Optimized Backstepping Consensus Control Using Reinforcement Learning for a Class of Nonlinear Strict-Feedback-Dynamic Multi-Agent Systems

In this article, an optimized leader-following consensus control scheme is proposed for the nonlinear strict-feedback-dynamic multi-agent system by learning from the controlling idea of optimized backstepping technique, which designs the virtual and actual controls of backstepping to be the optimize...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 34; no. 3; pp. 1524 - 1536
Main Authors: Wen, Guoxing, Chen, C. L. Philip
Format: Journal Article
Language:English
Published: United States IEEE 01.03.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximation
Artificial neural networks
Backstepping
Consensus control
Critic-actor architecture
Feedback
high-order multi-agent system (MAS)
Learning
Machine learning
Mathematical model
Multiagent systems
Natural selection
neural network (NN)
Neural networks
Nonlinear control
Nonlinear dynamical systems
Optimal control
Optimization
Performance analysis
Reinforcement
reinforcement learning (RL)
Subsystems
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:In this article, an optimized leader-following consensus control scheme is proposed for the nonlinear strict-feedback-dynamic multi-agent system by learning from the controlling idea of optimized backstepping technique, which designs the virtual and actual controls of backstepping to be the optimized solution of corresponding subsystems so that the entire backstepping control is optimized. Since this control needs to not only ensure the optimizing system performance but also synchronize the multiple system state variables, it is an interesting and challenging topic. In order to achieve this optimized control, the neural network approximation-based reinforcement learning (RL) is performed under critic-actor architecture. In most of the existing RL-based optimal controls, since both the critic and actor RL updating laws are derived from the negative gradient of square of the Hamilton-Jacobi-Bellman (HJB) equation's approximation, which contains multiple nonlinear terms, their algorithm are inevitably intricate. However, the proposed optimized control derives the RL updating laws from the negative gradient of a simple positive function, which is correlated with the HJB equation; hence, it can be significantly simple in the algorithm. Meanwhile, it can also release two general conditions, known dynamic and persistence excitation, which are required in most of the RL-based optimal controls. Therefore, the proposed optimized scheme can be a natural selection for the high-order nonlinear multi-agent control. Finally, the effectiveness is demonstrated by both theory and simulation.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3105548