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Global adaptive dynamic programming for Continuous-Time Nonlinear Systems

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2015年第11期60卷 2917-2929页

作者： Jiang, Yu Jiang, Zhong-Ping The MathWorks Natick MA 01760 USA NYU Polytech Sch Engn Dept Elect & Comp Engn Brooklyn NY 11201 USA

This paper presents a novel method of global adaptive dynamic programming (ADP) for the adaptive optimal control of nonlinear polynomial systems. The strategy consists of relaxing the problem of solving the Hamilton-Jacobi-Bellman (HJB) equation to an optimization problem, which is solved via a new policy iteration method. The proposed method distinguishes from previously known nonlinear ADP methods in that the neural network approximation is avoided, giving rise to significant computational improvement. Instead of semiglobally or locally stabilizing, the resultant control policy is globally stabilizing for a general class of nonlinear polynomial systems. Furthermore, in the absence of the a priori knowledge of the system dynamics, an online learning method is devised to implement the proposed policy iteration technique by generalizing the current ADP theory. Finally, three numerical examples are provided to validate the effectiveness of the proposed method.

关键词： adaptive dynamic programming global stabilization nonlinear systems optimal control

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A Novel Online adaptive dynamic programming Algorithm With Adjustable Convergence Rate

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第3期71卷 1371-1384页

作者： Wang, Yonghua Zhang, Zheliang Zhang, Yongwei Liang, Mingming Liu, Derong Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

This article develops a novel online adaptive dynamic programming algorithm with adjustable convergence rate to address the optimal control problem of nonlinear systems. Relaxation factors are introduced to tune the convergence rate of value function sequence online. A novel update law based on recursive least squares is developed to adjust the weight of critic neural network at the sampling instant. The uniform ultimate boundedness of the neural network estimation error and the closed-loop system state are analyzed by utilizing the Lyapunov technique. Finally, the effectiveness of the present algorithm is demonstrated by executing three simulation examples.

关键词： adaptive dynamic programming discrete-time nonlinear systems convergence rate online optimal control neural networks

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Event-triggered trajectory-tracking guidance for reusable launch vehicle based on neural adaptive dynamic programming

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NEURAL COMPUTING & APPLICATIONS 2022年第21期34卷 18725-18740页

作者： Wang, Xueyun Quan, Zhiyuan Li, Yifan Liu, Yunpeng Beihang Univ Sch Instrument Sci & Optoelect Engn Beijing 100191 Peoples R China Beijing Aerosp Automat Control Inst Beijing 102200 Peoples R China

To improve the trajectory-tracking guidance performance for reusable launch vehicle under various uncertainties and distortions, an event-triggered (ET) guidance method based on neural adaptive dynamic programming (ADP) is proposed. Firstly, the reference trajectory and corresponding steady-state control are generated optimally offline based on Gauss pseudo-spectral method. Secondly, the approximate optimal feedback controller based on single-critic ADP is designed. The great adaptation capacity inheriting from reinforcement learning technique ensures the tracking errors to converge to zero, and yet no offline dataset or pre-training is required. Event-triggered mechanism is introduced to reduce online training computation and save data transmission resource. Event-triggered condition is designed and the asymptotic stability of the event-triggered guidance system is proved. Comprehensive simulations are conducted and results validate the effectiveness of the feedback controller based on ADP and the significantly improved efficiency of ET mechanism. Besides, the improved performance of the proposed guidance method over traditional method of linear quadratic regulator has also been verified through simulations.

