Prescribed-time adaptive state observer approach for distributed output-feedback formation tracking of networked uncertain underactuated surface vehicles

초록

This paper presents a prescribed-time adaptive state observer (PTASO) approach for distributed output-feedback formation tracking of multiple underactuated surface vehicles (USVs) with unknown system nonlinearities. The PTASO is designed to estimate the unmeasurable velocity information of the USVs by constructing a time-varying gain function that operates continuously beyond the prescribed time (PT). An adaptive mechanism for tuning the weights of neural networks is derived using the gain function within the Lyapunov stability framework. Based on the PTASO, we propose a distributed PT output-feedback tracking control law, taking into account the underactuation property in the command-filtered backstepping framework. Neural-network-based compensating signals are designed using the gain function in a distributed manner. Unlike existing USV formation tracking strategies, the proposed PT output-feedback tracking algorithm ensures that the formation tracking errors are confined within an adjustable neighborhood of the origin. This adjustment is controlled by a design parameter of the gain function and occurs within a pre-specified settling time, independent of the initial states of the USVs and the design parameters of the closed-loop system. The effectiveness of the proposed PT output-feedback formation tracking algorithm is demonstrated through simulations.

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