scholarly journals Adaptive Fuzzy Robust Control for a Class of Nonlinear Systems via Small Gain Theorem

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Xingjian Wang ◽  
Shaoping Wang
Keyword(s):  
Adaptive Control ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Closed Loop ◽  
Design Procedure ◽  
Control Law ◽  
Fuzzy Logic Systems ◽  
Adaptive Fuzzy ◽  
Small Gain Theorem ◽  
Small Gain

Practical nonlinear systems can usually be represented by partly linearizable models with unknown nonlinearities and external disturbances. Based on this consideration, we propose a novel adaptive fuzzy robust control (AFRC) algorithm for such systems. The AFRC effectively combines techniques of adaptive control and fuzzy control, and it improves the performance by retaining the advantages of both methods. The linearizable part will be linearly parameterized with unknown but constant parameters, and the discontinuous-projection-based adaptive control law is used to compensate these parts. The Takagi-Sugeno fuzzy logic systems are used to approximate unknown nonlinearities. Robust control law ensures the robustness of closed-loop control system. A systematic design procedure of the AFRC algorithm by combining the backstepping technique and small-gain approach is presented. Then the closed-loop stability is studied by using small gain theorem, and the result indicates that the closed-loop system is semiglobally uniformly ultimately bounded.

Adaptive fuzzy backstepping robust control for uncertain nonlinear systems based on small-gain approach

2010 ◽  
Vol 161 (6) ◽  
pp. 771-796 ◽  
Author(s):  
Shaocheng Tong ◽  
Xianglei He ◽  
Yongming Li ◽  
Huaguang Zhang
Keyword(s):  
Robust Control ◽  
Nonlinear Systems ◽  
Uncertain Nonlinear Systems ◽  
Adaptive Fuzzy ◽  
Small Gain

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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