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第33届中国控制与决策会议

作者： Qiuxia Qu Juan Wang QiongXia Liangliang Sun Yang Cui School of Information and Control Engineering Shenyang Jianzhu University School of Electronic and Information Engineering University of Science and Technology Liaoning

Considering the leader-following consensus problem for the nonlinear multi-agent systems with bounded input-disturbances under fixed topology,a novel distributed robust protocol is designed to guarantee all followers synchronize to the leader by investigating the gain of the Nash *** robustness restrictions are given through Lyapunov *** get the Nash solution,critic neural networks are trained based on adaptive dynamic programming algorithm in an online and forward-in-time manner to solve the coupled Hamilton-Jacobi *** additional term is added to the neural network weight tuning law to avoid the requirement for the initial admissible control law.

关键词： adaptive dynamic programming Input-disturbance Leader-following consensus problem Multi-agent systems Nash equilibrium Neural network

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adaptive dynamic programming for discrete-time linear quadratic regulation based on multirate generalised policy iteration

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INTERNATIONAL JOURNAL OF CONTROL 2018年第6期91卷 1223-1240页

作者： Chun, Tae Yoon Lee, Jae Young Park, Jin Bae Choi, Yoon Ho Yonsei Univ Sch Elect & Elect Engn Seoul South Korea Univ Alberta Dept Comp Sci Edmonton AB Canada Kyonggi Univ Dept Elect Engn Suwon South Korea

In this paper, we propose two multirate generalised policy iteration (GPI) algorithms applied to discrete-time linear quadratic regulation problems. The proposed algorithms are extensions of the existing GPI algorithm that consists of the approximate policy evaluation and policy improvement steps. The two proposed schemes, named heuristic dynamic programming (HDP) and dual HDP (DHP), based on multirate GPI, use multi-step estimation (M-step Bellman equation) at the approximate policy evaluation step for estimating the value function and its gradient called costate, respectively. Then, we show that these two methods with the same update horizon can be considered equivalent in the iteration domain. Furthermore, monotonically increasing and decreasing convergences, so called value iteration (VI)-mode and policy iteration (PI)-mode convergences, are proved to hold for the proposed multirate GPIs. Further, general convergence properties in terms of eigenvalues are also studied. The data-driven online implementation methods for the proposed HDP and DHP are demonstrated and finally, we present the results of numerical simulations performed to verify the effectiveness of the proposed methods.

关键词： Multirate generalised policy iteration heuristic dynamic programming dual heuristic dynamic programming adaptive dynamic programming mixed-mode convergence linear quadratic regulation

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Stable value iteration for two-player zero-sum game of discrete-time nonlinear systems based on adaptive dynamic programming

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NEUROCOMPUTING 2019年 340卷 180-195页

作者： Song, Ruizhuo Zhu, Liao Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

In this paper, a stable value iteration (SVI) algorithm is developed to solve discrete-time two-player zero-sum game (TP-ZSG) for nonlinear systems based on adaptive dynamic programming (ADP). In the SVI algorithm, both optimality and stability of nonlinear systems are considered with proofs given. First, an iterative ADP algorithm is presented to obtain the approximate optimal solutions by solving Hamilton-Jacobi-Isaacs (HJI) equation. Second, a range of the discount factor is shown, which guarantees HJI equation serving as a Lyapunov equation. Moreover, we prove that if the iteration number reaches a given number, then the iterative control inputs make the closed-loop system asymptotic stable. Third, in order to improve the practicability of the developed stability condition, a simple criteria is established based on Lyapunov stability theory. Neural networks (NNs) are used to approximate the system states, the value function, the control and disturbance inputs. Finally, simulation results are given to illustrate the performance of the developed optimal control method. (C) 2019 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Neural network-based Zero-sum game

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Connected cruise control with delayed feedback and disturbance: An adaptive dynamic programming approach

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INTERNATIONAL JOURNAL OF adaptive CONTROL AND SIGNAL PROCESSING 2019年第2期33卷 356-370页

作者： Huang, Mengzhe Gao, Weinan Jiang, Zhong-Ping NYU Dept Elect & Comp Engn Tandon Sch Engn Control & Networks Lab Brooklyn NY 11201 USA

