Neural-network distributed event-triggered consensus tracking control for high-order nonlinear strict-feedback multiagent systems

This paper focuses on the significant and practical problem that how to improve communication rate between neighbor agents for high-order nonlinear multiagent systems. This issue shall be addressed by establishing a series of event-triggered mechanisms between neighbor agents, such that neighbor signals are transmitted only when the preset events are activated. By utilizing backstepping approach, a novel event-triggered consensus controller design procedure is elaborated. Initially, the uncertainties of the systems are handled by radial basis function neural network, whose unknown weight vectors can be estimated by only one required adaptive law. Next, a novel consensus control strategy is proposed to resolve a contradiction between communication rate and system performance. Stability analysis of the control algorithm is then strictly proved by Lyapunov functional theory. This ensures that all the closed-loop signals remain bounded and the consensus errors converge exponentially to an adjustable domain. Simulation studies at the end demonstrate the effectiveness of this method.