A Cooperative Optimization Algorithm Based on Adaptive Dynamic Programming

Liu Yi; Zhang Yun

doi:10.12052/gdutxb.170082

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

A Cooperative Optimization Algorithm Based on Adaptive Dynamic Programming

更新时间：2026-05-27

- A Cooperative Optimization Algorithm Based on Adaptive Dynamic Programming
- Vol. 34, Issue 6, Pages: 15-19(2017)
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.170082
  CLC： TP273
- Online First：09 November 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
刘毅, 章云. 一种基于自适应动态规划的协同优化算法[J]. 广东工业大学学报, 2017, 34(6): 15-19.

Liu Yi, Zhang Yun. A Cooperative Optimization Algorithm Based on Adaptive Dynamic Programming[J]. 2017, 34(6): 15-19.
刘毅, 章云. 一种基于自适应动态规划的协同优化算法[J]. 广东工业大学学报, 2017, 34(6): 15-19. DOI： 10.12052/gdutxb.170082.

Liu Yi, Zhang Yun. A Cooperative Optimization Algorithm Based on Adaptive Dynamic Programming[J]. 2017, 34(6): 15-19. DOI： 10.12052/gdutxb.170082.

摘要

采用值迭代的自适应动态规划的收敛条件是迭代性能指标函数初始化为任意半正定函数. 根据此收敛条件

本文研究了迭代性能指标函数的初始化和更新方法

提出了一种基于自适应动态规划的协同优化算法. 仿真结果表明

该协同优化算法令迭代的残差快速减小

大幅提高了自适应动态规划的收敛速度.

Abstract

If the initial iterative performance index function is a positive semi-definite function

then the value iteration of adaptive dynamic programming will converge to the optimal. This is the convergence condition of value-iteration based adaptive dynamic programming. Based on the condition

the initializing and updating methods for iterative performance index function is studied and a cooperative optimization algorithm based on adaptive dynamic programming is proposed. The simulation results show that the proposed algorithm can rapidly reduce the iteration residuals and greatly improve the convergence rate of adaptive dynamic programming.

关键词

Keywords

references

BELLMAN R E. Dynamic Programming[M]. Princeton: Princeton University Press, 1957.

WERBOS P J. Advanced forecasting methods for global crisis warning and models of intelligence [J]. General Systems Yearbook, 1977, 22(6): 25-38.

WERBOS P J. Approximate Dynamic Programming for Real-time Control and Neural Modeling, in Handbook of Intelligent Control[M]. New York: Van Nostrand Reinhold, 1992.

MILLER W T, SUTTON R S, WERBOS P J. A Menu of Designs for Reinforcement Learning Over Time, in Neural Networks for Control[M]. Cambridge: MIT Press, 1991.

MURRAY J J, COX C J, LENDARIS G G, SAEKS R. Adaptive dynamic programming [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 2002, 32(2): 140-153.

PROKHOROV D V, WUNSCH D C. Adaptive critic designs [J]. IEEE Transactions on Neural Networks, 1997, 8(5): 997-1007.

LEWIS F L, LIU D. Reinforcement learning and adaptive dynamic programming for feedback control [J]. IEEE Circuits Systems Magazine, 2009, 9(3): 32-50.

LEWIS F L, VRABIE D, VAMVOUDAKIS K G. Reinforcement learning and feedback control: using natural decision methods to design optimal adaptive controllers [J]. IEEE Control Systems, 2012, 32(6): 76-105.

AL-TAMIMI A, LEWIS F L, ABU-KHALAF M. Discrete-time nonlinear HJB solution using approximate dynamic programming: convergence proof [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetices, 2008, 38(4): 943-949.

ZHANG H, WEI Q, LUO Y. A novel infinite-time optimal tracking control scheme for a class of discrete-time nonlinear systems via the greedy HDP iteration algorithm [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetices, 2008, 38(4): 937-942.

WANG D, LIU D, WEI Q, ZHAO D, JIN N. Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming [J]. Automatica, 2012, 48(8): 1825-1832.

WEI Q, LIU D. A novel iterative θ-adaptive dynamic programming for discrete-time nonlinear systems [J]. IEEE Transactions on Automation Science and Engineering, 2014, 11(4): 1176-1190.

WEI Q, LIU D, LIN H. Value iteration adaptive dynamic programming for optimal control of discrete-time nonlinear systems [J]. IEEE Transactions on Cybernetics, 2016, 46(3): 840-853.

LIU Y,ZHANG Y. A convergence condition of value-iteration based adaptive dynamic programming [J]. Journal of Guangdong University of Technology, 2017, 34(5): 10-14.

NAVARRO-LOPEZ E M. Local feedback passivation of nonlinear discrete-time systems through the speed-gradient algorithm [J]. Automatica, 2007, 43(7): 1302-1306.

SIRA-RAMIREZ H. Non-linear discrete variable structure systems in quasi-sliding mode [J]. International Journal of Control, 1991, 54(5): 1171-1187.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Convergence Condition of Value-iteration Based Adaptive Dynamic Programming

A Data-Driven Prescribed Convergence Rate Design for Robust Tracking of Discrete-Time Systems

A Survey of Energy Management System Based on Adaptive Dynamic Programming

Efficient Temporal Modeling for RGB-T Tracking

Bimodal Iterative Cross-attention Fusion Ensemble Framework

Related Author

Liu Yi

Zhang Yun

Chen Ci

Xie Li-hua

Yuan Jun

Zhang Gui-dong

Li Zhong

Chen Zhe

Related Institution

Guangdong Key Laboratory of IoT Information Technology

School of Electrical and Electronic Engineering, Nanyang Technological University

School of Mathematics and Computer Science, FernUniversität in Hagen, Hagen

Department of Energy Technology, Aalborg University, Aalborg

School of Engineering, Deakin University

AI问答

⁰