This paper studies the connected cruise control problem for a platoon of human-operated and autonomous vehicles. The autonomous vehicles can receive motional data, ie, headway and velocity information from other vehicles by wireless vehicle-to-vehicle communication. The use of wireless communications in information exchange between vehicles inevitably causes input delay in the platooning system. Meanwhile, unpredictable behaviors of the leading vehicle constitute exogenous disturbance for the system. An adaptive optimal control problem with input delay and disturbance is formulated, and a novel data-driven control solution is proposed such that each vehicle in the platoon can achieve safe distance and desired velocity. By adopting an adaptive dynamic programming technique with sampled-data system theory, a data-driven adaptive optimal control approach is proposed for autonomous vehicles by the learning strategies of policy iteration without the accurate knowledge of the dynamics of all human drivers and vehicles. The efficacy of the proposed controller is substantiated by rigorous analysis and validated by simulation results in different scenarios.

关键词： adaptive dynamic programming connected vehicles time-delayed input

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Ascent trajectory tracking method using time-varying quadratic adaptive dynamic programming

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2019年第11期233卷 4154-4165页

作者： Sun, Zhengxiang Chao, Tao Wang, Songyan Yang, Ming Harbin Inst Technol Control & Simulat Ctr Harbin 150001 Heilongjiang Peoples R China

Ascent trajectory tracking in the longitudinal plane is a class of nonaffine noncascade nonlinear system control problems with single input and multiple output, which is difficult to control by nonlinear method directly. adaptive dynamic programming algorithm has the advantages of precise control and adaptability for general nonlinear control problem, and the time-varying quadratic adaptive dynamic programming algorithm proposed in this paper promotes the convergence and calculation speed of the traditional adaptive dynamic programming algorithm. To implement the algorithm effectively, the independent variables of ascent model are substituted in the launching coordinate, and then the model is treated as a discrete nominal trajectory tracking problem. Besides, the heuristic dynamic programming structure is used to train the processed model, and thus only the time-varying weight in the designed evaluation network needs to be updated. Simulation shows that the proposed algorithm can update the control variable online after predicting the cost function offline with the dynamic equations, which is faster than the general adaptive dynamic programming algorithm. In addition, the proposed algorithm can effectively and accurately track the nominal trajectory under the uncertainty of parameters compared with the linear quadratic regulators algorithm.

关键词： adaptive dynamic programming ascent trajectory tracking optimal control time-varying quadratic polynomial neural network

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Distributed optimal coordination control for nonlinear multi-agent systems using event-triggered adaptive dynamic programming method

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ISA TRANSACTIONS 2019年 91卷 184-195页

作者： Zhao, Wei Zhang, Huaipin Southeast Univ Sch Math Nanjing 210096 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Jiangsu Peoples R China

This paper is concerned with the design of distributed optimal coordination control for nonlinear multi agent systems (NMASs) based on event-triggered adaptive dynamic programming (ETADP) method. The method is firstly introduced to design the distributed coordination controllers for NMASs, which not only avoids the transmission of redundant data compared with traditional time-triggered adaptive dynamic programming (TTADP) strategy and minimizes the performance function of each agent. The event-triggered conditions are proposed based on Lyapunov functional method, which is deduced by guaranteeing the stability of NMASs. Then a new adaptive policy iteration algorithm is presented to obtain the online solutions of the Hamiton-Jocabi-Bellman (HJB) equations. In order to implement the proposed ETADP method, the fuzzy hyperbolic model based critic neural networks (NN) are utilized to approximate the value functions and help calculate the control policies. In critic NNs, the NN weight estimations are updated at the event-triggered instants leading to aperiodic weight tuning laws so that computation cost is reduced. It is proved that the weight estimation errors and the local neighborhood coordination errors is uniformly ultimately bounded (UUB). Finally, two simulation examples are provided to show the effectiveness of the proposed ETADP method. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Multi-agent systems Event-triggered sampling Distributed optimal coordination control adaptive dynamic programming

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Data-driven adaptive dynamic programming for partially observable nonzero-sum games via Q-learning method

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2019年第7期50卷 1338-1352页

作者： Wang, Wei Chen, Xin Fu, Hao Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China

This paper concerns with a class of discrete-time linear nonzero-sum games with the partially observable system state. As is known, the optimal control policy for the nonzero-sum games relies on the full state measurement which is hard to fulfil in partially observable environment. Moreover, to achieve the optimal control, one needs to know the accurate system model. To overcome these deficiencies, this paper develops a data-driven adaptive dynamic programming method via Q-learning method using measurable input/output data without any system knowledge. First, the representation of the unmeasurable inner system state is built using historical input/output data. Then, based on the representation state, a Q-function-based policy iteration approach with convergence analysis is introduced to approximate the optimal control policy iteratively. A neural network (NN)-based actor-critic framework is applied to implement the developed data-driven approach. Finally, two simulation examples are provided to demonstrate the effectiveness of the developed approach.

