Distributed event-triggered output-feedback synchronized tracking with connectivity-preserving performance guarantee for nonstrict-feedback nonlinear multiagent systems

In this paper, we present a connectivity-preserving performance function approach for the distributed output-feedback synchronized tracking of uncertain heterogeneous nonlinear multiagent systems in a nonstrict-feedback form. Compared with existing output -feedback cooperative control results using neural networks, this paper contributes to developing a universal output-feedback synchronized control methodology that uses a connectivity-preserving performance function to ensure both initial network connectivity and preselected synchronization performance with a designable convergence time. To this end, a neural-network-based adaptive observer for each follower is designed to ensure the boundedness of estimation errors of unmeasurable states. Then, local event-triggered syn-chronized trackers using only relative output information and the connectivity-preserving performance function are constructed to guarantee the closed-loop stability in a low -complexity sense, where no adaptive neural networks and command filters are not required in the local trackers. Finally, a purely academic example and a practical platoon-control problem of multiple uncertain vehicular systems are considered to clarify and verify the proposed connectivity-preserving performance function approach in the simulation. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

In this paper, a connectivity-preserving performance function approach is proposed for the distributed output-feedback synchronized tracking of uncertain heterogeneous nonlinear multiagent systems. The approach utilizes a universal output-feedback synchronized control methodology that ensures both initial network connectivity and preselected synchronization performance using a connectivity-preserving performance function with a designable convergence time. The approach includes the design of neural-network-based adaptive observers for each follower to ensure boundedness of estimation errors, and the construction of local event-triggered synchronized trackers using relative output information and the connectivity-preserving performance function in a low-complexity sense.