Filter-Based Average Dwell-Time Tuning Approach for Adaptive Prescribed-Time Tracking of Uncertain Switched Nonlinear Systems

Abstract

This paper addresses neural-network-based adaptive prescribed-time (PT) tracking for uncertain switched systems with unmatched nonlinearities. A continuously switched adaptive tuning mechanism for neural network learning is developed by applying the average dwell time (ADT). First, a neural-network-based PT tracking control design strategy using the ADT-based adaptive tuning mechanism is established for switched nonlinear systems in strict-feedback form. A novel adaptive dynamic surface controller is designed recursively using a practical finite-time scaling function and continuously switched tuning parameters. The switched adaptive tuning laws for neural networks are structured to reduce the conservatism associated with common adaptive laws. Then, a filter-based tuning approach is employed to ensure the continuity of switched adaptive parameters with ADT in the designed controller. The practical PT stability of the closed-loop system is demonstrated based on the boundedness of the adaptive parameters. Building upon this foundation, the proposed PT design approach is extended to control switched pure-feedback nonlinear systems, even in cases where control directions are unspecified. The unknown sign problem encountered with switched virtual and actual control coefficient functions is resolved in the PT control framework. It is shown that the PT performance bound of the tracking error can be reduced by selecting the design parameter of the scaling function. Finally, simulation results illustrate the merits of the proposed theoretical approach.