Adaptive Robust Control for a Class of Non-Minimum Phase Nonlinear System

2006 ◽  
Author(s):  
Bo Xie ◽  
Bin Yao
Keyword(s):  
Robust Control ◽  
Nonlinear Systems ◽  
Tracking Error ◽  
Control Design ◽  
Adaptive Robust Control ◽  
Minimum Phase ◽  
Control Approach ◽  
Internal States ◽  
Robust Control Design ◽  
Error Dynamics

The paper presents the state feedback adaptive robust control approach to track the reference input for a class of nonminimum phase nonlinear systems. The key for this approach is to combine the adaptive robust control design techniques and the inputto-state property to deal with a class of non-minimum phase nonlinear systems with unknown parameter and unstructural uncertainties. The control design will guarantee that the tracking error dynamics is stabilized with bounded internal states and the closed-loop system is robust to the unstructural uncertainties.

scholarly journals Adaptive Robust Control for Networked Strict-Feedback Nonlinear Systems with State and Input Quantization

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2783
Author(s):  
Yanbin Liu ◽  
Jue Wang ◽  
Luis Gomes ◽  
Weichao Sun
Keyword(s):  
Robust Control ◽  
Nonlinear Systems ◽  
Networked Control Systems ◽  
Tracking Error ◽  
Adaptive Robust Control ◽  
Feedback Form ◽  
Control Approach ◽  
Input Quantization ◽  
The Stability ◽  
Strict Feedback

Backstepping method is a successful approach to deal with the systems in strict-feedback form. However, for networked control systems, the discontinuous virtual law caused by state quantization introduces huge challenges for its applicability. In this article, a quantized adaptive robust control approach in backsetpping framework is developed in this article for networked strict-feedback nonlinear systems with both state and input quantization. In order to prove the efficiency of the designed control scheme, a novel form of Lyapunov candidate function was constructed in the process of analyzing the stability, which is applicable for the systems with nondifferentiable virtual control law. In particular, the state and input quantizers can be in any form as long as they meet the sector-bound condition. The theoretic result shows that the tracking error is determined by the pregiven constants and quantization errors, which are also verified by the simulation results.

Wavelet Neural Network Based Adaptive Robust Control for a Class of MIMO Nonlinear Systems

2011 ◽  
Vol 383-390 ◽  
pp. 290-296
Author(s):  
Yong Hong Zhu ◽  
Wen Zhong Gao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Wavelet Neural Network ◽  
Adaptive Robust Control ◽  
Loop Control ◽  
Control Approach ◽  
Mimo Nonlinear Systems ◽  
Output Tracking Control ◽  
Dynamic Inverse

Wavelet neural network based adaptive robust output tracking control approach is proposed for a class of MIMO nonlinear systems with unknown nonlinearities in this paper. A wavelet network is constructed as an alternative to a neural network to approximate unknown nonlinearities of the classes of systems. The proposed WNN adaptive law is used to compensate the dynamic inverse errors of the classes of systems. The robust control law is designed to attenuate the effects of approximate errors and external disturbances. It is proved that the controller proposed can guarantee that all the signals in the closed-loop control system are uniformly ultimately bounded (UUB) in the sense of Lyapunov. In the end, a simulation example is presented to illustrate the effectiveness and the applicability of the suggested method.

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