Self-triggered adjustable prescribed performance control for stochastic multiagent systems with communication faults

The complex working environment and accurate transient performance bring the challenges for the tracking control of multiagent systems (MASs). To guarantee the control performance of stochastic MASs under the limited communication resources and random communication noises, an adaptive adjustable prescribed performance control strategy via compensatory self-triggered mechanism is designed. First of all, a data-driven fault detection mechanism is proposed to detect the occurrence of the communication faults caused by the random communication noises. The boundary of detecting outlier is directly determined by the less collected operation data without updating continuously. Then, the shifting function which has less intermediate functions is designed, which can be used to achieve the adjustable prescribed performance control method that the tracking errors are restricted in the adjustable bounds. Next, a compensatory self-triggered mechanism is proposed to address the problem of the excessive trigger intervals caused by the large control signal. Finally, all signals of the closed-loop system are verified semiglobally uniformly ultimately bounded in probability by the Lyapunov stability method. The effectiveness of the control method is verified by simulation results.