Semiglobal Suboptimal Output Regulation for Heterogeneous Multi-Agent Systems With Input Saturation via Adaptive Dynamic Programming

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 3; pp. 1 - 9
• Main Authors: Wang, Bingjie, Xu, Lei, Yi, Xinlei, Jia, Yao, Yang, Tao
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control systems; Adaptive dynamic programming; Adaptive dynamic programming (ADP); Algorithms; Closed loops; Control strategies; Decentralised control; Decentralized control; Directed graphs; Dynamic programming; Eigenvalue and eigenfunctions; Eigenvalues and eigenfunctions; input saturation; Mathematical models; Multi agent systems; Multiagent systems; output regulation; Reference signals; Regulation; Regulator; Regulators; System dynamics
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2022.3191673

This article considers the semiglobal cooperative suboptimal output regulation problem of heterogeneous multi-agent systems with unknown agent dynamics in the presence of input saturation. To solve the problem, we develop distributed suboptimal control strategies from two perspectives, namely, model-based and data-driven. For the model-based case, we design a suboptimal control strategy by using the low-gain technique and output regulation theory. Moreover, when the agents' dynamics are unknown, we design a data-driven algorithm to solve the problem. We show that proposed control strategies ensure each agent's output gradually follows the reference signal and achieves interference suppression while guaranteeing closed-loop stability. The theoretical results are illustrated by a numerical simulation example.