Self-Learning Optimal Control for Uncertain Nonlinear Systems via Online Updated Cost Function

被引：0

作者：

Zhao, Bo ^{[1
]}

Shi, Guang ^{[2
]}

Li, Chao ^{[2
]}

机构：

[1] Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing, Peoples R China

[2] Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing, Peoples R China

来源：

PROCEEDINGS 2018 33RD YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION (YAC) | 2018年

基金：

北京市自然科学基金; 中国国家自然科学基金;

关键词：

Adaptive dynamic programming; Uncertain nonlinear systems; Optimal control; Disturbance observer; Neural networks; Reinforcement learning; DISTURBANCE OBSERVER; DESIGN;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an online updated cost function based self-learning optimal control scheme for uncertain nonlinear systems. By establishing an online updated cost function with the help of disturbance observer, the Hamilton-Jacobi-Bellman equation is solved by constructing a critic neural network, whose weight vector is tuned by self-learning algorithm. And then, the optimal control scheme is derived indirectly. Based on Lyapunov stability analysis, the closed-loop system with the proposed scheme is guaranteed to be stable. The simulation results show the effectiveness of the developed self-learning optimal control scheme. The cost function reflects the system uncertainties in real time, which implies that this method relaxes the assumptions on available upper-bounds and matching condition for system dynamics in compared with many existing methods.

引用

页码：1061 / 1065

页数：5

共 18 条

[1] [Anonymous], 1992, HDB INTELLIGENT CONT
[2] A novel actor-critic-identifier architecture for approximate optimal control of uncertain nonlinear systems
Bhasin, S.
Kamalapurkar, R.
Johnson, M.
Vamvoudakis, K. G.
Lewis, F. L.
Dixon, W. E.
[J]. AUTOMATICA, 2013, 49 (01) : 82 - 92
[3] Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems
Jiang, Yu
Jiang, Zhong-Ping
[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 25 (05) : 882 - 893
[4] A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme
Kayacan, Erkan
Peschel, Joshua M.
Chowdhary, Girish
[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2017, 62 : 276 - 285
[5] Reinforcement Learning and Feedback Control USING NATURAL DECISION METHODS TO DESIGN OPTIMAL ADAPTIVE CONTROLLERS
Lewis, Frank L.
Vrabie, Draguna
Vamvoudakis, Kyriakos G.
[J]. IEEE CONTROL SYSTEMS MAGAZINE, 2012, 32 (06): : 76 - 105
[6] Liu D, 2017, ADV IND CONTROL, P1, DOI 10.1007/978-3-319-50815-3
[7] Adaptive Optimal Control Using Frequency Selective Information of the System Uncertainty With Application to Unmanned Aircraft
Maity, Arnab
Hoecht, Leonhard
Heise, Christian
Holzapfel, Florian
[J]. IEEE TRANSACTIONS ON CYBERNETICS, 2018, 48 (01) : 165 - 177
[8] Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances
Song, Ruizhuo
Lewis, Frank L.
Wei, Qinglai
Zhang, Huaguang
[J]. IEEE TRANSACTIONS ON CYBERNETICS, 2016, 46 (05) : 1041 - 1050
[9] Disturbance observer-based robust missile autopilot design with full-state constraints via adaptive dynamic programming
Sun, Jingliang
Liu, Chunsheng
[J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2018, 355 (05): : 2344 - 2368
[10] Optimal Robust Linear Quadratic Regulator for Systems Subject to Uncertainties
Terra, Marco H.
Cerri, Joao P.
Ishihara, Joao Y.
[J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2014, 59 (09) : 2586 - 2591

← 1 2 →