Output-Feedback Consensus Maneuvering of Uncertain MIMO Strict-Feedback Multiagent Systems Based on a High-Order Neural Observer

• Published in: IEEE transactions on cybernetics Vol. 54; no. 7; pp. 4111 - 4123
• Main Authors: Zhang, Yibo, Wu, Wentao, Chen, Weixing, Lu, Haibo, Zhang, Weidong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive systems; Closed loops; Consensus maneuvering; Control methods; Control theory; Design parameters; Dynamic structural analysis; high-order neural observer; Maneuvers; MIMO; MIMO (control systems); Modular structures; Modular systems; Multi-agent systems; Multiagent systems; neural network adaptive control; Nonlinear systems; Observers; Output feedback; output-feedback control; Parameterization; Systems analysis; Task analysis; uncertain multi-input and multi-output (MIMO) multiagent systems; Vehicle dynamics
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2024.3351476

In this article, a distributed output-feedback consensus maneuvering problem is investigated for a class of uncertain multiagent systems with multi-input and multi-output (MIMO) strict-feedback dynamics. The followers are subject to immeasurable states and external disturbances. A distributed neural observer-based adaptive control method is designed for consensus maneuvering of uncertain MIMO multiagent systems. The method is based on a modular structure, resulting in the separation of three modules: 1) a variable update law for the parameterized path; 2) a high-order neural observer; and 3) an output-feedback consensus maneuvering control law. The proposed distributed neural observer-based adaptive control method ensures that all followers agree on a common motion guided by a desired parameterized path, and the proposed method evades adopting the adaptive backstepping or dynamic surface control design by reformulating the dynamics of agents, thereby reducing the complexity of the control structure. Combined with the cascade system analysis and interconnection system analysis, the input-to-state stability of the consensus maneuvering closed loop is established in the Lyapunov sense. A simulation example is presented to demonstrate the performance of the proposed distributed neural observer-based adaptive control method for output-feedback consensus maneuvering.