MT‐filters‐based event‐triggered adaptive prescribed performance tracking control of multi‐agent systems with unknown direction actuator failure

Abstract

AbstractIn this article, to ensure better performance and simultaneously save resources, an event‐triggered adaptive prescribed performance cooperative dynamic surface control (DSC) strategy is proposed for a class of nonlinear multi‐agent systems with unmodeled dynamics and unknown direction actuator failure under directed graphs. Firstly, with the help of MT‐filters and linearly parametrized neural networks, the unmeasurable states and nonlinearities are dealt with, respectively. Then, to reduce the impact of unmodeled dynamics on the system, a quantifiable dynamic signal created by an auxiliary first‐order system is utilized. Furthermore, a Nussbaum function and a smooth function are introduced to compensate for negative influence of unknown direction actuator failure on the system. Following that, by using the DSC approach to design the controller, all of the closed‐loop system's signals are proven to be cooperative semi‐globally uniformly ultimately bounded, all followers achieve the consensus results, and each follower tracking error reaches a specific bound within a finite time. Finally, the applicability of the proposed adaptive control strategy is demonstrated through numerical simulation.