Decentralized robust control for a class of nonlinear systems

1997 ◽  
Author(s):  
Yu Tang ◽  
M. Tomizuka
Keyword(s):  
Robust Control ◽  
Nonlinear Systems ◽  
Decentralized Robust Control

Decentralized robust control for interconnected nonlinear systems

1997 ◽  
Vol 11 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Myung-Chul Han ◽  
Keum Shik Hong ◽  
Joòngsun Yoon ◽  
Suk Lee
Keyword(s):  
Robust Control ◽  
Nonlinear Systems ◽  
Decentralized Robust Control ◽  
Interconnected Nonlinear Systems

scholarly journals Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration

2021 ◽  
Vol 11 (5) ◽  
pp. 2312
Author(s):  
Dengguo Xu ◽  
Qinglin Wang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Policy Iteration ◽  
Control Law ◽  
Globally Asymptotically Stable ◽  
Control Approach ◽  
Input Uncertainties ◽  
Theoretical Results

In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided.

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