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

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...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 35; číslo 3; s. 1 - 9
Hlavní autoři: Wang, Bingjie, Xu, Lei, Yi, Xinlei, Jia, Yao, Yang, Tao
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3191673