Adaptive Consensus Control of High-Order Uncertain Nonlinear Multi-agent Systems with Fuzzy Dead-Zone

This paper investigates the development of an adaptive cooperative control scheme for the consensus of uncertain nonlinear multi-agent systems subjected to uncertain time-varying control direction, disturbances, and dead-zone nonlinearity. The dead-zone nonlinearity is described by an uncertain nonlinear function of the control input which can represent a wide class of practical input nonlinearities. It is assumed that no prior knowledge about the uncertain nonlinearities of agents, disturbances, dead-zone parameters, magnitude and sign of the control gain is available. In this paper, a Nussbaum function is used to deal with the unknown time-varying control directions problem and a disturbance-like term in the dead-zone description is approximated by an adaptive TSK-type fuzzy system. Then, by using the dynamic surface control approach and radial-basis function neural network, an adaptive distributed controller is designed for each follower agent. Stability analysis shows that all signals of the closed-loop multi-agent system are semi-globally uniformly ultimately bounded and the consensus error can be made arbitrary small by the proper selection of design parameters. Simulation and comparison results demonstrate the effectiveness of the proposed algorithm.