is proved based on Lyapunov approach. Finally, two simulation examples are given to demonstrate the effectiveness of the developed scheme.

关键词： Adaptive dynamic programming (ADP) approximate dynamic programming multiplayer nonzero-sum games neural networks neuro-dynamic programming policy iteration

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Stabilization of Stochastic Iterative Methods for Singular and Nearly Singular Linear Systems

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MATHEMATICS OF OPERATIONS RESEARCH 2014年第1期39卷 1-30页

作者： Wang, Mengdi Bertsekas, Dimitri P. MIT Lab Informat & Decis Syst Cambridge MA 02139 USA

We consider linear systems of equations, Ax = b, with an emphasis on the case where A is singular. Under certain conditions, necessary as well as sufficient, linear deterministic iterative methods generate sequences {x(k)} that converge to a solution as long as there exists at least one solution. This convergence property can be impaired when these methods are implemented with stochastic simulation, as is often done in important classes of large-scale problems. We introduce additional conditions and novel algorithmic stabilization schemes under which {x(k)} converges to a solution when A is singular and may also be used with substantial benefit when A is nearly singular.

关键词： stochastic algorithm singular system stabilization projected equation simulation regularization approximate dynamic programming

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Near Optimal Output Feedback Control of Nonlinear Discrete-time Systems Based on Reinforcement Neural Network Learning

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IEEE/CAA Journal of Automatica Sinica 2014年第4期1卷 372-384页

作者： Qiming Zhao Hao Xu Sarangapani Jagannathan the DENSO International America Inc. with the College of Science and Engineering Texas A&M University the Department of Electrical&Computer Engineering Missouri University of Science and Technology

In this paper, the output feedback based finitehorizon near optimal regulation of nonlinear affine discretetime systems with unknown system dynamics is considered by using neural networks(NNs) to approximate Hamilton-JacobiBellman(HJB) equation solution. First, a NN-based Luenberger observer is proposed to reconstruct both the system states and the control coefficient matrix. Next, reinforcement learning methodology with actor-critic structure is utilized to approximate the time-varying solution, referred to as the value function, of the HJB equation by using a NN. To properly satisfy the terminal constraint, a new error term is defined and incorporated in the NN update law so that the terminal constraint error is also minimized over time. The NN with constant weights and timedependent activation function is employed to approximate the time-varying value function which is subsequently utilized to generate the finite-horizon near optimal control policy due to NN reconstruction errors. The proposed scheme functions in a forward-in-time manner without offline training phase. Lyapunov analysis is used to investigate the stability of the overall closedloop system. Simulation results are given to show the effectiveness and feasibility of the proposed method.

关键词： Approximation methods Artificial neural networks Feedback Learning (artificial intelligence) Nonlinear dynamical systems Observers Optimal control Finite-horizon Hamilton-Jacobi-Bellman equation approximate dynamic programming neural network optimal regulation Optimal control Approximation method Output Feedback Control Feedback Nonlinear dynamical systems Observers System dynamics Artificial neural networks Neural network dynamic programming

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A Clustering-Based Graph Laplacian Framework for Value Function Approximation in Reinforcement Learning

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第12期44卷 2613-2625页

作者： Xu, Xin Huang, Zhenhua Graves, Daniel Pedrycz, Witold Natl Univ Def Technol Coll Mechatron & Automat Changsha 410073 Hunan Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

In order to deal with the sequential decision problems with large or continuous state spaces, feature representation and function approximation have been a major research topic in reinforcement learning (RL). In this paper, a clustering-based graph Laplacian framework is presented for feature representation and value function approximation (VFA) in RL. By making use of clustering-based techniques, that is, K-means clustering or fuzzy C-means clustering, a graph Laplacian is constructed by subsampling in Markov decision processes (MDPs) with continuous state spaces. The basis functions for VFA can be automatically generated from spectral analysis of the graph Laplacian. The clustering-based graph Laplacian is integrated with a class of approximation policy iteration algorithms called representation policy iteration (RPI) for RL in MDPs with continuous state spaces. Simulation and experimental results show that, compared with previous RPI methods, the proposed approach needs fewer sample points to compute an efficient set of basis functions and the learning control performance can be improved for a variety of parameter settings.

关键词： approximate dynamic programming clustering learning control Markov decision processes reinforcement learning value function approximation

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Risk aversion and adaptive management: Insights from a multi-armed bandit model of invasive species risk

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JOURNAL OF ENVIRONMENTAL ECONOMICS AND MANAGEMENT 2014年第2期68卷 226-242页

作者： Springborn, Michael R. Univ Calif Davis Dept Environm Sci & Policy Davis CA 95616 USA

This article explores adaptive management (AM) for decision-making under environmental uncertainty. In the context of targeting invasive species inspections of agricultural imports, I find that risk aversion increases the relative value of AM and can increase the rate of exploratory action. While calls for AM in natural resource management are common, many analyses have identified modest gains from this approach. I analytically and numerically examine the distribution of outcomes from AM under risk neutrality and risk aversion. The inspection decision is framed as a multi-armed bandit problem and solved using the Lagrangian decomposition method. Results show that even when expected gains are modest, asymmetry in the distribution of outcomes has important implications. Notably, AM can serve to buffer against large losses, even if the most likely outcome is a small loss. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Adaptive management approximate dynamic programming Multi-armed bandit Decision-making under uncertainty Bayesian learning Invasive species Risk aversion

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approximate Optimal Tracking Control for Continuous-Time Unknown Nonlinear Systems 33

Approximate Optimal Tracking Control for Continuous-Time Unk...

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33rd Chinese Control Conference (CCC)

作者： Na, Jing Lv, Yongfeng Wu, Xing Guo, Yu Chen, Qiang Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China

ISBN: (纸本)9789881563842

This paper proposes an online adaptive approximate solution for the infinite-horizon optimal tracking control for continuous-time nonlinear systems with unknown system dynamics, which is achieved in terms of a novel identifier-critic based approximate dynamic programming (ADP) structure. To obviate the requirement of complete knowledge of system dynamics, an adaptive neural network (NN) identifier is designed with a novel adaptive law. A steady-state control in conjunction with an adaptive optimal control is proposed to stabilize the tracking error dynamics in an optimal manner. A critic NN is utilized to approximate the optimal value function and to obtain the optimal control action. Novel adaptive laws based on parameter estimation error are developed to guarantee that both the identifier NN weights and critic NN weights converge to small neighborhoods of their ideal values. The closed-loop system stability and the convergence to the optimal solution are all proved based on Lyapunov theory. Simulation results exemplify the efficacy of the proposed methods.

关键词： Adaptive control Optimal control approximate dynamic programming System identification

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Optimal Learning Control for Discrete-Time Nonlinear Systems Using Generalized Policy Iteration Based Adaptive dynamic programming 11

Optimal Learning Control for Discrete-Time Nonlinear Systems...

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11th World Congress on Intelligent Control and Automation

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

ISBN: (纸本)9781479958252

In this paper, a novel generalized policy iteration algorithm is investigated to solve infinite horizon optimal control problems for discrete-time nonlinear systems. Two iteration indices are introduced in the generalized policy iteration algorithm, which iterate for policy improvement and policy evaluation, respectively. For the first time the properties of monotonicity, convergence and admissibility for the generalized policy iteration algorithm are analyzed to guarantee that the iterative performance index function converges to the optimum and the iterative control law stabilizes the control system. Finally, numerical results are presented to illustrate the performance of the developed method.

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

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