Abstract
This paper focuses on the problem of the observer-based event-triggered adaptive containment control for a class of nonlinear multiagent systems (MASs) with prescribed performance. First, the radial basis function neural networks (RBFNNs) are adopted to approximate the uncertain smooth nonlinear function, and the neural network-based state observer is designed to estimate the unmeasurable state. Besides, to reduce the control resource assumption and get a better balance between the system performance and network constraints, the switching threshold based event-triggered control strategy is introduced. Based on this, the novel distributed containment controller is designed by utilizing the adaptive backstepping technique and the dynamic surface control (DSC) technique to guarantee that the output of each follower converges to the convex hull formed by multileader.
Moreover, the containment errors can be converged to the prescribed boundary and all signals in closed-loop system are semi-global uniformly ultimately bounded (SGUUB) as well. Finally, the simulation example is carried out to illustrate the efficiency of the proposed controller.
Observer-based event-triggered adaptive containment control for multiagent systems with prescribed performance
