{"title":"基于粒子群优化模型预测控制的Leader-follower多机器人编队系统","authors":"Hanzhen Xiao, C. L. P. Chen","doi":"10.1109/YAC.2017.7967457","DOIUrl":null,"url":null,"abstract":"For controlling the multi-robot formation system, a leader-follower separation-bearing-orientation scheme (S-BOS) is proposed and the leader-follower relationship can be represented as a formation-error kinematic system through SBOS strategy. In order to achieve the control objective, a nonlinear model predictive control (NMPC) strategy is applied to formulate the formation-error kinematic into a minimization optimization problem according to cost function. To solve this optimization problem online efficiently, a particle swarm optimization (PSO) is proposed to search for the global optimal solution as the control input. In the end of this work, simulations of the multi-robot formation are performed to verify the effectiveness of the developed strategy.","PeriodicalId":232358,"journal":{"name":"2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Leader-follower multi-robot formation system using model predictive control method based on particle swarm optimization\",\"authors\":\"Hanzhen Xiao, C. L. P. Chen\",\"doi\":\"10.1109/YAC.2017.7967457\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For controlling the multi-robot formation system, a leader-follower separation-bearing-orientation scheme (S-BOS) is proposed and the leader-follower relationship can be represented as a formation-error kinematic system through SBOS strategy. In order to achieve the control objective, a nonlinear model predictive control (NMPC) strategy is applied to formulate the formation-error kinematic into a minimization optimization problem according to cost function. To solve this optimization problem online efficiently, a particle swarm optimization (PSO) is proposed to search for the global optimal solution as the control input. In the end of this work, simulations of the multi-robot formation are performed to verify the effectiveness of the developed strategy.\",\"PeriodicalId\":232358,\"journal\":{\"name\":\"2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/YAC.2017.7967457\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/YAC.2017.7967457","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Leader-follower multi-robot formation system using model predictive control method based on particle swarm optimization
For controlling the multi-robot formation system, a leader-follower separation-bearing-orientation scheme (S-BOS) is proposed and the leader-follower relationship can be represented as a formation-error kinematic system through SBOS strategy. In order to achieve the control objective, a nonlinear model predictive control (NMPC) strategy is applied to formulate the formation-error kinematic into a minimization optimization problem according to cost function. To solve this optimization problem online efficiently, a particle swarm optimization (PSO) is proposed to search for the global optimal solution as the control input. In the end of this work, simulations of the multi-robot formation are performed to verify the effectiveness of the developed strategy.