Abstract
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.
Highlights
Nonlinear systems have been studied by many scholars, and the control methods used are as follows: iterative learing control [1], feedback linearization [2], fuzzy control [3], sliding mode control [4], etc
For a class of nonlinear mismatched systems, we proposed a new RBF neural network (RBF NN)-based backstepping adaptive control
Sine signal is chosen as input signal, system has larger tracking error and worse adaptive tracking ability when using PID control, but system has smaller tracking error and better adaptive tracking ability when using RBFNNB, validating the superiority of the proposed control strategy
Summary
Nonlinear systems have been studied by many scholars, and the control methods used are as follows: iterative learing control [1], feedback linearization [2], fuzzy control [3], sliding mode control [4], etc. When the system is a nonlinear mismatched system, the disadvantages of the above methods will be revealed. Backstepping control can be used to control nonlinear mismatched systems [5]. The control algorithm combining RBF NN and backstepping control has been successfully applied by scholars [7,8,9,10,11]. We use RBF NN-based backstepping control for a class of nonlinear mismatched systems. Simulation results verify the feasibility of the proposed control algorithm
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