关键词： adaptive dynamic programming nonzero-sum games partially observable Q-learning

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Design and implementation of an optimal switching controller for uninterruptible power supply inverters using adaptive dynamic programming

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IET POWER ELECTRONICS 2019年第12期12卷 3068-3076页

作者： Gogani Khiabani, Ataollah Heydari, Ali Southern Methodist Univ Dept Mech Engn 6425 Boaz Lane Dallas TX 75205 USA

In this study, a new approach based on adaptive dynamic programming (ADP) is proposed to control single-phase uninterruptible power supply inverters. The control scheme uses a single function approximator, called critic, to evaluate the optimal cost and determine the optimal switching. After offline training of the critic, which is a function of system states and elapsed time, the resulting optimal weights are used in online control, to get a smooth output AC voltage in a feedback form. Simulations show the desirable performance of this controller with linear and non-linear loads and its relative robustness to parameter uncertainty and disturbances. Furthermore, the proposed controller is upgraded so that the inverter is suitable for single-phase variable frequency drives. Finally, as one of the few studies in the field of ADP, the proposed controllers are implemented on a physical prototype to show the performance in practise.

关键词： dynamic programming invertors variable speed drives function approximation uninterruptible power supplies feedback optimal switching controller ADP adaptive dynamic programming single-phase uninterruptible power supply inverters control scheme single function approximator called critic optimal cost offline training system states elapsed time resulting optimal weights online control smooth output AC voltage nonlinear loads inverter single-phase variable frequency drives

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Control-Limited adaptive dynamic programming for Multi-Battery Energy Storage Systems

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IEEE TRANSACTIONS ON SMART GRID 2019年第4期10卷 4235-4244页

作者： Zhu, Yuanheng Zhao, Dongbin Li, Xiangjun Wang, Ding Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China China Elect Power Res Inst State Key Lab Control & Operat Renewable Energy & Beijing 100192 Peoples R China

When an energy storage system comprises multiple batteries, the optimal scheduling of charging/discharging actions must take into account their different characteristics. To prolong battery lifetime, each battery must stay in their safety zones. Since load demand and energy price vary over time, the value function that reflects future power cost is both state- and time-dependent. To address this control-limited optimization problem, a new adaptive dynamic programming algorithm is proposed. The time-varying optimal value function is subdivided into a sequence of time-invariant functions which are solved by a periodic value iteration. These functions are further approximated by fuzzy systems, and the optimal charging/discharging actions are searched by the projected Newton method. Experiment results show the new algorithm is able to coordinate multiple batteries efficiently in their constrained control space.

关键词： Microgrid energy storage system multi-battery management system adaptive dynamic programming control-limited optimization

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Modeling city logistics using adaptive dynamic programming based multi-agent simulation

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2019年 125卷 74-96页

作者： Firdausiyah, N. Taniguchi, E. Qureshi, A. G. Brawijaya Univ Malang 65145 Indonesia Kyoto Univ Nishikyo Ku Kyoto 6158540 Japan

The effects of city logistics solutions are uncertain due to fluctuating demand, parking issues and multiple agents within the system. This research modelled the behavior of freight carriers and an Urban Consolidation Center (UCC) operator using Multi-Agent Simulation-adaptive dynamic programming based Reinforcement Learning (MAS-ADP based RL) to evaluate a Joint Delivery Systems in an uncertain environment. The MAS-ADP based RL is superior to MAS-Q-learning in replicating the potential actions of the agents under uncertain environment by adapting to the changing environment properly into accurate decisions thus increasing the accuracy of agent's decision making and eventually reducing environmental emissions as well.

关键词： adaptive dynamic programming Multi-agent simulation Reinforcement learning City logistics Urban consolidation center

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