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检索条件"主题词=Adaptive Dynamic Programming"

共 1125 条记录，以下是861-870 订阅

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A Combined Policy Gradient and Q-learning Method for Data-driven Optimal Control Problems 9

A Combined Policy Gradient and Q-learning Method for Data-dr...

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9th International Conference on Information Science and Technology (ICIST)

作者： Lin, Mingduo Liu, Derong Zhao, Bo Dai, Qionghai Dong, Yi Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Beijing Normal Univ Sch Syst Sci Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Inst Technol Sch Opt & Photon Beijing Peoples R China

ISBN: (纸本)9781728121062

This paper focuses on the data-driven controller design for optimal control problems of nonlinear nonaffine discrete-time systems. A novel policy gradient and Q-learning (PGQL) adaptive algorithm which learns the optimal control policy from real empirical data is developed without requiring system dynamics. A policy iteration scheme is designed to iteratively update the approximate Q-function, and the control policy is improved via gradient method until they converge to the bounded regions of the optimal Q-function and the optimal control policy, respectively. Two neural networks (NNs) are employed to realize the developed algorithm. Moreover, the convergence analysis of approximate Q-function is established. Since the control policy is parameterized, it can be upgraded through updating the actor-NN parameters in the direction of the performance gradient. Finally, the simulation results are given to verify the performance of the developed PGQL adaptive algorithm.

关键词： adaptive dynamic programming optimal control reinforcement learning policy gradient Q-learning data-driven

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Reinforcement Learning With Adjustable Convergence Rate for Data-Based Nonlinear Control 36

Reinforcement Learning With Adjustable Convergence Rate for ...

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36th Chinese Control and Decision Conference (CCDC)

作者： Tang, Guohan Wang, Ding Li, Xin Ren, Jin Liu, Nan Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9798350387780;9798350387797

In this paper, a value-iteration-based off-policy Q-learning algorithm is developed. The proposed algorithm solves the optimal regulation problem of nonlinear systems with unknown dynamics. Under the off-policy mechanism, the algorithm utilizes the behavioral policy for full exploration, which is beneficial to avoid the target policy from falling into the local optimal solution. In addition, a relaxation factor is introduced to adjust the convergence rate of the cost function sequence. To implement the algorithm, the critic network and the action network are used to approximate the optimal Q-function and the optimal control policy, respectively. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed algorithm.

关键词： adaptive dynamic programming convergence rate discrete-time nonlinear systems Q-learning value iteration

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adaptive temperature tracking control of microwave heating high titanium slag based on ε-ADP 34

Adaptive temperature tracking control of microwave heating h...

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34th Chinese Control and Decision Conference (CCDC)

作者： Yang, Biao Du, Wan Li, Xinpei Kunming Univ Sci & Technol Kunming Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kuiuning Univ Sci & Technol Key Lab Unconvent Met Minist Educ Kunming 650093 Yunnan Peoples R China

ISBN: (纸本)9781665478960

A data-driven model-based microwave heating adaptive dynamic planning temperature tracking algorithm is proposed for the microwave heating process with time-varying nonlinearity and strong coupling. The microwave heating process is modeled using a single hidden-layer feedforward network and the discrimination capability of an extreme learning machine. Based on this, the performance index functions and control laws are approximated using the approximation capability of the single hidden layer feedforward network and the extreme learning machine. Experimental and simulation results show that the proposed algorithm successfully tracks the temperature of high titanium slag during the microwave heating process.

关键词： Microwave heating High titanium slag adaptive dynamic programming Extreme learning machine Temperature tracking control

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Finite Horizon Optimal Tracking Control for a Class of Discrete-Time Nonlinear Systems

Finite Horizon Optimal Tracking Control for a Class of Discr...

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8th International Symposium on Neural Networks

作者： Wei, Qinglai Wang, Ding Liu, Derong Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9783642210891

In this paper, a new iterative ADP algorithm is proposed to solve the finite horizon optimal tracking control problem for a class of discrete-time nonlinear systems. The idea is that using system transformation, the optimal tracking problem is transformed into optimal regulation problem, and then the iterative ADP algorithm is introduced to deal with the regulation problem with convergence guarantee. Three neural networks are used to approximate the performance index function, compute the optimal control policy and model the unknown system dynamics, respectively, for facilitating the implementation of iterative ADP algorithm. An example is given to demonstrate the validity of the proposed optimal tracking control scheme.

关键词： adaptive dynamic programming approximate dynamic programming optimal tracking control neural networks finite horizon

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Distributed Optimal Consensus Control for Coupled Linear Systems Based on Off-Policy Integral Reinforcement Learning 39

Distributed Optimal Consensus Control for Coupled Linear Sys...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Zhao, Wenyan Liu, Zhongchang Yang, Xin Dalian Maritime Univ Coll Marine Elect Engn Dalian Peoples R China

ISBN: (纸本)9798350390780;9798350379228

This article studies the consensus problem for coupled linear multi-agent systems with completely unknown system dynamics. These coupled individual agent systems aim to achieve consensus for their system states while minimizing a novel type of cost function which can result in distributed control law for each agent system. The optimal distributed controller of each agent is designed using adaptive dynamic programming (ADP). The decoupled value function is derived first, and the model-based ARE is solved iteratively using policy iteration method. Without knowing the system models, the off-policy integral reinforcement learning method is applied, and a linear regression equation is solved to derive the optimal control gains from data collected online. Simulation results verify the effectiveness of the designed algorithm.

