Robust Adaptive Controller for a Class of Uncertain Nonlinear Systems with Disturbances
Authors: Ngo Tri Nam Cuong, Le Van Chuong, and Mai The Anh
Nonlinear Dynamics and Applications
: : 695–706
Publishing year: 6/2022
This paper presents a method to synthesize the controller for uncertain nonlinear systems based on a combination of sliding mode control, adaptive control, and radial basis function (RBF) neural network. We propose an adaptive control law based on the RBF neural network to identify and compensate for variable parameter components, nonlinear function vectors, and external disturbance. The main linear component is built based on a sliding control. The designed controller has the advantage of being resistant to the elements of uncertainty and has a high control quality.
Nonlinear systems, Adaptive control, System identification
