Adaptive neural asymptotic control for uncertain nonlinear multiagent systems with a fuzzy dead zone constraint.

A consensus control scheme is developed for a class of leader-follower multiagent systems with a fuzzy dead zone constraint. Differently from previous work, the method developed implements a predefined convergence of tracking errors for multiagent systems; especially, the dynamic model for each follower has a fuzzy dead zone input. To solve the problem, not only a set of smooth functions are used within the control design to increase the stability of the systems but also the unfuzziness and center-of-gravity method are used to analyze and process the actuator constraint model, where each slope of the dead zone is uncertain and fuzzy. It is verified that the method developed effectively guarantees that the tracking errors of multiagent systems can converge to a predefined interval; that is, the problem of better asymptotic consensus performance for nonlinear multiagent systems is solved. Simulation illustrates the results obtained. [ABSTRACT FROM AUTHOR]