Synthesis of a robust neural input-state feedback controller for nonlinear systems

Author(s):  
Sondess Ben Aoun ◽  
Nabil Derbel ◽  
Houssem Jerbi
Author(s):  
Qinghui Du

The problem of adaptive state-feedback stabilization of stochastic nonholonomic systems with an unknown time-varying delay and perturbations is studied in this paper. Without imposing any assumptions on the time-varying delay, an adaptive state-feedback controller is skillfully designed by using the input-state scaling technique and an adaptive backstepping control approach. Then, by adopting the switching strategy to eliminate the phenomenon of uncontrollability, the proposed adaptive state-feedback controller can guarantee that the closed-loop system has an almost surely unique solution for any initial state, and the equilibrium of interest is globally asymptotically stable in probability. Finally, the simulation example shows the effectiveness of the proposed scheme.


2018 ◽  
Vol 41 (3) ◽  
pp. 615-620
Author(s):  
Tiancheng Wang ◽  
Shi Zheng ◽  
Wuquan Li

This paper aims to solve the state feedback stabilization problem for a class of high-order nonlinear systems with more general high-order terms. Based on the backstepping design method and Lyapunov stability theorem, a state feedback controller is constructed to ensure that the origin of the closed-loop system is globally asymptotically stable. The efficiency of the state feedback controller is demonstrated by a simulation example.


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