Online cooperative learning for nonlinear multi-agent systems with quantised states and inputs under switching topologies

This paper addresses the problem of fuzzy adaptive state and input quantisation control for nonlinear multi-agent systems (MASs) under switching topologies and directed graphs. The fuzzy logic system (FLS) is employed to approximate unknown nonlinear functions, while a second-order command filter is adopted to address the complexity inherent in computing derivatives of virtual control functions. Under the uniform quantiser and fuzzy adaptive backstepping design framework, the controller is designed via Lyapunov functions, and it is theoretically proven that all signals of the MASs are semi-globally and uniformly ultimately bounded (SGUUB). Finally, simulation experiments under switching topologies validate the effectiveness of the proposed control scheme.