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检索条件"主题词=Adaptive Dynamic Programming"

共 1118 条记录，以下是901-910 订阅

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Data-Based Self-Learning Optimal Control for Continuous-Time Unknown Nonlinear Systems With Disturbance 28

Data-Based Self-Learning Optimal Control for Continuous-Time...

引用

28th Chinese Control and Decision Conference

作者： Wei, Qinglai Liu, Derong Song, Ruizhuo Yan, Pengfei Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9781467397148

In this paper, a new data-based self-learning control scheme is developed to solve infinite horizon optimal control problems for continuous-time nonlinear systems. The developed optimal control scheme can be implement without knowing the mathematical model of the system. According to the input-output data of the nonlinear systems, a recurrent neural network (RNN) is employed to reconstruct the dynamics of the nonlinear system. According to the RNN model of the system, a new two-person zero-sum adaptive dynamic programming (ADP) algorithm is developed to obtain the optimal control, where the reconstruction error and the system disturbance are considered the control input of the system. Single-layer neural networks are used to construct the critic and action networks, which are presented to approximate the performance index function and the control law, respectively. Finally, simulation results will show the effectiveness of the developed data-based ADP methods.

关键词： adaptive critic designs adaptive dynamic programming Approximate dynamic programming Neuro-dynamic programming Recurrent neural network Data-based control Optimal control

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Finite-Horizon Neural Optimal Tracking Control for a Class of Nonlinear Systems with Unknown dynamics

Finite-Horizon Neural Optimal Tracking Control for a Class o...

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10th World Congress on Intelligent Control and Automation (WCICA)

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

ISBN: (纸本)9781467313988

A neural-network-based finite-horizon optimal tracking control scheme for a class of unknown nonlinear discrete-time systems is developed. First, the tracking control problem is converted into designing a regulator for the tracking error dynamics under the framework of finite-horizon optimal control theory. Then, with convergence analysis in terms of cost function and control law, the iterative adaptive dynamic programming algorithm is introduced to obtain the finite-horizon optimal controller to make the cost function close to its optimal value within an g-error bound. Furthermore, in order to implement the algorithm via dual heuristic dynamic programming technique, three neural networks are employed to approximate the error dynamics, the cost function, and the control law, respectively. In addition, a numerical example is given to demonstrate the validity of the present approach.

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

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Critic-Identifier Structure-Based ADP for Decentralized Robust Optimal Control of Modular Robot Manipulators 8

Critic-Identifier Structure-Based ADP for Decentralized Robu...

引用

8th International Conference on Information Science and Technology (ICIST)

作者： Dong, Bo Wang, Shuxiang Zhou, Fan Li, Yan Wang, Shenquan Liu, Keping Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Jilin Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781538637821

This paper presents a decentralized robust optimal control method for modular robot manipulators (MRMs) via a novel critic-identifier (CI) structure-based adaptive dynamic programming (ADP) scheme. The robust control problem of MRMs is transformed into an optimal compensation control approach, which combines model-based compensation control, identifier-based learning control and ADP-based optimal control. The dynamic model of MRMs is formulated based on a torque sensing technique that is deployed for each joint module, where the local dynamic information is utilized effectively to design the model compensation controller. A neural network (NN) identifier is established to approximate the dynamics of the interconnected dynamic coupling (IDC). Based on the ADP algorithm, the Hamiltonian-Jacobi-Bellman (HJB) equation can be solved by constructing a critic NN, and the approximate optimal control policy is derived. The closed-loop robotic system is guaranteed to be asymptotic stable by the implementation of a set of decentralized control policies that have been developed. Finally, simulations verify the effectiveness of the proposed method.

关键词： modular robot manipulator decentralized control adaptive dynamic programming optimal control

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Critic-Identifier Structure ADP Based Near-optimal Decentralized Tracking Control of Modular and Reconfigurable Robots 34

Critic-Identifier Structure ADP Based Near-optimal Decentral...

