Adaptive Neural-Network-Based Control for a Class of Nonlinear Systems With Unknown Output Disturbance and Time Delays

Abstract

This paper pays close attention to the adaptive neural network tracking control. Aiming at a class of uncertain nonlinear systems with completely unknown output disturbance and unknown time delay, a corresponding robust control method is proposed based on the backstepping design technology. Neural network approximation is introduced as a very effective estimation technique for modeling uncertain partitions in the design process of virtual controller. The suitable Lyapunov–Krasovskii function is constructed, and by using the organic combination of Young’s inequality, unknown time delays are compensated. Nussbaum function is used to handle unknown virtual control directions. A practical robust control method is proposed to deal with the controller singularity problems. A priori knowledge is not required for this method. In this method, all signals achieve semi-global uniform ultimate boundedness, and it is demonstrated that the tracking error eventually converges the region around the origin. The simulation results verify this method’s feasibility and effectiveness.