Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive
In order to cut down influence on the uncertainty disturbances of a linear motion single axis robot machine, such as the external load force, the cogging force, the column friction force, the Stribeck force, and the parameters variations, the micrometer backstepping control system, using an amended...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/16/3616 |
| _version_ | 1811304956496445440 |
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| author | Chih-Hong Lin Kuo-Tsai Chang |
| author_facet | Chih-Hong Lin Kuo-Tsai Chang |
| author_sort | Chih-Hong Lin |
| collection | DOAJ |
| description | In order to cut down influence on the uncertainty disturbances of a linear motion single axis robot machine, such as the external load force, the cogging force, the column friction force, the Stribeck force, and the parameters variations, the micrometer backstepping control system, using an amended recurrent Gottlieb polynomials neural network and altered ant colony optimization (AACO) with the compensated controller, is put forward for a linear motion single axis robot machine drive system mounted on the linear-optical ruler with 1 um resolution. To achieve high-precision control performance, an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is proposed to estimate the lumped uncertainty. Besides this, a novel error-estimated law of the compensated controller is also proposed to compensate for the estimated error between the lumped uncertainty and the amended recurrent Gottlieb polynomials neural network with the adaptive law. Meanwhile, the AACO is used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. The main contributions of this paper are: (1) The digital signal processor (DSP)-based current-regulation pulse width modulation (PWM) control scheme being successfully applied to control the linear motion single axis robot machine drive system; (2) the micrometer backstepping control system using an amended recurrent Gottlieb polynomials neural network with the compensated controller being successfully derived according to the Lyapunov function to diminish the lumped uncertainty effect; (3) achieving high-precision control performance, where an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is successfully applied to estimate the lumped uncertainty; (4) a novel error-estimated law of the compensated controller being successfully used to compensate for the estimated error; and (5) the AACO being successfully used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. Finally, the effectiveness of the proposed control scheme is also verified by the experimental results. |
| first_indexed | 2024-04-13T08:16:44Z |
| format | Article |
| id | doaj.art-503b72048c5e4d7694d10badc6b13b46 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:16:44Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-503b72048c5e4d7694d10badc6b13b462022-12-22T02:54:45ZengMDPI AGSensors1424-82202019-08-011916361610.3390/s19163616s19163616Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine DriveChih-Hong Lin0Kuo-Tsai Chang1Department of Electrical Engineering, National United University, 36063 Miaoli, TaiwanDepartment of Electrical Engineering, National United University, 36063 Miaoli, TaiwanIn order to cut down influence on the uncertainty disturbances of a linear motion single axis robot machine, such as the external load force, the cogging force, the column friction force, the Stribeck force, and the parameters variations, the micrometer backstepping control system, using an amended recurrent Gottlieb polynomials neural network and altered ant colony optimization (AACO) with the compensated controller, is put forward for a linear motion single axis robot machine drive system mounted on the linear-optical ruler with 1 um resolution. To achieve high-precision control performance, an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is proposed to estimate the lumped uncertainty. Besides this, a novel error-estimated law of the compensated controller is also proposed to compensate for the estimated error between the lumped uncertainty and the amended recurrent Gottlieb polynomials neural network with the adaptive law. Meanwhile, the AACO is used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. The main contributions of this paper are: (1) The digital signal processor (DSP)-based current-regulation pulse width modulation (PWM) control scheme being successfully applied to control the linear motion single axis robot machine drive system; (2) the micrometer backstepping control system using an amended recurrent Gottlieb polynomials neural network with the compensated controller being successfully derived according to the Lyapunov function to diminish the lumped uncertainty effect; (3) achieving high-precision control performance, where an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is successfully applied to estimate the lumped uncertainty; (4) a novel error-estimated law of the compensated controller being successfully used to compensate for the estimated error; and (5) the AACO being successfully used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. Finally, the effectiveness of the proposed control scheme is also verified by the experimental results.https://www.mdpi.com/1424-8220/19/16/3616ant colony optimizationbackstepping controlGottlieb polynomials neural networkLyapunov functionlinear motion single axis robot machine |
| spellingShingle | Chih-Hong Lin Kuo-Tsai Chang Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive Sensors ant colony optimization backstepping control Gottlieb polynomials neural network Lyapunov function linear motion single axis robot machine |
| title | Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive |
| title_full | Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive |
| title_fullStr | Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive |
| title_full_unstemmed | Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive |
| title_short | Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive |
| title_sort | micrometer backstepping control system for linear motion single axis robot machine drive |
| topic | ant colony optimization backstepping control Gottlieb polynomials neural network Lyapunov function linear motion single axis robot machine |
| url | https://www.mdpi.com/1424-8220/19/16/3616 |
| work_keys_str_mv | AT chihhonglin micrometerbacksteppingcontrolsystemforlinearmotionsingleaxisrobotmachinedrive AT kuotsaichang micrometerbacksteppingcontrolsystemforlinearmotionsingleaxisrobotmachinedrive |