Fixed-time adaptive neural control for nonstrict-feedback uncertain nonlinear systems with output constraints

This paper addresses the fixed-time output-constrained control problem for a class of uncertain nonlinear systems in nonstrict-feedback form. A novel fixed-time adaptive neural control scheme is proposed by integrating the barrier Lyapunov function (BLF), neural networks, and fixed-time control technique into the backstepping control design. Rigorous theoretical analysis for the semi-global fixed-time stability of the whole closed-loop system is provided. The proposed controller can guarantee all the closed-loop error signals converge to the small regions about zero in fixed time while ensuring the system output can always stay within the predefined output constraints. In addition, the proposed controller is structurally simple, which makes it affordable for practical applications. Finally, two simulation examples are performed to illustrate the effectiveness and benefits of the proposed control scheme.