Fuzzy intelligent control method for improving flight attitude stability of plant protection quadrotor UAV
At present, the attitude control method of plant protection UAV is the classical PID control, but there are some imperfections in the PID control, such as the contradiction between speediness and overshoot, the weak anti-jamming ability and adaptability. The physical parameters of plant protection U...
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| Veröffentlicht in: | International journal of agricultural and biological engineering 2019-11, Vol.12 (6), p.110-115 |
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| container_title | International journal of agricultural and biological engineering |
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| creator | He, Zhihui Gao, Wanlin He, Xiongkui Wang, Minjuan Liu, Yunling Song, Yue An, Zewu |
| description | At present, the attitude control method of plant protection UAV is the classical PID control, but there are some imperfections in the PID control, such as the contradiction between speediness and overshoot, the weak anti-jamming ability and adaptability. The physical parameters of plant protection UAV are time-varying, and the airflow also interferes with it. The control ability of classical PID is limited, and its control parameters are fixed, and its anti-jamming ability and adaptability are not strong. Therefore, a fuzzy adaptive PID controller is proposed in this paper. Fuzzy logic control is used to optimize the control parameters of PID in order to improve the dynamic and static performance and adaptability of attitude control of plant protection UAV. In the process of research, the mathematical model of UAV is established firstly, then the fuzzy adaptive PID is designed, and then the simulation is carried out in Simulink. The simulation results show that the fuzzy adaptive PID controller has better dynamic and static control performance and adaptability than the traditional PID controller. Therefore, the proposed control method has excellent application value in the attitude of plant protection UAV. |
| doi_str_mv | 10.25165/j.ijabe.20191206.5108 |
| format | Article |
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School of Mechanical Engineer & Automation, Beihang University, Beijing 100191, China ; 2. Key Laboratory of Agricultural Informationization Standardization, Ministry of Agriculture and Rural Affairs, Beijing 100083, China ; 3. Centre for Chemicals Application Technology, College of Science, China Agricultural University, Beijing 100083, China ; 1. College of Information and Electrical Engineering, China Agricultural University, Beijing100083, China</creatorcontrib><description>At present, the attitude control method of plant protection UAV is the classical PID control, but there are some imperfections in the PID control, such as the contradiction between speediness and overshoot, the weak anti-jamming ability and adaptability. The physical parameters of plant protection UAV are time-varying, and the airflow also interferes with it. The control ability of classical PID is limited, and its control parameters are fixed, and its anti-jamming ability and adaptability are not strong. Therefore, a fuzzy adaptive PID controller is proposed in this paper. Fuzzy logic control is used to optimize the control parameters of PID in order to improve the dynamic and static performance and adaptability of attitude control of plant protection UAV. In the process of research, the mathematical model of UAV is established firstly, then the fuzzy adaptive PID is designed, and then the simulation is carried out in Simulink. The simulation results show that the fuzzy adaptive PID controller has better dynamic and static control performance and adaptability than the traditional PID controller. Therefore, the proposed control method has excellent application value in the attitude of plant protection UAV.