Finite-time Neural Tracking Control of Non-strict Feedback systems with Output Constraints

This article presents a finite-time neural adaptive tracking control of an unknown non-strict feedback system with output constraints. Firstly, novel virtual input signals and actual input signals are designed by using Barrier Lyapunov function, RBF (radial basis function) neural network adaptation, backstepping, and finite-time control theory to solve the problem of finite-time output constraint control of non-strict feedback systems. Secondly, a finite time stability theorem with output constraints is proposed to ensure that the controller designed above enables the output of the system to track the reference signal in finite time, and that the tracking error is constrained in the small neighborhood of the origin and all signals are bounded in the closed-loop system. Finally, the physical simulation shows the effectiveness of the designed controller. Therefore, the controller designed for non-strict feedback systems with output constraints has good stability and tracking performance, which provides theoretical support for practical system applications.