Finite-time adaptive tracking control for a class of nonstrict feedback nonlinear systems with full state constraints

This article reports our investigation on the finite-time tracking control problem for nonstrict feedback nonlinear systems subject to full state constraints. A finite-time stability criterion is founded by employing the barrier Lyapunov function methodology. With the backstepping technique, an adaptive neural networks controller is proposed, which can pledge that the closed-loop system signals bounded and the tracking error converges to a sufficiently modest area around the origin in finite-time under full state constraints. Finally, an example of the electromechanical system is given to corroborate the validity of acquired methods.

Automated summary

This study proposes an adaptive neural networks controller based on the barrier Lyapunov function methodology and backstepping technique for finite-time tracking control of nonstrict feedback nonlinear systems. The controller ensures bounded closed-loop system signals and convergence of tracking error to a sufficiently modest area around the origin under full state constraints. An example involving an electromechanical system is provided to validate the effectiveness of the proposed methods.