H. Fukushima, K. Kon, Y. Hada, F. Matsuno, K. Kawabata, H. Asama
{"title":"存在时滞和扰动的自主飞艇状态预测控制","authors":"H. Fukushima, K. Kon, Y. Hada, F. Matsuno, K. Kawabata, H. Asama","doi":"10.1109/CCA.2007.4389228","DOIUrl":null,"url":null,"abstract":"In this paper, state-predictive control is applied to an autonomous blimp in the presence of time delay and disturbance. To this end, a state predictor to compensate time delay is constructed based on the separate-bias filters taking into account nonzero-mean disturbances. Experimental results show that constraint violations are reduced in model predictive control (MPC) with input and state constraints by compensating time delay. Also, flight experiments in the presence of the winds show that the steady-state error to disturbances are reduced as a result that the state prediction performance is improved by using separate-bias predictor. Moreover, MPC using soft bounds is applied for recovering constraint violations due to disturbances.","PeriodicalId":176828,"journal":{"name":"2007 IEEE International Conference on Control Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"State-Predictive Control of an Autonomous Blimp in the Presence of Time Delay and Disturbance\",\"authors\":\"H. Fukushima, K. Kon, Y. Hada, F. Matsuno, K. Kawabata, H. Asama\",\"doi\":\"10.1109/CCA.2007.4389228\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, state-predictive control is applied to an autonomous blimp in the presence of time delay and disturbance. To this end, a state predictor to compensate time delay is constructed based on the separate-bias filters taking into account nonzero-mean disturbances. Experimental results show that constraint violations are reduced in model predictive control (MPC) with input and state constraints by compensating time delay. Also, flight experiments in the presence of the winds show that the steady-state error to disturbances are reduced as a result that the state prediction performance is improved by using separate-bias predictor. Moreover, MPC using soft bounds is applied for recovering constraint violations due to disturbances.\",\"PeriodicalId\":176828,\"journal\":{\"name\":\"2007 IEEE International Conference on Control Applications\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Conference on Control Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCA.2007.4389228\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Conference on Control Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCA.2007.4389228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
State-Predictive Control of an Autonomous Blimp in the Presence of Time Delay and Disturbance
In this paper, state-predictive control is applied to an autonomous blimp in the presence of time delay and disturbance. To this end, a state predictor to compensate time delay is constructed based on the separate-bias filters taking into account nonzero-mean disturbances. Experimental results show that constraint violations are reduced in model predictive control (MPC) with input and state constraints by compensating time delay. Also, flight experiments in the presence of the winds show that the steady-state error to disturbances are reduced as a result that the state prediction performance is improved by using separate-bias predictor. Moreover, MPC using soft bounds is applied for recovering constraint violations due to disturbances.