An Intelligent Control Method for Servo Motor Based on Reinforcement Learning

Gao, Depeng; Wang, Shuai; Yang, Yuwei; Zhang, Haifei; Chen, Hao; Mei, Xiangxiang; Chen, Shuxi; Qiu, Jianlin

doi:10.3390/a17010014

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…The purpose is to transfer knowledge between tasks in different domains and provide various benefits, such as improving the performance of the agent in the target task, improving the agent's total reward and reducing the time needed to carry out the learning [17,18]. Along these lines, transfer reinforcement learning techniques have been applied in important domains, especially in the areas of robotics [19][20][21][22][23][24] and multiagent systems [25][26][27]. However, the literature has paid little attention to the transfer reinforcement learning for combinatorial optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Transfer Reinforcement Learning for Combinatorial Optimization Problems

Souza,

Santos,

Ottoni

et al. 2024

Algorithms

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Reinforcement learning is an important technique in various fields, particularly in automated machine learning for reinforcement learning (AutoRL). The integration of transfer learning (TL) with AutoRL in combinatorial optimization is an area that requires further research. This paper employs both AutoRL and TL to effectively tackle combinatorial optimization challenges, specifically the asymmetric traveling salesman problem (ATSP) and the sequential ordering problem (SOP). A statistical analysis was conducted to assess the impact of TL on the aforementioned problems. Furthermore, the Auto_TL_RL algorithm was introduced as a novel contribution, combining the AutoRL and TL methodologies. Empirical findings strongly support the effectiveness of this integration, resulting in solutions that were significantly more efficient than conventional techniques, with an 85.7% improvement in the preliminary analysis results. Additionally, the computational time was reduced in 13 instances (i.e., in 92.8% of the simulated problems). The TL-integrated model outperformed the optimal benchmarks, demonstrating its superior convergence. The Auto_TL_RL algorithm design allows for smooth transitions between the ATSP and SOP domains. In a comprehensive evaluation, Auto_TL_RL significantly outperformed traditional methodologies in 78% of the instances analyzed.

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Section: Introductionmentioning

confidence: 99%

Transfer Reinforcement Learning for Combinatorial Optimization Problems

Souza,

Santos,

Ottoni

et al. 2024

Algorithms

View full text Add to dashboard Cite

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A Risk-Sensitive Intelligent Control Algorithm for Servo Motor Based on Value Distribution

Gao,

Xiao,

Wang

et al. 2024

Applied Sciences

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With the development of artificial intelligence, reinforcement-learning-based intelligent control algorithms, which generally learn control strategies through trial and error, have received more attention in the automation equipment and manufacturing fields. Although they can intelligently adjust their control strategy without the need for human effort, the most relevant algorithms for servo motors only consider the overall performance, while ignoring the risks in special cases. Therefore, overcurrent problems are often triggered in the training process of the reinforcement learning agent. This can damage the motors’ service life and even burn it out directly. To solve this problem, in this study we propose a risk-sensitive intelligent control algorithm based on value distribution, which uses the quantile function to model the probability distribution of cumulative discount returns and employs the condition value at risk to measure the loss caused by overcurrent. The agent can accordingly learn a control strategy that is more sensitive to environmental restrictions and avoid the overcurrent problem. The performance is verified on three different servo motors with six control tasks, and the experimental results show that the proposed method can achieve fewer overcurrent occurrences than others in most cases.

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An Intelligent Control Method for Servo Motor Based on Reinforcement Learning

Cited by 2 publications

References 22 publications

Transfer Reinforcement Learning for Combinatorial Optimization Problems

Transfer Reinforcement Learning for Combinatorial Optimization Problems

A Risk-Sensitive Intelligent Control Algorithm for Servo Motor Based on Value Distribution

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