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34th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Xia, Hongbing Xue, Daiwei Wang, Yanchun Qiao, Aimin Zhao, Bo Bengbu Univ Dept Elect & Elect Engn Bengbu Peoples R China Beijing Normal Univ Sch Syst Sci Beijing Peoples R China

ISBN: (纸本)9781728139364

This paper investigates a near-optimal decentralized tracking control (DTC) scheme for modular and reconfigurable robots (MRRs) via a novel critic-identifier (CI) structure-based adaptive dynamic programming (ADP) algorithm. The DTC problem of MRRs is transformed into an optimal control issue, which consists of local namely controller, local optimal feedback controller and robust compensator. By using desired states of coupled subsystems to substitute their corresponding actual states, the strict norm-boundedness assumption of interconnections can be avoided. By using self-learning ability of neural network (NN), an identifier is constructed to approximate the subsystem dynamics. Then, the local desired control law can be obtained based on identified dynamics. A critic NN is constructed to solve Hamiltonian-Jacobi-Bellman (HJB) equation, and the local tracking feedback control policy is derived. To remove the overall error caused by the substitution, identification and approximation of critic NN, a robust term is added to guarantee the reliable performance of MRR system. The stability of the closed-loop system is ensured to be asymptotically stable by using the Lyapunov's direct methods. Finally, simulation studies show the effectiveness of the proposed scheme.

关键词： adaptive dynamic programming Modular and reconfigurable robots Decentralized tracking control Critic-identifier structure

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Parallel adaptive Critic Designs of Optimal Control for Ice-Storage Air Conditioning Systems

Parallel Adaptive Critic Designs of Optimal Control for Ice-...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Liao, Zehua Wei, Qinglai Song, Ruizhuo Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728124858

As a kind of distributed energy storage system, ice-storage air conditioning (IAC) system is an effective way for transferring cooling load. A parallel adaptive critic design method is constructed for solving the optimal control problem of IAC systems. adaptive critic design is also called adaptive dynamic programming (ADP). The initial value function of the parallel ADP method is obtained by using particle swarm optimization (PSO) method to pre-train the weights of the neural networks. Another PSO algorithm is utilized in each iteration to achieve the target control, which avoids solving a nonlinear equation in traditional ADP algorithms. Finally, the efficiency of the parallel ADP algorithm will be illustrated through the experiment results.

关键词： Parallel adaptive critic design adaptive dynamic programming particle swarm optimization ice-storage air conditioning

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A Neural Architecture to Address Reinforcement Learning Problems

A Neural Architecture to Address Reinforcement Learning Prob...

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International Joint Conference on Neural Networks (IJCNN)

作者： de Arruda, Rodrigo L. S. Von Zuben, Fernando J. Univ Campinas UNICAMP Sch Elect & Comp Engn FEEC Dept Comp Engn & Ind Automat DCA Lab Bioinformat & Bioinspired Comp LBiC Campinas SP Brazil

ISBN: (纸本)9781424496365

In this paper, the Reinforcement Learning problem is formulated equivalently to a Markov Decision Process. We address the solution of such problem using a novel adaptive dynamic programming algorithm which is based on a Multi-layer Perceptron Neural Network composed of a parameterized function approximator called Wire-Fitting. Extending such established model, this work makes use of concepts of eligibility to conceive faster learning algorithms. The advantage of the proposed approach is founded on the capability to handle continuous environments and to learn a better policy while following another. Simulation results involving the automatic control of an inverted pendulum are presented to indicate the effectiveness of the proposed algorithm.

关键词： Approximation methods dynamic programming Equations Heuristic algorithms Markov decision process Markov processes Markov processes Mathematical model Monte Carlo methods adaptive dynamic programming automatic control dynamic programming inverted pendulum learning (artificial intelligence) learning algorithm multilayer perceptron neural network multilayer perceptrons neural architecture neural net architecture parameterized function approximator reinforcement learning wire-fitting

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Neural-Network Based Vector Control of VSC-HVDC Transmission Systems 4

Neural-Network Based Vector Control of VSC-HVDC Transmission...

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4th International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Li, Shuhui Fu, Xingang Alonso, Eduardo Fairbank, Michael Wunsch, Donald C. Univ Alabama Tuscaloosa AL 35487 USA City Univ London London England Univ Essex Colchester Essex England Missouri Univ Sc & Tech Rolla MO USA

ISBN: (纸本)9781479999828

The application of high-voltage dc (HVDC) using voltage-source converters (VSC) has surged recently in electric power transmission and distribution systems. An optimal vector control of a VSC-HVDC system which uses an artificial neural network to implement an approximate dynamic programming algorithm and is trained with Levenberg-Marquardt is introduced in this paper. The proposed neural network vector control algorithm is analyzed in comparison with standard vector control methods for various HVDC control requirements, including dc voltage, active and reactive power control, and ac system voltage support. Assessment of the resulting closed-loop control shows that the neural network vector control approach has superior performance and works efficiently within and beyond the constraints of the HVDC system, for instance, converter rated power and saturation of PWM modulation.

