Event‐Triggered Adaptive Neural Networks Fault‐Tolerant Control for Nonlinear Multi‐Agent Systems With Sensor Faults

This paper designs a novel adaptive neural networks (NNs) event‐triggered fault‐tolerant control scheme, which can effectively address the impacts of sensor faults and unknown external disturbances on non‐strict feedback multi‐agent systems (MASs). First, the radial basis function (RBF) NNs are employed to reconstruct the sensor faults for mitigating its adverse effects on the system. Second, compared with the conventional disturbance scaling method, the external disturbance observer is constructed to accurately eliminate the adverse effects induced by external disturbances. Third, a novel dynamic event‐triggered switching threshold strategy (DETSTS) is proposed to efficiently balance the diverse requirements of trigger conditions in different environments. The designed controller ensures that all signals in the closed‐loop system are semi‐globally, uniformly, ultimately bounded (SGUUB). Finally, simulation results verify the effectiveness of the proposed strategy.