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. 2023 Dec 11;23(24):9755.
doi: 10.3390/s23249755.

Integrated Controller Design and Application for CNC Machine Tool Servo Systems Based on Model Reference Adaptive Control and Adaptive Sliding Mode Control

Taihao Zhang  1 , Xuewei Li  1 , Hongdong Gai  2 , Yuheng Zhu  1 , Xiang Cheng  1
Affiliations

Integrated Controller Design and Application for CNC Machine Tool Servo Systems Based on Model Reference Adaptive Control and Adaptive Sliding Mode Control

Taihao Zhang et al. Sensors (Basel). .

Abstract

In order to reduce the effect of nonlinear friction and time-varying factors on the servo system of a computer numerical control (CNC) machine tool and improve its motion control accuracy, this paper uses an adaptive sliding mode control (ASMC) method based on model reference adaptive control (MRAC). The method adopts ASMC in the control outer loop and obtains the optimal control parameters by making the sliding mode control (SMC) law continuous and adaptively estimating the control parameters. At the same time, MRAC is used in the control inner loop to enhance the "invariance" of the controlled object so that the switching gain of SMC can satisfy the disturbance matching condition even under lesser conditions. Simulation and experimental results show that compared with the traditional SMC, the ASMC based on MRAC proposed in this paper effectively reduces the influence of nonlinear friction on the system performance, and the reduction in following error reaches 71.2%, which significantly improves the motion control accuracy of the control system. The spectral analysis of the following errors shows that the maximum magnitude reduction rate of the high-frequency chattering is 89.02%, which significantly reduces the effect of the high-frequency chattering and effectively improves the stability performance of the control system.

Keywords: adaptive sliding mode control; dynamics model for CNC machines; model reference adaptive control; nonlinear friction.

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Conflict of interest statement

Author Gai, H. was employed by the company Qilu Division, Sinopec Catalyst Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Equivalent double inertia mechanical model.
Figure 2
Figure 2
Simulink model of CNC machine tool dynamics.
Figure 3
Figure 3
Structural framework for ASMC based on MRAC.
Figure 4
Figure 4
Bipolar function schematic.
Figure 5
Figure 5
Trajectory following error curve.
Figure 6
Figure 6
Switching gain ε change curve.
Figure 7
Figure 7
Boundary thickness a change curve.
Figure 8
Figure 8
Following error curve.
Figure 9
Figure 9
Three-axis CNC engraving and milling machine experiment platform.
Figure 10
Figure 10
Step position response curve.
Figure 11
Figure 11
Steady state following error of step position response curve.
Figure 12
Figure 12
Comparison diagram of following error curve of two control methods.
Figure 13
Figure 13
Comparison diagram of following error curve of three control methods.
Figure 14
Figure 14
Following error graphs at different running times: (a) running time 1 s; (b) running time 2 s; (c) running time 4 s; (d) running time 8 s.
Figure 15
Figure 15
Mean value of following error for each processing run time.
Figure 16
Figure 16
Comparison diagram of following error curve of two control methods.
Figure 17
Figure 17
Comparison between ASMC and PID+rate feed-forward spectrum analysis.
Figure 18
Figure 18
Comparison between SMC and ASMC spectrum analysis.
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