关键词： VSC-HVDC transmission and distribution renewable energies neural network adaptive dynamic programming Levenberg-Marquardt voltage-source converter

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Stackelberg Differential Game-based Hierarchical Approximate Optimal Interaction Control of Human-Centered Reconfigurable Robot Manipulator Systems 3

Stackelberg Differential Game-based Hierarchical Approximate...

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3rd Conference on Fully Actuated System Theory and Applications (FASTA)

作者： An, Tianjiao Yan, Haoyu Ma, Bing Jiang, Hucheng Dong, Bo Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China

ISBN: (纸本)9798350373707;9798350373691

This paper proposes a Stackelberg game-based approximate optimal interaction control approach for human-centered reconfigurable robot manipulator (RRM) systems. Joint torque feedback technique is utilized to form the RRM dynamic model. According to differential game, the RRM's optimal control issue is transformed into a Stackelberg game issue between the human and the RRM. By the adaptive dynamic programming (ADP) algorithm, the cost functions of human as well as RRM are developed by critic neural network (NN) and implement to solve Hamilton-Jacobian (HJ) equations, which facilitate the acquisition of Stackelberg equilibrium. The position tracking error is ultimately uniformly bounded (UUB) according to the Lyapunov theory. Experiment results demonstrate the effectiveness of the proposed method.

关键词： adaptive dynamic programming Reconfigurable robot manipulators Human-robot collaboration Stackelberg differential game Interaction Control

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Data-Driven Optimal Tracking Control for the Wastewater Treatment Plant via Generalized GDHP 34

Data-Driven Optimal Tracking Control for the Wastewater Trea...

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34th Chinese Control and Decision Conference (CCDC)

作者： Li, Xin Wang, Ding Zhao, Mingming Hu, Lingzhi Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China

ISBN: (纸本)9781665478960

In order to implement the tracking control design of dissolved oxygen and nitrate nitrogen concentrations in a nonaffine wastewater treatment system, a globalized dual heuristic dynamic programming (GDHP) algorithm is established. First, the algorithm is initialized with a positive definite function to ensure that the sequence of cost functions is monotonically nonincreasing. Then, the updating rules of the cost function, the costate function, and the tracking control law are provided in the framework of generalized value iteration. Moreover, the monotonicity and the admissibility analysis of the generalized iteration algorithm are given. The model network, the critic network, and the action network are introduced to facilitate the implementation of the algorithm. Finally, the evolving policies in the iterative process are adopted to adjust the concentrations of dissolved oxygen and nitrate nitrogen in the experimental simulation, which verifies that the proposed generalized GDHP-based optimal tracking control method is applicable online.

关键词： adaptive dynamic programming Generalized value iteration Neural networks Optimal tracking control Wastewater treatment

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Research on Optimal Electrical Energy Management Method of Smart Grid Based on Value Iteration 3rd

Research on Optimal Electrical Energy Management Method of S...

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3rd IFAC Workshop pn Cyber-Physical and Human Systems (CPHS)

作者： Yan, Chunsheng Zheng, Yongjian Dai, Xun Gao, Baiyu Zhong, Liguang Xing, Shi Song, Ruizhuo Liaoning Elect Power Dev Stock Co Ltd Shenyang 110179 Peoples R China State Grid Liaoning Elect Power Supply Co Ltd Shenyang 110006 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

Based on the smart grid residential load data and energy storage terminal data, this paper expands a new optimal control method of smart grid supply and demand matching and optimization. This paper describes the operation principle and defines the required optimization parameters of the control object-electric energy storage management system. Aiming at the characteristics of discrete non linearity of the smart grid, a value iteration algorithm is selected to establish a control strategy,and an adaptive value iteration optimization scheme is derived. We use the BP neural network to export the control strategy of the electric energy management system by reducing the back propagation error. Finally,we use the day-ahead electricity price data to achieve numerical simulation. The result shows that the adaptive value iteration algorithm methods have higher efficiency and superior adaptability compared with the traditional BP neural network control method. Copyright (C) 2020 The Authors.

关键词： adaptive dynamic programming optimal control value iteration smart grid BP neural network

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