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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Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 34; číslo 3; s. 1524 - 1536
Hlavní autoři:	Wen, Guoxing, Chen, C. L. Philip
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.03.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
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