Event-triggered neural adaptive anti-disturbance control of nonlinear multi-agent systems with asymmetric constraints

The event-triggered adaptive control problem of stochastic nonlinear multi-agent systems subject to stochastic faults and asymmetric output constraints is investigated in this paper. Radial basis function neural networks (RBFNNs) are employed to adaptively approximate the unknown nonlinearities and changes in system dynamics model due to stochastic failures. By utilising a one-to-one nonlinear mapping, the asymmetric output constraints stochastic system is converted into a system without any constraints. Furthermore, to save the communication resources between controller and actuator, an improved dynamic event-triggered mechanism is developed, which contains threshold parameters and an exponential convergence term. Then, based on the stochastic Lyapunov function method, an event-triggered adaptive fault-tolerant controller is proposed for the considered systems. It is shown that the developed adaptive fault-tolerant controller can guarantee that all the signals remain semi-globally uniformly ultimately bounded while the output constraint is satisfied, even if the system is affected by stochastic failures. Eventually, the example results are provided to illustrate the effectiveness of the proposed control methodology.