scholarly journals Adaptive Fault Compensation and Disturbance Suppression Design for Nonlinear Systems with an Aircraft Control Application

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
X. Yao ◽  
Y. Yang
Keyword(s):  
Nonlinear Systems ◽  
Control Strategy ◽  
Feedback Linearization ◽  
Adaptive Controller ◽  
Nonlinear Feedback ◽  
Asymptotic Tracking ◽  
And Performance ◽  
Mismatched Disturbances ◽  
Adaptive Compensation Control ◽  
Multivariable Nonlinear Systems

A comprehensive adaptive compensation control strategy based on feedback linearization design is proposed for multivariable nonlinear systems with uncertain actuator fault and unknown mismatched disturbances. Firstly, the linear dynamic system is obtained through nonlinear feedback linearization, and the dynamic model of the mismatched disturbances as well as its relevance to the nonlinear system is given. The effect of disturbances on the system output is suppressed with the basic controller of the linearized system. Then, a direct adaptive controller is developed for the multiple uncertain actuator faults. Finally, an integrated algorithm based on adaptive weighted fusion could provide an effective compensation for the effect of multiple uncertain faults and mismatched disturbances. Thus, the stability and asymptotic tracking performance of the closed-loop system are ensured. The feasibility and performance of the proposed control strategy are validated by the numerical simulation results.

scholarly journals RBF NN-Based Backstepping Adaptive Control for a Class of Nonlinear Systems

2018 ◽  
Vol 214 ◽  
pp. 03005
Author(s):  
Haibo Zhao ◽  
Chengguang Wang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Control Strategy ◽  
Rbf Neural Network ◽  
Lyapunov Stability Theory ◽  
Adaptive Controller ◽  
Backstepping Control ◽  
Simulation Input ◽  
Sine Signal ◽  
Adaptive Ability

There are many control methods for nonlinear systems, but some of them can not control nonlinear mismatched systems very well. Backstepping control has obvious advantages in controlling nonlinear mismatched systems. Thus we proposed a new radial-basis-function (RBF) neural network-based backstepping adaptive controller combining RBF neural network (RBF NN) and backstepping control for a class of nonlinear mismatched systems. We adopted RBF NN to approximate the system uncertainty. And we analyzed the controller stability using Lyapunov stability theory. Finally we chose sine signal as simulation input signal, simulation results show that the proposed control strategy has better adaptive ability and robustness than PID control, validating the effectivess of the proposed control strategy.

Sign in / Sign up

Export Citation Format

Share Document