A dual asynchronous impulsive neuro-adaptive control framework for multiagent systems with multiple uncertainties

This paper studies the tracking consensus of multiagent systems in an asynchronous impulsive control framework with multiple uncertainties including dynamics uncertainty, unknown loss of effectiveness of communication, partial immeasurable states and unknown leader input. Such framework has flexibility of control design and requires low communication resources, but difficulty appears since the uncertainties are divided into two types as continuous-time and non-continuous-time uncertainties due to their affection in time scales, which cannot be addressed by a general method. Observers and compensators based on the adaptive learning of neural networks (NNs) are designed to handle the continuous-time uncertainties while an asynchronous impulsive adaptive mechanism is proposed to handle the non-continuous-time uncertainties. The updating instants of adaptive parameters can be different from the asynchronous impulsive control instants, which together form a dual asynchronous impulsive adaptive control framework. The convergence analysis of observers, adaptive parameters and NNs are made firstly. Then, concise sufficient conditions of the tracking consensus are derived by a flexible analysis in a general case while a reduced case with improvement is also introduced to show the flexibility of such method. Finally, numerical examples including simulations and comparison are presented.