Learning-Based Cooperative Multiagent Formation Control With Collision Avoidance

This article presents a learning-based controller to solve the cooperative formation control problem for multiagent system (MAS) with collision avoidance. First, the consensus problem of first-order MAS is mostly solved by linear matrix inequality (LMI) without consideration of energy loss. To overc...

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Vydáno v:IEEE transactions on systems, man, and cybernetics. Systems Ročník 52; číslo 12; s. 7341 - 7352
Hlavní autoři: Mu, Chaoxu, Peng, Jiangwen
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptive dynamic programming (ADP)
Algorithms
Artificial neural networks
artificial potential field (APF)
Collision avoidance
Consensus control
Convergence
Cooperative control
Dynamic programming
Error analysis
Force
generalized policy iteration (GPI)
Iterative methods
Learning
Linear matrix inequalities
Mathematical analysis
Multi-agent systems
multiagent system (MAS)
Multiagent systems
Neural networks
neural networks (NNs)
Optimal control
Performance analysis
Performance indices
Potential fields
Stability analysis
ISSN:2168-2216, 2168-2232
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Shrnutí:This article presents a learning-based controller to solve the cooperative formation control problem for multiagent system (MAS) with collision avoidance. First, the consensus problem of first-order MAS is mostly solved by linear matrix inequality (LMI) without consideration of energy loss. To overcome these difficulties, an adaptive dynamic programming (ADP) technique is fit to solve the consensus problem and similar formation control problem for second-order MAS by the establishment of a performance index function. Besides, we introduce the generalized policy iteration (GPI) algorithm as a kind of ADP technique without the problem of low convergence speed and high computational complexity. Combined with previous works, it can be found that our proposed structure can be extended to high-order cases based on the structure of local neighborhood formation error and algorithm. Afterward, the convergence analysis, optimality analysis, and stability analysis are given. Neural networks (NNs) are also implemented to approximate the iterative control policies and value functions, respectively. Moreover, we realize that many collisions may occur in the formation control problem. Inspired by the idea of the artificial potential field (APF) technique, the concept of the repulsive force field is introduced based on our proposed learning-based structure to avoid collisions simply and efficiently. Finally, a simulation is provided to demonstrate the effectiveness of our proposed method.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2022.3153030