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

• 成果类型: Article
• 署名作者: Yuan, Chengzhi; Stegagno, Paolo; He, Haibo; Ren, Wei
• 署名单位: University of Rhode Island; University of Rhode Island; University of California System; University of California Riverside
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3056336
• 发表日期: 2021
• 页码: 5612-5618
• 关键词: Symmetric matrices; CONVERGENCE; adaptive learning; Adaptive control; Robot kinematics; Protocols; Multi-agent systems; containment control; cooperative adaptive learning control; cooperative finite-time excitation

摘要

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.