Adaptive neural predefined-time dynamic surface control for nonlinear multi-agent systems with input quantisation via global prescribed-time performance

This study explores a nonsingular predefined-time control approach using dynamic surface control (DSC) for nonlinear multi-agent networks. First, under the predefined-time stability criterion, a smooth controller is realised, which allows for the system convergence time to be set beforehand. Furthermore, the nonlinear decomposition technique is used to address input quantisation, and the coupling problem between multi-agent systems and nonlinear dynamics is solved with RBF neural network approximator. Moreover, prescribed-time performance function is devised for tracking error performance convergence, while eliminating the limitations of the initial error condition. Furthermore, the difficult problem of integrating dynamic surface control into predefined time stability is addressed, dealing with the problem of explosion of complexity. It is finally demonstrated that the suggested controller can ensure the closed-loop system's predetermined time stability based on Lyapunov stability analysis, and the final simulation confirms the efficacy of the suggested approach.