Fixed-time adaptive neural tracking control of output constrained nonlinear pure-feedback system with input saturation

摘要

This paper considers the fixed-time tracking control problem for system-constrained nonlinear pure -feedback systems involving input saturation and output constraints. A sequence of auxiliary virtual and actual input signals is designed to obtain an expression for the system tracking error and stabilize the system. A combination of the fixed-time stability theory, barrier Lyapunov function, and radial basis function neural network is employed to develop the proposed method for obtaining the expected perfor-mance from the considered system. Further, a theorem is proposed to ensure that the designed controller allows the system output to track the reference signal within a fixed time, ensuring that the tracking error is limited to a small neighborhood of origin within a fixed time and that all the signals in the system are bounded. The aforementioned problem can be solved using the proposed control method, and the simu-lation experiments indicate the effectiveness of the designed controller.(c) 2021 Elsevier B.V. All rights reserved.