Data-Based Adaptive Output Feedback Tracking Control for a Class of Nonlinear Systems

Abstract

In this paper, we propose an output feedback tracking control scheme for a class of continuous-time nonlinear systems without specific model. A radial basis function neural network (RBFNN) observer is designed to online estimate the unmeasured inner state variables only using the input and output data. Based on the designed RBFNN observer, a sliding mode controller is derived to guarantee that the system states follow the desired trajectories. Simulation results on an example show the effectiveness and tracking performance of the proposed scheme.