Finite-time fuzzy control strategy for nonlinear MASs with actuator faults and deception attacks

Abstract

In this paper, we addressed the issue of maintaining a desirable level of performance in the presence of actuator faults and deception attacks in nonlinear multi-agent systems (MASs). These problems are critical for the stability and coordination of MASs that are increasingly used in robotics, autonomous vehicles, and industrial automation. Our aim was to design control strategies that, in the presence of these challenges, guarantee practical finite-time stability and robust tracking performance. To accomplish this, a distributed adaptive fuzzy control scheme based on backstepping was developed. Fuzzy logic systems were utilized to capture the complex system's unknown nonlinearities, while adaptive laws were designed to estimate and mitigate actuator gain and bias faults. A Nussbaum-type function was introduced to address unknown control directions resulting from deception attacks. Stability was verified by the Lyapunov theory. The suggested approach ensured that it was a finite-time stable method, and every signal in the closed loop was found to be semi-globally uniformly eventually bounded. Our control strategy, compared to published approaches, improved convergence time by approximately 38% and tracking accuracy of approximately 35% under the same conditions of simultaneous actuator faults and deception attacks.