contour control
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2021 ◽  
Vol 13 (8) ◽  
pp. 168781402110348
Author(s):  
Sanxiu Wang ◽  
Shengtao Jiang

Friction is the main factor which degrades the control precisions of the servo system. In this paper, a cross coupled control method based on RBF neural network and disturbance observer is proposed for multi-axis servo system with LuGre friction, in order to implement high precision tracking and contouring control. Firstly, a feedback linearization controller is designed to realize the position stable tracking for single-axis motion; then, the disturbance observer is used to observe and compensate the friction. However, in practical application, the observation gain is difficult to select, and it is easy to cause observation error. In order to enhance the tracking accuracy and system robustness, the RBF neural network is introduced to approximate the disturbance observation error online. Finally, the cross coupled control is used to coordinate the motion between the axes to improve the contour accuracy. The simulation results show that the proposed method can effectively compensate the influence of friction on the system, has good tracking accuracy and high contour control precision.


2020 ◽  
Vol 10 (24) ◽  
pp. 9036
Author(s):  
Syuan-Yi Chen ◽  
Zi-Jie Chien ◽  
Wei-Yen Wang ◽  
Hsin-Han Chiang

Linear motors (LMs) are widely used in numerous industry automation where precise and fast motions are required to convert electric energy into linear actuation without the need of any switching mechanism. This study aims to develop a control strategy of auto-tuning cross-coupled two-degree-of-freedom proportional-integral-derivative (ACC2PID) to achieve extremely high-precision contour control of a LMs-driven X-Y-Y stage. Three 2PID controllers are developed to control the mover positions in individual axes while two compensators are designed to eliminate the contour errors in biaxial motions. Furthermore, an improved artificial bee colony algorithm is employed as a powerful optimization technique so that all the control parameters can be concurrently evaluated and optimized online while ensuring the non-fragility of the proposed controller. In this way, the tracking error in each axis and contour errors of the biaxial motions can be concurrently minimized, and further, satisfactory positioning accuracy and synchronization performance can be achieved. Finally, the experimental comparison results confirm the validity of the proposed ACC2PID control system regarding the multi-axis contour tracking control of the highly uncertain and nonlinear LMs-driven X-Y-Y stage.


2020 ◽  
Vol 25 (3) ◽  
pp. 1266-1275
Author(s):  
Jiangang Li ◽  
Yiming Wang ◽  
Yanan Li ◽  
Wenshu Luo

2020 ◽  
Vol 108 (9-10) ◽  
pp. 2803-2821
Author(s):  
Zhe Liu ◽  
Jingchuan Dong ◽  
Taiyong Wang ◽  
Chengzu Ren ◽  
Jianxin Guo

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Sanxiu Wang ◽  
Qiang Zhou ◽  
Yang Wang

In the contouring process, the trajectory generated by the computer numerical control (CNC) machine tool is a result of the multiaxis coordinated motion. The contour error has a direct impact on the accuracy of the machined product. To obtain higher contouring accuracy of the multiaxis motion control system, this paper presents a cross-coupled control approach based on the extended state observer sliding mode control. First, a single-axis sliding mode controller is designed, and an extended state observer is used to estimate system disturbances and improve the system robustness. Then, the cross-coupled control approach handles the coordinated motion of multiple axes to improve the contour control accuracy. Next, a simulation study is conducted on the three-axis motion platform. Its result shows that the control algorithm is effective in reducing tracking errors and contour errors.


2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Sanxiu Wang ◽  
Guang Chen ◽  
Yueli Cui

In two-axis servo contour motion control, friction and various uncertainties unavoidably exist, reducing the contour control accuracy. This paper proposes a neural network contour error coupling control method based on LuGre friction compensation, which includes a contour error calculation model, single-axis computed torque controller (CTC), and neural network friction compensation controller. The LuGre friction model can describe servo system’s complicated static and dynamic friction characteristics, and the RBF neural network has a universal approximation property to realize compensation control of friction. Simulation results indicate that the proposed contour error control method can effectively compensate for the effect of friction and improve contour control accuracy.


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