</description><identifier>ISSN: 1934-6344</identifier><identifier>EISSN: 1934-6352</identifier><identifier>DOI: 10.25165/j.ijabe.20191206.5108</identifier><language>eng</language><publisher>Beijing: International Journal of Agricultural and Biological Engineering (IJABE)</publisher><subject>Adaptability ; Adaptive control ; Air flow ; Attitude control ; Attitude stability ; Computer simulation ; Control stability ; Controllers ; Fuzzy control ; Fuzzy logic ; Jamming ; Mathematical models ; Order parameters ; Physical properties ; Plant protection ; Process controls ; Proportional integral derivative ; Unmanned aerial vehicles</subject><ispartof>International journal of agricultural and biological engineering, 2019-11, Vol.12 (6), p.110-115</ispartof><rights>2019. This work is published under https://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c283t-35ce29d46a04da369885dadaa4ff75025403bfe20f7a7b2052a93d97353db8763</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>He, Zhihui</creatorcontrib><creatorcontrib>Gao, Wanlin</creatorcontrib><creatorcontrib>He, Xiongkui</creatorcontrib><creatorcontrib>Wang, Minjuan</creatorcontrib><creatorcontrib>Liu, Yunling</creatorcontrib><creatorcontrib>Song, Yue</creatorcontrib><creatorcontrib>An, Zewu</creatorcontrib><creatorcontrib>4. School of Mechanical Engineer & Automation, Beihang University, Beijing 100191, China</creatorcontrib><creatorcontrib>2. Key Laboratory of Agricultural Informationization Standardization, Ministry of Agriculture and Rural Affairs, Beijing 100083, China</creatorcontrib><creatorcontrib>3. Centre for Chemicals Application Technology, College of Science, China Agricultural University, Beijing 100083, China</creatorcontrib><creatorcontrib>1. College of Information and Electrical Engineering, China Agricultural University, Beijing100083, China</creatorcontrib><title>Fuzzy intelligent control method for improving flight attitude stability of plant protection quadrotor UAV</title><title>International journal of agricultural and biological engineering</title><description>At present, the attitude control method of plant protection UAV is the classical PID control, but there are some imperfections in the PID control, such as the contradiction between speediness and overshoot, the weak anti-jamming ability and adaptability. The physical parameters of plant protection UAV are time-varying, and the airflow also interferes with it. The control ability of classical PID is limited, and its control parameters are fixed, and its anti-jamming ability and adaptability are not strong. Therefore, a fuzzy adaptive PID controller is proposed in this paper. Fuzzy logic control is used to optimize the control parameters of PID in order to improve the dynamic and static performance and adaptability of attitude control of plant protection UAV. In the process of research, the mathematical model of UAV is established firstly, then the fuzzy adaptive PID is designed, and then the simulation is carried out in Simulink. The simulation results show that the fuzzy adaptive PID controller has better dynamic and static control performance and adaptability than the traditional PID controller. Therefore, the proposed control method has excellent application value in the attitude of plant protection UAV.</description><subject>Adaptability</subject><subject>Adaptive control</subject><subject>Air flow</subject><subject>Attitude control</subject><subject>Attitude stability</subject><subject>Computer simulation</subject><subject>Control stability</subject><subject>Controllers</subject><subject>Fuzzy control</subject><subject>Fuzzy logic</subject><subject>Jamming</subject><subject>Mathematical models</subject><subject>Order parameters</subject><subject>Physical properties</subject><subject>Plant protection</subject><subject>Process controls</subject><subject>Proportional integral derivative</subject><subject>Unmanned aerial vehicles</subject><issn>1934-6344</issn><issn>1934-6352</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNo9kF1LwzAUhoMoOKd_QQJet-a77eUYzgkDb5y3IW2SLaVrtjQVtl9v3NSrcw485z2HB4BHjHLCseDPbe5aVZucIFxhgkTOMSqvwARXlGWCcnL93zN2C-6GoUVIsJLyCWgX4-l0hK6PpuvcxvQRNr6PwXdwZ-LWa2h9gG63D_7L9RtoE7SNUMXo4qgNHKKqXefiEXoL951K-wmNponO9_AwKp2mlLCefd6DG6u6wTz81ilYL14-5sts9f76Np-tsoaUNGaUN4ZUmgmFmFZUVGXJtdJKMWsLjghniNbWEGQLVdQEcaIqqquCcqrrshB0Cp4uuemRw2iGKFs_hj6dlIRSjnmVlCRKXKgm-GEIxsp9cDsVjhIjefYqW3n2Kv-8yh-v9BuUO29Z</recordid><startdate>20191101</startdate><enddate>20191101</enddate><creator>He, Zhihui</creator><creator>Gao, Wanlin</creator><creator>He, Xiongkui</creator><creator>Wang, Minjuan</creator><creator>Liu, Yunling</creator><creator>Song, Yue</creator><creator>An, Zewu</creator><general>International Journal of Agricultural and Biological Engineering (IJABE)</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7QO</scope><scope>7SN</scope><scope>7ST</scope><scope>7T7</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BVBZV</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>RC3</scope><scope>SOI</scope></search><sort><creationdate>20191101</creationdate><title>Fuzzy intelligent control method for improving flight attitude stability of plant protection quadrotor UAV</title><author>He, Zhihui ; Gao, Wanlin ; He, Xiongkui ; Wang, Minjuan ; Liu, Yunling ; Song, Yue ; An, Zewu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c283t-35ce29d46a04da369885dadaa4ff75025403bfe20f7a7b2052a93d97353db8763</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adaptability</topic><topic>Adaptive control</topic><topic>Air flow</topic><topic>Attitude control</topic><topic>Attitude stability</topic><topic>Computer simulation</topic><topic>Control stability</topic><topic>Controllers</topic><topic>Fuzzy control</topic><topic>Fuzzy logic</topic><topic>Jamming</topic><topic>Mathematical models</topic><topic>Order parameters</topic><topic>Physical properties</topic><topic>Plant protection</topic><topic>Process controls</topic><topic>Proportional integral derivative</topic><topic>Unmanned aerial vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>He, Zhihui</creatorcontrib><creatorcontrib>Gao, Wanlin</creatorcontrib><creatorcontrib>He, Xiongkui</creatorcontrib><creatorcontrib>Wang, Minjuan</creatorcontrib><creatorcontrib>Liu, Yunling</creatorcontrib><creatorcontrib>Song, Yue</creatorcontrib><creatorcontrib>An, Zewu</creatorcontrib><creatorcontrib>4. 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School of Mechanical Engineer & Automation, Beihang University, Beijing 100191, China</aucorp><aucorp>2. Key Laboratory of Agricultural Informationization Standardization, Ministry of Agriculture and Rural Affairs, Beijing 100083, China</aucorp><aucorp>3. Centre for Chemicals Application Technology, College of Science, China Agricultural University, Beijing 100083, China</aucorp><aucorp>1. College of Information and Electrical Engineering, China Agricultural University, Beijing100083, China</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fuzzy intelligent control method for improving flight attitude stability of plant protection quadrotor UAV</atitle><jtitle>International journal of agricultural and biological engineering</jtitle><date>2019-11-01</date><risdate>2019</risdate><volume>12</volume><issue>6</issue><spage>110</spage><epage>115</epage><pages>110-115</pages><issn>1934-6344</issn><eissn>1934-6352</eissn><abstract>At present, the attitude control method of plant protection UAV is the classical PID control, but there are some imperfections in the PID control, such as the contradiction between speediness and overshoot, the weak anti-jamming ability and adaptability. The physical parameters of plant protection UAV are time-varying, and the airflow also interferes with it. The control ability of classical PID is limited, and its control parameters are fixed, and its anti-jamming ability and adaptability are not strong. Therefore, a fuzzy adaptive PID controller is proposed in this paper. Fuzzy logic control is used to optimize the control parameters of PID in order to improve the dynamic and static performance and adaptability of attitude control of plant protection UAV. In the process of research, the mathematical model of UAV is established firstly, then the fuzzy adaptive PID is designed, and then the simulation is carried out in Simulink. The simulation results show that the fuzzy adaptive PID controller has better dynamic and static control performance and adaptability than the traditional PID controller. Therefore, the proposed control method has excellent application value in the attitude of plant protection UAV.</abstract><cop>Beijing</cop><pub>International Journal of Agricultural and Biological Engineering (IJABE)</pub><doi>10.25165/j.ijabe.20191206.5108</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Adaptability Adaptive control Air flow Attitude control Attitude stability Computer simulation Control stability Controllers Fuzzy control Fuzzy logic Jamming Mathematical models Order parameters Physical properties Plant protection Process controls Proportional integral derivative Unmanned aerial vehicles |
| title | Fuzzy intelligent control method for improving flight attitude stability of plant protection quadrotor UAV |
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