Distributed H∞ Optimal Tracking Control for Strict-Feedback Nonlinear Large-Scale Systems With Disturbances and Saturating Actuators

被引:55
作者
Luy Nguyen Tan [1 ]
机构
[1] Ind Univ Ho Chi Minh City, Fac Elect Technol, Ho Chi Minh City 700000, Vietnam
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2020年 / 50卷 / 11期
关键词
Large-scale systems; Decentralized control; Optimal control; Multi-agent systems; Feedforward systems; Actuators; Adaptive dynamic programming (ADP); distributed control; large-scale systems; saturating actuators; FINITE-TIME CONSENSUS; INTERCONNECTED SYSTEMS; MULTIAGENT SYSTEMS; CONTROL DESIGN; GAMES;
D O I
10.1109/TSMC.2018.2861470
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, a novel distributed H-infinity optimal tracking control scheme is designed for a class of physically interconnected large-scale nonlinear systems in the presence of strict-feedback form, external disturbance and saturating actuators. First, by designing feedforward control, the distributed H-infinity optimal tracking control problem of a physically interconnected large-scale system is transformed into equivalent control of a decoupled multiagent system. Subsequently, a feedback control algorithm is designed to learn the optimal control input and the worst-case disturbance policy. The algorithm guarantees that the function approximation error and the distributed tracking error are uniformly ultimately bounded while the cost function converges to the bounded H-infinity-gain optimal value. Finally, the effectiveness of the proposed scheme is demonstrated by simulation results of distributed control for the mobile multirobot system.
引用
收藏
页码:4719 / 4731
页数:13
相关论文
共 49 条
[1]   Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach [J].
Abu-Khalaf, M ;
Lewis, FL .
AUTOMATICA, 2005, 41 (05) :779-791
[2]   Neurodynamic programming and zero-sum games for constrained control systems [J].
Abu-Khalaf, Murad ;
Lewis, Frank L. ;
Huang, Jie .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2008, 19 (07) :1243-1252
[3]  
Basar T., 1995, Optimal Control and Related Minimax Design Problems: A Dynamic Game Approach
[4]   Decentralized Adaptive Optimal Control of Large-Scale Systems With Application to Power Systems [J].
Bian, Tao ;
Jiang, Yu ;
Jiang, Zhong-Ping .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2015, 62 (04) :2439-2447
[5]   Adaptive finite-time consensus tracking for multiple uncertain mechanical systems with input saturation [J].
Cai, Mingjie ;
Xiang, Zhengrong .
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2017, 27 (09) :1653-1676
[6]   Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints [J].
Chen, Ziting ;
Li, Zhijun ;
Chen, C. L. Philip .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 28 (06) :1318-1330
[8]   Distributed control design for spatially interconnected systems [J].
D'Andrea, R ;
Dullerud, GE .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2003, 48 (09) :1478-1495
[9]   A vision-based formation control framework [J].
Das, AK ;
Fierro, R ;
Kumar, V ;
Ostrowski, JP ;
Spletzer, J ;
Taylor, CJ .
IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, 2002, 18 (05) :813-825
[10]   Optimal and event-based networked control of physically interconnected systems and multi-agent systems [J].
Demir, Ozan ;
Lunze, Jan .
INTERNATIONAL JOURNAL OF CONTROL, 2014, 87 (01) :169-185