Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming

被引：333
|
作者：
Wang, Ding [1 ]
Liu, Derong [1 ]
Wei, Qinglai [1 ]
Zhao, Dongbin [1 ]
Jin, Ning [2 ]
机构：
[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
[2] Univ Illinois, Dept Elect & Comp Engn, Chicago, IL 60607 USA
来源：
AUTOMATICA | 2012年 / 48卷 / 08期
基金：
中国国家自然科学基金; 北京市自然科学基金;
关键词：
Adaptive critic designs; Adaptive dynamic programming; Approximate dynamic programming; Globalized dual heuristic programming; Intelligent control; Neural network; Optimal control; FEEDBACK-CONTROL; REINFORCEMENT; ADP;
D O I：
10.1016/j.automatica.2012.05.049
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An intelligent-optimal control scheme for unknown nonaffine nonlinear discrete-time systems with discount factor in the cost function is developed in this paper. The iterative adaptive dynamic programming algorithm is introduced to solve the optimal control problem with convergence analysis. Then, the implementation of the iterative algorithm via globalized dual heuristic programming technique is presented by using three neural networks, which will approximate at each iteration the cost function, the control law, and the unknown nonlinear system, respectively. In addition, two simulation examples are provided to verify the effectiveness of the developed optimal control approach. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1825 / 1832
页数：8
相关论文
共 50 条
  • [41] Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Discrete-Time Systems
    Dong, Lu
    Zhong, Xiangnan
    Sun, Changyin
    He, Haibo
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 28 (07) : 1594 - 1605
  • [42] Design of Fuzzy Adaptive Iterative Learning Control for Nonaffine Nonlinear Discrete-Time Systems
    Chien, Chiang-Ju
    Wang, Ying-Chung
    Shen, Dong
    Chi, Ronghu
    2015 34TH CHINESE CONTROL CONFERENCE (CCC), 2015, : 3218 - 3223
  • [43] Stable Adaptive Neural Network Control of MIMO Nonaffine Nonlinear Discrete-Time Systems
    Zhai, Lianfei
    Chai, Tianyou
    Yang, Chenguang
    Ge, Shuzhi Sam
    Lee, Tong Heng
    47TH IEEE CONFERENCE ON DECISION AND CONTROL, 2008 (CDC 2008), 2008, : 3646 - 3651
  • [44] Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input
    Liu, Yan-Jun
    Li, Shu
    Tong, Shaocheng
    Chen, C. L. Philip
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 30 (01) : 295 - 305
  • [45] Adaptive dynamic programming for optimal control of discrete-time nonlinear system with state constraints based on control barrier function
    Xu, Jiahui
    Wang, Jingcheng
    Rao, Jun
    Zhong, Yanjiu
    Wang, Hongyuan
    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2022, 32 (06) : 3408 - 3424
  • [46] A Novel Iterative θ-Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems
    Wei, Qinglai
    Liu, Derong
    IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2014, 11 (04) : 1176 - 1190
  • [47] Broad Learning System Approximation-Based Adaptive Optimal Control for Unknown Discrete-Time Nonlinear Systems
    Yuan, Liang'en
    Li, Tieshan
    Tong, Shaocheng
    Xiao, Yang
    Shan, Qihe
    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2022, 52 (08): : 5028 - 5038
  • [48] ε-Adaptive Dynamic Programming for Discrete-Time Systems
    Liu, Derong
    Jin, Ning
    2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8, 2008, : 1417 - 1424
  • [49] Spiking Adaptive Dynamic Programming Based on Poisson Process for Discrete-Time Nonlinear Systems
    Wei, Qinglai
    Han, Liyuan
    Zhang, Tielin
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (05) : 1846 - 1856
  • [50] A novel adaptive dynamic programming based on tracking error for nonlinear discrete-time systems
    Li, Chun
    Ding, Jinliang
    Lewis, Frank L.
    Chai, Tianyou
    AUTOMATICA, 2021, 129
12345