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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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
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Abstract 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.
AbstractList 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.
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.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.
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. 
Author Wang, Bingjie
Xu, Lei
Yang, Tao
Yi, Xinlei
Jia, Yao
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SubjectTerms 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
Title Semiglobal Suboptimal Output Regulation for Heterogeneous Multi-Agent Systems With Input Saturation via Adaptive Dynamic Programming
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