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Model-Free Event-Tr...
Model-Free Event-Triggered Optimal Consensus Control of Multiple Euler-Lagrange Systems via Reinforcement Learning
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- Wang, Saiwei (author)
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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- Jin, Xin (author)
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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- Mao, Shuai (author)
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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- Vasilakos, Athanasios V. (author)
- Luleå tekniska universitet,Datavetenskap,School of Electrical and Data Engineering, University of Technology Sydney, Australia. Department of Computer Science and Technology, Fuzhou University, Fuzhou 350116, China
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- Tang, Yang (author)
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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(creator_code:org_t)
- IEEE, 2021
- 2021
- English.
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In: IEEE Transactions on Network Science and Engineering. - : IEEE. - 2327-4697. ; 8:1, s. 246-258
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- This paper develops a model-free approach to solve the event-triggered optimal consensus of multiple Euler-Lagrange systems (MELSs) via reinforcement learning (RL). Firstly, an augmented system is constructed by defining a pre-compensator to circumvent the dependence on system dynamics. Secondly, the Hamilton-Jacobi-Bellman (HJB) equations are applied to the deduction of the model-free event-triggered optimal controller. Thirdly, we present a policy iteration (PI) algorithm derived from reinforcement learning (RL), which converges to the optimal policy. Then, the value function of each agent is represented through a neural network to realize the PI algorithm. Moreover, the gradient descent method is used to update the neural network only at a series of discrete event-triggered instants. The specific form of the event-triggered condition is then proposed, and it is guaranteed that the closed-loop augmented system under the event-triggered mechanism is uniformly ultimately bounded (UUB). Meanwhile, the Zeno behavior is also eliminated. Finally, the validity of this approach is verified by a simulation example.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Reinforcement learning
- event-triggered control
- Euler-Lagrange system
- augmented system
- Pervasive Mobile Computing
- Distribuerade datorsystem
Publication and Content Type
- ref (subject category)
- art (subject category)
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