Adaptive neural tracking control for uncertain nonlinear systems with input and output constraints using disturbance observer

This paper investigates the tracking control problem for a class of strict-feedback nonlinear uncertain systems in the presence of unknown disturbance, input and output constraints. By using backstepping approach, an adaptive tracking controller is developed on the basis of neural network and disturbance observer. The Nussbaum function is introduced to tackle the problem of the nonlinear term arising from the input saturation, and the barrier Lyapunov function is employed to prevent the outputs from violating the constraints. The disturbance observer is developed to estimate unknown external disturbances. The proposed control scheme can guarantee that all signals of the closed-loop system are bounded by using the Lyapunov analysis method. Finally, the simulation results for three degrees of freedom (3-DOF) model helicopter are given to illustrate the effectiveness of the developed control scheme.