Energy Saving in Biaxial Feed Drive Systems Using Adaptive Sliding Mode Contouring Control with a Nonlinear Sliding Surface.
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| Title: | Energy Saving in Biaxial Feed Drive Systems Using Adaptive Sliding Mode Contouring Control with a Nonlinear Sliding Surface. |
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| Authors: | Farrage, Abdallah1,2 abdallahfraag@eng.au.edu.eg, Uchiyama, Naoki2 uchiyama@tut.jp |
| Source: | Mechatronics. Oct2018, Vol. 54, p26-35. 10p. |
| Subjects: | Machine tools -- Automatic control, Sliding mode control, Automatic control systems, Electronic control, Energy consumption |
| Abstract: | Highlights • The proposed control is proposed for machine tool feed drive systems. • Two different trajectories were experimentally implemented. • The proposed approach improves the contouring accuracy of the machine. • The proposed method reduces the consumed energy experimentally. • The control input variance is also experimentally decreased. Abstract Energy saving and producing highly accurate products are major essential demands in computer numerical control (CNC) machines. These machines operate all day and night for a long time, therefore they largely consume energy all over the world. This paper proposes adaptive sliding mode contouring control (ASMCC) with a nonlinear sliding surface (NSS) to reduce consumed energy and further improve machining accuracy for a biaxial feed drive system. The control gain of the proposed scheme is formulated to be mainly relied on a contour error. Once the contour error is changed, the control gain is adjusted simultaneously to generate appropriate control signal. ASMCC constantly reduces consumed energy and improves machining accuracy by reducing the resultant contour error. In order to verify the effectiveness of ASMCC, simulation and experiment are carried out on a biaxial feed drive system using a circular trajectory. Results show that the adaptive algorithm significantly reduces the mean contour error by 37.62% and 34.65% compared to sliding mode control (SMCC) without any additional energy on simulation and experiment, respectively. In addition, the proposed approach reduces experimentally consumed energy and control input variance by 8.20% and 17.29%, respectively. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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