Pub Date : 2021-07-15DOI: 10.3389/fcteg.2021.700053
Sulong Li, Qin Wang, E. Wang, Yangyang Chen
In this paper, the bearing-only formation control problem of a class of second-order system with unknown disturbance is investigated, where the control law merely depends on the relative bearings between neighboring agents. In order to offset the effect of unknown disturbance on the system, adaptive estimation is introduced. In the design of the control law, the back-stepping design method and the negative gradient method are used. The Barbalat’s lemma is used to prove the global stability of the system. The simulation results prove the effectiveness of the proposed formation control algorithm.
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Pub Date : 2021-02-22DOI: 10.3389/fcteg.2021.632417
Lei Xu, Yang-Yang Chen
The applications of the deep reinforcement learning method to achieve the arcs welding by multi-robot systems are presented, where the states and the actions of each robot are continuous and obstacles are considered in the welding environment. In order to adapt to the time-varying welding task and local information available to each robot in the welding environment, the so-called multi-agent deep deterministic policy gradient (MADDPG) algorithm is designed with a new set of rewards. Based on the idea of the distributed execution and centralized training, the proposed MADDPG algorithm is distributed. Simulation results demonstrate the effectiveness of the proposed method.
{"title":"Deep Reinforcement Learning Algorithms for Multiple Arc-Welding Robots","authors":"Lei Xu, Yang-Yang Chen","doi":"10.3389/fcteg.2021.632417","DOIUrl":"https://doi.org/10.3389/fcteg.2021.632417","url":null,"abstract":"The applications of the deep reinforcement learning method to achieve the arcs welding by multi-robot systems are presented, where the states and the actions of each robot are continuous and obstacles are considered in the welding environment. In order to adapt to the time-varying welding task and local information available to each robot in the welding environment, the so-called multi-agent deep deterministic policy gradient (MADDPG) algorithm is designed with a new set of rewards. Based on the idea of the distributed execution and centralized training, the proposed MADDPG algorithm is distributed. Simulation results demonstrate the effectiveness of the proposed method.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43586206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-29DOI: 10.3389/fcteg.2020.602469
A. Visioli
The paper discusses the strategies that should be applied to maximize the impact of control systems in our society. This is achieved, in particular, by revealing the role of control systems (at different levels) in different fields and by reducing the theory/practice gap so that methodologies proposed by academic researchers can be transferred to industry. Some considerations about the role of scientific publications are also made.
{"title":"Maximizing the Impact of Control at All Levels","authors":"A. Visioli","doi":"10.3389/fcteg.2020.602469","DOIUrl":"https://doi.org/10.3389/fcteg.2020.602469","url":null,"abstract":"The paper discusses the strategies that should be applied to maximize the impact of control systems in our society. This is achieved, in particular, by revealing the role of control systems (at different levels) in different fields and by reducing the theory/practice gap so that methodologies proposed by academic researchers can be transferred to industry. Some considerations about the role of scientific publications are also made.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3389/fcteg.2020.602469","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42309096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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