State-Predictor-Based Adaptive Neural Dynamic Surface Control of Uncertain Strict-Feedback Systems with Unknown Control Direction

2019 ◽  
Author(s):  
Tengfei Zhang ◽  
Yingmin Jia
Keyword(s):  
Dynamic Surface Control ◽  
Feedback Systems ◽  
Neural Dynamic ◽  
Dynamic Surface ◽  
Surface Control ◽  
Unknown Control ◽  
Unknown Control Direction ◽  
Control Direction ◽  
Strict Feedback

scholarly journals Observer-Based Adaptive Fuzzy Predefined Performance Control of a Class of Nonlinear Pure-Feedback Systems with Input Delay

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xin Li ◽  
Qiang Zhang ◽  
Dakuo He
Keyword(s):  
Dynamic Surface Control ◽  
Input Delay ◽  
Feedback Systems ◽  
Estimation Errors ◽  
Dynamic Surface ◽  
Performance Control ◽  
Adaptive Fuzzy ◽  
Unknown Control ◽  
Unknown Control Direction ◽  
Control Direction

This paper presents a problem of observer-based adaptive fuzzy predefined performance control of a class of nonlinear pure-feedback systems with input delay and unknown control direction. Compared with the existing research, a novel predefined performance controller is proposed, which relaxes the assumption that the initial error is known. In addition, it is difficult to design the controllers due to input delay and nonaffine properties of the pure-feedback systems, which can be simplified by Pade approximation. Moreover, dynamic surface control and Nussbaum functions are applied to overcome the calculation explosion caused by repeated differentiations and unknown control direction, respectively. Based on the above methods, an adaptive fuzzy predefined performance controller is proposed, and it is proved that all the signals of a closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). The tracking errors converge within a predefined range, while the observer estimation errors converge within a small zero region. Finally, the simulation results demonstrate the effectiveness of the proposed control system.

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