Optimal Tracking Control of a Nonlinear Multiagent System Using Q-Learning via Event-Triggered Reinforcement Learning

This article offers an optimal control tracking method using an event-triggered technique and the internal reinforcement Q-learning (IrQL) algorithm to address the tracking control issue of unknown nonlinear systems with multiple agents (MASs). Relying on the internal reinforcement reward (IRR) form...

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Vydané v:Entropy (Basel, Switzerland) Ročník 25; číslo 2; s. 299
Hlavní autori: Wang, Ziwei, Wang, Xin, Tang, Yijie, Liu, Ying, Hu, Jun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 05.02.2023
MDPI
Predmet:
Algorithms
Analysis
Communication
Control
Controllers
Design
Distance learning
event-triggered mechanism
Game theory
Iterative methods
Machine learning
Mathematical optimization
Methods
Multi-agent systems
Multiagent systems
Neural networks
neural networks (NNs)
Nonlinear control
Nonlinear systems
Optimal control
optimal tracking control
Parameter modification
Process controls
Reinforcement learning (Machine learning)
reinforcement learning (RL)
System dynamics
systems with multiple agents
Technology application
Tracking control
China
systems with multiple agents
event-triggered mechanism
optimal tracking control
neural networks (NNs)
reinforcement learning (RL)
ISSN:1099-4300, 1099-4300
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Shrnutí:This article offers an optimal control tracking method using an event-triggered technique and the internal reinforcement Q-learning (IrQL) algorithm to address the tracking control issue of unknown nonlinear systems with multiple agents (MASs). Relying on the internal reinforcement reward (IRR) formula, a Q-learning function is calculated, and then the iteration IRQL method is developed. In contrast to mechanisms triggered by time, an event-triggered algorithm reduces the rate of transmission and computational load, since the controller may only be upgraded when the predetermined triggering circumstances are met. In addition, in order to implement the suggested system, a neutral reinforce-critic-actor (RCA) network structure is created that may assess the indices of performance and online learning of the event-triggering mechanism. This strategy is intended to be data-driven without having in-depth knowledge of system dynamics. We must develop the event-triggered weight tuning rule, which only modifies the parameters of the actor neutral network (ANN) in response to triggering cases. In addition, a Lyapunov-based convergence study of the reinforce-critic-actor neutral network (NN) is presented. Lastly, an example demonstrates the accessibility and efficiency of the suggested approach.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e25020299