Cooperative Adaptive Containment Control With Parameter Convergence via Cooperative Finite-Time Excitation

This article addresses the problem of cooperative adaptive containment control for multiagent systems, which specifies the objective of jointly achieving containment control and accurate adaptive learning/identification of unknown system parameters. We consider a class of linear uncertain multiagent systems with multiple leaders subject to bounded unmeasurable inputs and multiple followers subject to unknown system dynamics. A novel cooperative adaptive containment control architecture is proposed, which consists of a discontinuous nonlinear state-feedback control law and a filter-based cooperative adaptation law. This new control architecture is compelling in the sense that exponential convergence of both containment tracking errors to zero and adaptation parameters to their true values can be achieved simultaneously under a mild cooperative finite-time excitation condition. This condition significantly relaxes existing ones (e.g., persistent excitation and finite-time excitation) for parameter identification in adaptive control systems. Effectiveness of the proposed approach has been demonstrated through both rigorous analysis and a case study.

Automated summary

This article addresses the problem of cooperative adaptive containment control for multiagent systems through a novel control architecture that achieves both containment control and parameter adaptive learning simultaneously. The proposed method achieves exponential convergence of containment tracking errors to zero and adaptation parameters to their true values under a mild cooperative finite-time excitation condition.