Practical Control of a Cold Milling Machine using an Adaptive PID Controller
This paper presents a supervised Hebb learning single neuron adaptive proportional-integral-derivative (PID) controller for the power control of a cold milling machine. The proposed controller aims to overcome the deficiency of the current power control algorithm, and to achieve as high an output po...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/7/2516 |
| _version_ | 1797571364594384896 |
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| author | Fanwei Meng Yongbiao Hu Pengyu Ma Xuping Zhang Zhixiong Li |
| author_facet | Fanwei Meng Yongbiao Hu Pengyu Ma Xuping Zhang Zhixiong Li |
| author_sort | Fanwei Meng |
| collection | DOAJ |
| description | This paper presents a supervised Hebb learning single neuron adaptive proportional-integral-derivative (PID) controller for the power control of a cold milling machine. The proposed controller aims to overcome the deficiency of the current power control algorithm, and to achieve as high an output power as possible for the cold milling machine. The control process and system model are established and presented to provide the insight and guidance to the controller design and analysis. The adaptive PID controller is developed using a supervised Hebb learning single neuron method with detailed algorithm and structure analysis. The field test is performed to validate the proposed single neuron adaptive PID control for the power control. In the test, the 8 cm-depth milling is conducted on a cement concrete pavement in which the cement is not well-distributed. The test results show that when the machine speed is adjusted by the machine itself or manually without the adaptive power control system, the machine is often overloaded or underloaded, and the average work speed is 2.4m/min. However, when the adaptive control system is implemented on the machine, it works very close to its rated work condition during its work process. With the developed controller, the machine work speed is adjusted in time to the load variation and uncertain dynamics. The average machine work speed can reach up to 2.766 m/min, which is 15.25% higher than the wok speed of the machine without an adaptive power control system. |
| first_indexed | 2024-03-10T20:39:22Z |
| format | Article |
| id | doaj.art-f41e431e33334839b738e5111d81a200 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:39:22Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f41e431e33334839b738e5111d81a2002023-11-19T20:49:04ZengMDPI AGApplied Sciences2076-34172020-04-01107251610.3390/app10072516Practical Control of a Cold Milling Machine using an Adaptive PID ControllerFanwei Meng0Yongbiao Hu1Pengyu Ma2Xuping Zhang3Zhixiong Li4National Engineering Laboratory for Highway Maintenance Equipment, Chang’an University, Xi’an 710064, ChinaNational Engineering Laboratory for Highway Maintenance Equipment, Chang’an University, Xi’an 710064, ChinaNational Engineering Laboratory for Highway Maintenance Equipment, Chang’an University, Xi’an 710064, ChinaDepartment of Engineering & School of Engineering, Aarhus University, 8000 Aarhus C, DenmarkSchool of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong 2522, AustraliaThis paper presents a supervised Hebb learning single neuron adaptive proportional-integral-derivative (PID) controller for the power control of a cold milling machine. The proposed controller aims to overcome the deficiency of the current power control algorithm, and to achieve as high an output power as possible for the cold milling machine. The control process and system model are established and presented to provide the insight and guidance to the controller design and analysis. The adaptive PID controller is developed using a supervised Hebb learning single neuron method with detailed algorithm and structure analysis. The field test is performed to validate the proposed single neuron adaptive PID control for the power control. In the test, the 8 cm-depth milling is conducted on a cement concrete pavement in which the cement is not well-distributed. The test results show that when the machine speed is adjusted by the machine itself or manually without the adaptive power control system, the machine is often overloaded or underloaded, and the average work speed is 2.4m/min. However, when the adaptive control system is implemented on the machine, it works very close to its rated work condition during its work process. With the developed controller, the machine work speed is adjusted in time to the load variation and uncertain dynamics. The average machine work speed can reach up to 2.766 m/min, which is 15.25% higher than the wok speed of the machine without an adaptive power control system.https://www.mdpi.com/2076-3417/10/7/2516cold milling machinesupervised Hebb learningsingle neuronadaptive power control |
| spellingShingle | Fanwei Meng Yongbiao Hu Pengyu Ma Xuping Zhang Zhixiong Li Practical Control of a Cold Milling Machine using an Adaptive PID Controller Applied Sciences cold milling machine supervised Hebb learning single neuron adaptive power control |
| title | Practical Control of a Cold Milling Machine using an Adaptive PID Controller |
| title_full | Practical Control of a Cold Milling Machine using an Adaptive PID Controller |
| title_fullStr | Practical Control of a Cold Milling Machine using an Adaptive PID Controller |
| title_full_unstemmed | Practical Control of a Cold Milling Machine using an Adaptive PID Controller |
| title_short | Practical Control of a Cold Milling Machine using an Adaptive PID Controller |
| title_sort | practical control of a cold milling machine using an adaptive pid controller |
| topic | cold milling machine supervised Hebb learning single neuron adaptive power control |
| url | https://www.mdpi.com/2076-3417/10/7/2516 |
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