Adaptive neural output feedback finite‐time command filtered backstepping control for nonlinear systems with full‐state constraints

Abstract

Abstract In this paper, an adaptive neural finite‐time control method via barrier Lyapunov function, command filtered backstepping, and output feedback is proposed to solve the tracking problem of uncertain high‐order nonlinear systems with full‐state constraints and input saturation. By utilizing the neural network (NN) to approximate unknown nonlinear functions, the finite‐time command filters are used to filtering the virtual control signals and get the intermediate control signals in a finite time in the backstepping process. Because there are errors between the output of finite‐time command filters and the virtual control signals, the error compensation signals are added to eliminate the influence of filtering errors. Based on the proposed control scheme, the states of the system can be constrained in the predetermined region, all signals in the system are bounded in finite time, and the tracking error can converge to the desired region in finite time. At last, a simulation example is given to show the effectiveness of the proposed control method.