Adaptive Learning Gain based Resilient Control of Multiagent Systems with Partial Uncertain Control Coefficients Under FDI Attacks

Abstract

Abstract This paper presents a novel approach to security resilient control in collective multiagent systems, incorporating an adaptive learning gain mechanism. The proposed method addresses challenges posed by bounded external disturbances, partial false-data-injection (FDI) attacks on the actuators, and partial unknown time-varying control coefficients. The presented synthesized control consists of following parts. Specifically, a robust adaptive control law with implicit learning is proposed to address the modeling parameterized uncertainties and disturbances. An adaptive learning gain-based control technique is designed to reduce the adversarial effect caused by mismatched disturbances with time-varying parameters, which curbs the cooperative errors to the assigned field. A resilient control approach is established to mitigate the detrimental impact of malicious attacks on actuators. An adaptive control strategy, utilizing a novel congelation of variables, is implemented to address the control issue pertaining to partially unknown control coefficients. This technique ensures that control inputs align with the practical actuators by establishing feasible control bounds. The property of the resulting closed-loop error multiagent systems is analyzed. Finally, the efficacy of the designed controller is illustrated by providing an illustrative example.