Event‐Triggered Adaptive Neural Network Control for Constrained Stochastic Multi‐Agent Systems With Actuator Failures and Time‐Delay

ABSTRACT This paper studies the problem of event‐triggered adaptive neural network (NN) control for constrained stochastic multi‐agent systems (MASs) subject to actuator failures (AFs) and time delay. In the controller design process, the NN is exploited to approximate the unknown terms of the bias fault and unknown nonlinearities. Meanwhile, the AFs are successfully addressed by designing appropriate parameter adaptation laws and intermediate variables. Besides, to overcome the impact of state constraints (SCs) on the system, a suitable barrier Lyapunov function (BLF) is designed. Then, based on the backstepping technique and Lyapunov stability theory, a distributed event‐triggered adaptive controller is constructed to guarantee the consensus tracking with a certain error range, all closed‐loop system signals are bounded, and Zeno behavior (ZB) is excluded. Finally, the simulation results demonstrate the effectiveness of the presented method.