关键词： adaptive dynamic programming Event-triggered control Approximate optimal control Trajectory-tracking guidance

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Web Service QoS Prediction Based on adaptive dynamic programming Using Fuzzy Neural Networks for Cloud Services

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IEEE ACCESS 2015年 3卷 2260-2269页

作者： Luo, Xiong Lv, Yixuan Li, Ruixing Chen, Yi Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Beijing Key Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China

Recently, more and more traditional services are being migrated into a cloud computing environment that makes the quality of service (QoS) becomes an important factor for service selection and optimal service composition while forming cross-cloud service applications. Considering the nonlinear and dynamic property of QoS data, it is so difficult to achieve dynamic prediction while designing a QoS prediction method with unsatisfactory prediction accuracy. It is thus desirable to explore how to design an effective approach by incorporating some intelligent techniques into the QoS prediction method to improve prediction performance. In this paper, motivated by the adaptive critic design and Q-learning technique, we propose a novel QoS prediction approach to serve this purpose through the combination of fuzzy neural networks and adaptive dynamic programming (ADP), i.e., an online learning scheme. This approach extracts fuzzy rules from QoS data and employs the ADP method to parameter learning of the fuzzy rules. Moreover, we provide a convergence boundedness result for our proposed approach to guarantee the stability. Experimental results on a large-scale QoS service data set verify the prediction accuracy of our proposed approach.

关键词： Quality of service (QoS) QoS prediction fuzzy neural network adaptive dynamic programming cloud services

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dynamic Event-Triggered Consensus Control for Multi-Agent Systems Using adaptive dynamic programming

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IEEE ACCESS 2022年 10卷 110285-110293页

作者： Zhang, Qi Yang, Yang Xie, Xiaoran Xu, Chunming Yang, Han Changchun Univ Sci & Technol Sch Elect Informat Engn Changchun 130013 Peoples R China Changchun Shikai Sci & Technol Ind Co Ltd Changchnu 130015 Peoples R China Beijing Engn Res Ctr Emergency Survival Secur Beijing 102206 Peoples R China

The consensus optimal control problem for a class of linear multi-agent systems with directed communication networks is studied in this paper using adaptive dynamic programming. To overcome the restrictions of the agent's low processing capability and to extend the actuator's lifetime, consider the event-triggered. In the beginning, a dynamic event-triggered is provided, with several existing static event-triggered serving as special examples. When using the dynamic event-triggered, a longer interval can be shown between any two consecutive event-triggered. Designing a dynamic event-triggered control law becomes more challenging when implemented in directed networks. In addition, on the basis of dynamic event-triggered, a novel adaptive dynamic programming is used to construct a suitable dynamic event-triggered control law, which employs just the interaction information between agents and does not need model dynamics, overcoming the difficulty of solving the algebraic Riccati equation. Finally, the effectiveness of the proposed method is verified by simulation results, and no agent exhibits Zeno behavior.

关键词： Multi-agent systems dynamic programming Behavioral sciences Optimal control Protocols Decentralized control Convergence Multi-agent systems event-triggered adaptive dynamic programming reinforcement learning

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Data-Based Online Optimal Temperature Tracking Control in Continuous Microwave Heating System by adaptive dynamic programming

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NEURAL PROCESSING LETTERS 2020年第1期51卷 167-191页

作者： Liu, Tong Liang, Shan Xiong, Qingyu Wang, Kai Chongqing Univ Key Lab Complex Syst Safety & Control Minist Educ Chongqing 400044 Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 401331 Peoples R China

Control of continuous microwave heating system (CMHS) is truly a complex problem with time variance, uncertainty and nonlinearity, which becomes prohibitive to use a conventional model-based approach. To overcome this, a novel data-based optimal temperature tracking control is designed for CMHS in this paper. In order to obtain the complex dynamics of CMHS, a neural network model is first constructed driven by process data. After transforming the original temperature tracking problem into an error regulation problem, adaptive dynamic programming is introduced to deal with the regulation problem as well as to decrease operation cost. The design and operation of this controller depend mainly on the online data, and minor prior knowledge is required. Simulation results show that the proposed method can effectively control the CMHS in terms of temperature tracking and energy utilization.

