Speed governing behavior on parallel two-wheeled electric scooter by life-sized humanoid robot based on learning control with online tuning for PID gain during control
This paper proposes the online learning controller for PID gain tuning to regulate the speed of robot on parallel two-wheeled electric scooter. We define this speed regulation as “speed governing behavior”. The proposed system based on Iterative Feedback Tuning (IFT) contributes the online continuit...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | Japanese |
| Published: |
The Japan Society of Mechanical Engineers
2018-06-01
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| Series: | Nihon Kikai Gakkai ronbunshu |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/transjsme/84/864/84_18-00032/_pdf/-char/en |
| _version_ | 1828193651760562176 |
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| author | Kohei KIMURA Hiroto MIZOHANA Shunichi NOZAWA Kei OKADA Masayuki INABA |
| author_facet | Kohei KIMURA Hiroto MIZOHANA Shunichi NOZAWA Kei OKADA Masayuki INABA |
| author_sort | Kohei KIMURA |
| collection | DOAJ |
| description | This paper proposes the online learning controller for PID gain tuning to regulate the speed of robot on parallel two-wheeled electric scooter. We define this speed regulation as “speed governing behavior”. The proposed system based on Iterative Feedback Tuning (IFT) contributes the online continuity during learning control not to interrupt and reset/restart the controller. PID gain update during control has the problem that the integral gain change affects the divergence of control input. To this problem, we propose the digital integrator which modifies the previous integrated value of control error. This proposed integrator solves the impulsive control input without any anti-windup or reference shaping. In conclusion, this paper shows the experimental results based on the online learning control and demonstrates the speed governing behavior by life-sized humanoid robot. We finally aim the driving of parallel two-wheeled electric scooter by speed control of humanoid robot. |
| first_indexed | 2024-04-12T09:12:07Z |
| format | Article |
| id | doaj.art-de51e83e85b043418168ec52496c3324 |
| institution | Directory Open Access Journal |
| issn | 2187-9761 |
| language | Japanese |
| last_indexed | 2024-04-12T09:12:07Z |
| publishDate | 2018-06-01 |
| publisher | The Japan Society of Mechanical Engineers |
| record_format | Article |
| series | Nihon Kikai Gakkai ronbunshu |
| spelling | doaj.art-de51e83e85b043418168ec52496c33242022-12-22T03:38:57ZjpnThe Japan Society of Mechanical EngineersNihon Kikai Gakkai ronbunshu2187-97612018-06-018486418-0003218-0003210.1299/transjsme.18-00032transjsmeSpeed governing behavior on parallel two-wheeled electric scooter by life-sized humanoid robot based on learning control with online tuning for PID gain during controlKohei KIMURA0Hiroto MIZOHANA1Shunichi NOZAWA2Kei OKADA3Masayuki INABA4Graduate School of Information Science and Technology, The University of TokyoGraduate School of Information Science and Technology, The University of TokyoGraduate School of Information Science and Technology, The University of TokyoGraduate School of Information Science and Technology, The University of TokyoGraduate School of Information Science and Technology, The University of TokyoThis paper proposes the online learning controller for PID gain tuning to regulate the speed of robot on parallel two-wheeled electric scooter. We define this speed regulation as “speed governing behavior”. The proposed system based on Iterative Feedback Tuning (IFT) contributes the online continuity during learning control not to interrupt and reset/restart the controller. PID gain update during control has the problem that the integral gain change affects the divergence of control input. To this problem, we propose the digital integrator which modifies the previous integrated value of control error. This proposed integrator solves the impulsive control input without any anti-windup or reference shaping. In conclusion, this paper shows the experimental results based on the online learning control and demonstrates the speed governing behavior by life-sized humanoid robot. We finally aim the driving of parallel two-wheeled electric scooter by speed control of humanoid robot.https://www.jstage.jst.go.jp/article/transjsme/84/864/84_18-00032/_pdf/-char/enonline learning controlpid controlleriterative feedback tuninganti-winduphumanoid robot |
| spellingShingle | Kohei KIMURA Hiroto MIZOHANA Shunichi NOZAWA Kei OKADA Masayuki INABA Speed governing behavior on parallel two-wheeled electric scooter by life-sized humanoid robot based on learning control with online tuning for PID gain during control Nihon Kikai Gakkai ronbunshu online learning control pid controller iterative feedback tuning anti-windup humanoid robot |
| title | Speed governing behavior on parallel two-wheeled electric scooter by life-sized humanoid robot based on learning control with online tuning for PID gain during control |
| title_full | Speed governing behavior on parallel two-wheeled electric scooter by life-sized humanoid robot based on learning control with online tuning for PID gain during control |
| title_fullStr | Speed governing behavior on parallel two-wheeled electric scooter by life-sized humanoid robot based on learning control with online tuning for PID gain during control |
| title_full_unstemmed | Speed governing behavior on parallel two-wheeled electric scooter by life-sized humanoid robot based on learning control with online tuning for PID gain during control |
| title_short | Speed governing behavior on parallel two-wheeled electric scooter by life-sized humanoid robot based on learning control with online tuning for PID gain during control |
| title_sort | speed governing behavior on parallel two wheeled electric scooter by life sized humanoid robot based on learning control with online tuning for pid gain during control |
| topic | online learning control pid controller iterative feedback tuning anti-windup humanoid robot |
| url | https://www.jstage.jst.go.jp/article/transjsme/84/864/84_18-00032/_pdf/-char/en |
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