Event-triggered prescribed performance control for nonlinear multi-agent systems with input saturation

In this paper, the event-triggered output-feedback prescribed performance consensus tracking protocol is proposed for a class of strict-feedback nonlinear multi-agent systems with input saturation. By constructing neural network observers, the RBF neural networks are used to approximate the unknown nonlinear functions and unmeasurable state variables are estimated. The switching thresholds strategy is introduced to avoid the updating delay of control laws due to too large amplitudes of control inputs, the repeated oscillations of the control inputs are effectively avoided. The proposed control protocol guarantees all signals of the multi-agent systems are uniformly ultimate bounded, the transient and steady performance requirements of the tracking errors are guaranteed, and the communication overheads are reduced.