关键词： Continuous microwave heating system Data-based Optimal temperature tracking control adaptive dynamic programming Neural networks

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Finite horizon optimal control of non-linear discrete-time switched systems using adaptive dynamic programming with ε-error bound

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2014年第8期45卷 1683-1693页

作者： Qin, Chunbin Zhang, Huaguang Luo, Yanhong Wang, Binrui Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Henan Univ Basic Expt Teaching Ctr Kaifeng Henan Peoples R China China Jiliang Univ Coll Mech & Elect Engn Hangzhou Zhejiang Peoples R China

In this paper, we aim to solve the finite-horizon optimal control problem for a class of non-linear discrete-time switched systems using adaptive dynamic programming(ADP) algorithm. A new epsilon-optimal control scheme based on the iterative ADP algorithm is presented which makes the value function converge iteratively to the greatest lower bound of all value function indices within an error according to epsilon within finite time. Two neural networks are used as parametric structures to implement the iterative ADP algorithm with epsilon-error bound, which aim at approximating the value function and the control policy, respectively. And then, the optimal control policy is obtained. Finally, a simulation example is included to illustrate the applicability of the proposed method.

关键词： non-linear switched system adaptive dynamic programming optimal control finite horizon neural network

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Deterministic policy gradient adaptive dynamic programming for model-free optimal control

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NEUROCOMPUTING 2020年第0期387卷 40-50页

作者： Zhang, Yongwei Zhao, Bo Liu, Derong Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

In this paper, a deterministic policy gradient adaptive dynamic programming (DPGADP) algorithm is proposed for solving model-free optimal control problems of discrete-time nonlinear systems. By using the measured data, the developed algorithm improves the control performance with the policy gradient method. The convergence of DPGADP algorithm is demonstrated by showing that the constructed Q-function sequence is monotonically non-increasing and converges to the optimal Q-function. An actorcritic neural network (NN) structure is established to implement the DPGADP algorithm. Experience replay and target network techniques from deep Q-learning are employed during the training process. The stability of the NN weight error dynamics is also analyzed. Finally, two simulation examples are presented to verify the effectiveness of the proposed method. (C) 2019 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Deterministic policy gradient Optimal control Neural networks Model-free

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A novel optimal tracking control scheme for a class of discrete-time nonlinear systems using generalised policy iteration adaptive dynamic programming algorithm

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2017年第3期48卷 525-534页

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

In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm, called generalised policy iteration ADP algorithm, is developed to solve optimal tracking control problems for discrete-time nonlinear systems. The idea is to use two iteration procedures, including an i-iteration and a j-iteration, to obtain the iterative tracking control laws and the iterative value functions. By system transformation, we first convert the optimal tracking control problem into an optimal regulation problem. Then the generalised policy iteration ADP algorithm, which is a general idea of interacting policy and value iteration algorithms, is introduced to deal with the optimal regulation problem. The convergence and optimality properties of the generalised policy iteration algorithm are analysed. Three neural networks are used to implement the developed algorithm. Finally, simulation examples are given to illustrate the performance of the present algorithm.

关键词： adaptive dynamic programming affine nonlinear systems discrete-time generalised policy iteration neural network tracking control

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Modified general policy iteration based adaptive dynamic programming for unknown discrete-time linear systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第12期32卷 7149-7173页

作者： Jiang, Huaiyuan Zhou, Bin Duan, Guang-Ren Harbin Inst Technol Ctr Control Theory & Guidance Technol Harbin 150001 Peoples R China

In this article, the general policy iteration (GPI) method for the optimal control of discrete-time linear systems is studied. First, the existing result on the GPI method is recalled and some new properties are proposed. Based on these new properties, a model-based modified GPI algorithm is proposed with its convergence proof. Moreover, the data-driven implementation for the proposed method is introduced without using the information of the system matrices. Compared with the existing results, the condition to initiate the GPI based algorithm is further relaxed by the proposed algorithm. The simulation example verifies the effectiveness of the proposed modified GPI based algorithm.

关键词： adaptive dynamic programming data-driven control discrete-time systems general policy iteration policy iteration unknown systems

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