关键词： coupled linear systems optimal consensus control reinforcement learning adaptive dynamic programming

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A Comparison of Learning Speed and Ability to Cope Without Exploration between DHP and TD(0)

A Comparison of Learning Speed and Ability to Cope Without E...

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International Joint Conference on Neural Networks (IJCNN)

作者： Fairbank, Michael Alonso, Eduardo City Univ London Dept Comp Sch Informat London EC1V 0HB England

ISBN: (纸本)9781467314909

This paper demonstrates the principal motivations for Dual Heuristic dynamic programming (DHP) learning methods for use in adaptive dynamic programming and Reinforcement Learning, in continuous state spaces: that of automatic local exploration, improved learning speed and the ability to work without stochastic exploration in deterministic environments. In a simple experiment, the learning speed of DHP is shown to be around 1700 times faster than TD(0). DHP solves the problem without any exploration, whereas TD(0) cannot solve it without explicit exploration. DHP requires knowledge of, and differentiability of, the environment's model functions. This paper aims to illustrate the advantages of DHP when these two requirements are satisfied.

关键词： Dual Heuristic dynamic programming DHP adaptive dynamic programming Reinforcement Learning

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Data-Based Nonaffine Optimal Tracking Control Using Iterative DHP Approach 21st

Data-Based Nonaffine Optimal Tracking Control Using Iterativ...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Ha, Mingming Wang, Ding Liu, Derong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

In this paper, a data-based optimal tracking control approach is developed by involving the iterative dual heuristic dynamic programming algorithm for nonaffine systems. In order to gain the steady control corresponding to the desired trajectory, a novel strategy is established with regard to the unknown system function. Then, according to the iterative adaptive dynamic programming algorithm, the updating formula of the costate function and the new optimal control policy for unknown nonaffine systems are provided to solve the optimal tracking control problem. Moreover, three neural networks are used to facilitate the implementation of the proposed algorithm. In order to improve the accuracy of the steady control corresponding to the desired trajectory, we employ a model network to directly approximate the unknown system function instead of the error dynamics. Finally, the effectiveness of the proposed method is demonstrated through a simulation example. Copyright (C) 2020 The Authors.

关键词： adaptive dynamic programming data-based optimal tracking control iterative dual heuristic dynamic programming neural network

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Improvement of LMI controllers of Takagi-Sugeno models via Q-learning

Improvement of LMI controllers of Takagi-Sugeno models via Q...

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4th IFAC Conference on Intelligent Control and Automation Sciences (ICONS)

作者： Diaz, Henry Armesto, Leopoldo Sala, Antonio Univ Politecn Valencia IDF Inst Univ Autom Inf Ind AI2 C Camino Vera S-N E-46022 Valencia Spain

This paper presents a preliminary attempt to bridge the conservative (shape-independent) results from guaranteed-cost LMIs and the reinforcement learning setups which learn optimal controllers from data. In this sense, the proposed approach uses an initialization based on the LMI solution and proposes an approximation of the Q-function using polynomials of the membership functions in Takagi-Sugeno models. The resulting controller is shape-dependent, that is, uses the knowledge of membership functions and data to clearly improve LMI solutions. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Reinforcement learning adaptive dynamic programming Q-learning Takagi-Sugeno LMI

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Decentralized Tracking Control of Interconnected Nonlinear Systems with Unmatched External Disturbances 43

Decentralized Tracking Control of Interconnected Nonlinear S...

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43rd Chinese Control Conference (CCC)

作者： Yuan, Zeqiang Wang, Ding Liu, Ao Hu, Qinna Zhou, Zihang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

In this paper, we develop a decentralized tracking control (DTC) strategy to stabilize a class of continuous-time nonlinear interconnected large-scale systems in the presence of unmatched external disturbances. This strategy is derived from an online learning approach of optimal control. Initially, the DTC problem is formulated to ensure both tracking control of isolated subsystems and the overall stability of the large-scale system. By combining the tracking error with the reference trajectory, some augmented systems are constructed and then the DTC design is transformed into an optimal control problem. Considering external disturbances, we formulated it as a two-player zero-sum game. Critic neural networks are utilized to solve the Hamilton-Jacobi-Isaacs equation. This approach allows for the estimation of Nash equilibrium solution that encompasses both the optimal control law and the worst-case disturbance law. Notably, a novel gradient descent strategy with momentum is established to tune the weights of the critic neural network for approximating the cost function better. Finally, an experimental simulation is provided to verify the effectiveness of the developed DTC scheme.

关键词： adaptive dynamic programming Decentralized control H-infinity control Reinforcement learning Tracking control

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Constrained Robust Optimal Sliding Mode Control for Uncertain Nonlinear Systems Using ADP Approach 6

Constrained Robust Optimal Sliding Mode Control for Uncertai...

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2017 IEEE 6th Data Driven Control and Learning Systems Conference (DDCLS’17)

作者： Qiuxia Qu Huaguang Zhang Rui Yu Geyang Xiao School of Information Science and Engineering Northeastern University

ISBN: (纸本)9781509054626

In this paper, a novel constrained composite sliding mode controller is studied for uncertain nonlinear systems with input saturation. Based on integral sliding mode and approximate dynamic programming theory, the proposed sliding mode control consists of discontinuous control used for completely compensating matched uncertainties and continuous control that results in an optimal sliding mode dynamics. Without using linearization techniques, the constrained optimal control law for nominal nonlinear systems is approximated by using an online approximate learning algorithm with actor-critic NN *** techniques are used to demonstrate the uniform ultimate bounded convergence condition for closed-loop nominal system and the weight errors.

关键词： Sliding mode control Optimal control adaptive dynamic programming Nonlinear systems Input saturation

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