Hostname: page-component-7bb8b95d7b-dtkg6 Total loading time: 0 Render date: 2024-09-21T03:57:36.633Z Has data issue: false hasContentIssue false

Feedforward combined multi-axis cross-coupling contour control compensation strategy of optical mirror processing robot

Published online by Cambridge University Press:  25 March 2022

Zujin Jin
Affiliation:
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou221116, China
Gang Cheng*
Affiliation:
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou221116, China Shangdong Zhongheng Optoelectronic Technology Co., Ltd., Zaozhuang277000, China
Yutong Meng
Affiliation:
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou221116, China
*
*Corresponding author. E-mail: chg@cumt.edu.cn

Abstract

During the movement of an optical mirror processing robot (OMPR), the movement error of each branch chain leads to contour errors of the grinding tool, which reduce the accuracy of the optical mirror surface. To improve the processing accuracy of an OMPR, it is necessary to study the control and compensation strategy of its contour error. In this study, first, a kinematics analysis of an OMPR is conducted, and the trajectory of the end execution point in the world coordinate system is transformed into the fixed coordinate system of the robot. Combined with the common trajectory of optical mirror processing, based on the Frenet coordinate system, contour error models of the OMPR in straight line, arc, and spiral trajectories are established. Subsequently, the contour error, feedforward channel gain, and compensation channel gain models of the parallel module are established in the task space, and concurrently, the control variables and stability of the system are analyzed. Finally, the established feedforward combined multi-axis cross-coupling contour control compensation strategy is analyzed experimentally to verify its accuracy and effectiveness. It provides a theoretical basis for a robot to directly face the precision processing object using the control and compensation strategy in a future research study to improve the molding accuracy of a surface and optimize the processing technology of a large-scale optical mirror.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Jin, Z., Cheng, G., Chen, S. and Guo, F., “Human-machine-environment information fusion and control compensation strategy for large optical mirror processing system,” Proc. Inst. Mech. Eng. Part C-J. Eng. Mech. Eng. Sci. 0954406220959689 (2020).Google Scholar
Jiao, C., Shu, Y., Chen, Y. and Zhang, Z., “The kinematics solving algorithm of single-axis polishing machine,” Optik 224, 165436 (2020).CrossRefGoogle Scholar
Jiang, B., Zhao, D., Wang, B., Zhao, H., Liu, Y. and Lu, X., “Flatness maintenance and roughness reduction of silicon mirror in chemical mechanical polishing process,” Sci. China-Technol. Sci. 63(1), 166172 (2020).CrossRefGoogle Scholar
Kong, Y., Cheng, G., Guo, F., Gu, W. and Zhang, L., “Inertia matching analysis of a 5-DOF hybrid optical machining manipulator,” J. Mech. Sci. Technol. 33(10), 49915002 (2019).CrossRefGoogle Scholar
Hosseini, M. and Daniali, H., “Cartesian workspace optimization of Tricept parallel manipulator with machining application,” Robotica 33(9), 19481957 (2012).CrossRefGoogle Scholar
Farooq, S., Baqai, A. and Shah, M., “Optimal design of tricept parallel manipulator with particle swarm optimization using performance parameters,” J. Eng. Res. 9(2), 378395 (2021).CrossRefGoogle Scholar
Li, J., Ye, F., Shen, N., Wang, Z. and Geng, L., “Dimensional synthesis of a 5-DOF hybrid robot,” Mech Mach. Theory 150, 103865 (2020).CrossRefGoogle Scholar
Gutierrez-Giles, A. and Arteaga-Perez, M., “Output feedback hybrid force/motion control for robotic manipulators interacting with unknown rigid surfaces,” Robotica 38(1), 136158 (2020).CrossRefGoogle Scholar
Hu, S., “Motion error analysis for a 3-dof parallel robot,” Adv. Mater. Res.-SWITZ 460, 347350 (2012).CrossRefGoogle Scholar
Ibaraki, S. and Hiruya, M., “Assessment of non-rigid body, direction-andvelocity-dependent error motions and their cross-talk by two-dimensional digital scale measurements at multiple positions,” Precis Eng.-J. Int. Soc. Precis. Eng. Nanotechnol. 66(2), 144153 (2020).Google Scholar
Li, X., Li, X., Cheng, Q., Li, R., Deng, W., Luo, X., Zhang, F., Xue, D. and Zhang, X., “Optimized strategy to restrain the mid-spatial-frequency surface error in computer-controlled optical surfacing,” Results Phys. 19(C), 103356 (2020).CrossRefGoogle Scholar
Zhang, H., Li, L., Zhao, J., Zhao, J., Liu, S. and Wu, J., “Design and implementation of hybrid force/position control for robot automation grinding aviation blade based on fuzzy PID,” Int. J. Adv. Manuf. Technol. 107(3–4), 17411754 (2020).CrossRefGoogle Scholar
Yang, X., Seethaler, R., Zhan, C., Lu, D. and Zhao, W., “A model predictive contouring error precompensation method,” IEEE Trans. Ind. Electron. 67(5), 40364045 (2020).CrossRefGoogle Scholar
Liu, B., Xu, M., Fang, J. and Shi, Y., “A feedrate optimization method for CNC machining based on chord error revaluation and contour error reduction,” Int. J. Adv. Manuf. Technol. 111(11–12), 34373452 (2020).CrossRefGoogle Scholar
Izadbakhsh, A., Khorashadizadeh, S. and Kheirkhahan, P., “Tracking control of electrically driven robots using a model-free observer,” Robotica 37(4), 729755 (2019).CrossRefGoogle Scholar
Du, X., Huang, J., Zhu, L. and Ding, H., “Sliding mode control with third-order contour error estimation for free -form contour following,” Precis. Eng.-J. Int. Soc. Precis. Eng. Nanotechnol. 66, 282294 (2020).Google Scholar
Yang, M., Yang, J., Zhu, L. and Yu, X., “A novel curvature circle iterative algorithm for contour error control of multi-axis CNC machine tools,” Precis. Eng.-J. Int. Soc. Precis. Eng. Nanotechnol. 65, 2331 (2020).Google Scholar
Song, D., Ma, J., Zhong, Y. and Yao, J., “Definition and estimation of joint-space contour error based on generalized curve for five-axis contour following control,” Precis. Eng.-J. Int. Soc. Precis. Eng. Nanotechnol. 65(1–4), 3243 (2020).Google Scholar
Ma, J., Li, G., Lu, X., Jia, Z., Qin, F. and Qu, Z., “Toolpath regeneration in subregional contour-parallel processing based on isoscallop method,” IEEE-ASME Trans. Mechatron. 26(2), 730740 (2021).CrossRefGoogle Scholar
Hu, Q., Chen, Y. and Yang, J., “On-line contour error estimation and control for corner smoothed five-axis tool paths,” Int. J. Mech. Sci. 171(6), 105377 (2020).CrossRefGoogle Scholar
Wang, Z., Hu, C. and Zhu, Y., “Double taylor expansion-based real-time contouring error estimation for multiaxis motion systems,” IEEE Trans. Ind. Electron. 66(12), 94909499 (2019).CrossRefGoogle Scholar
Song, D., Zhong, Y. and Ma, J., “Third-order contour-error estimation for arbitrary free-form paths in contour-following tasks,” Precis. Eng.-J. Int. Soc. Precis. Eng. Nanotechnol. 60(1–4), 8592 (2019).Google Scholar
Sheng, X. and Wang, L., “A comparison strategy for improving the precision of contour error estimation,” Int. J. Precis. Eng. Manuf. 20(8), 13951403 (2019).CrossRefGoogle Scholar
Yang, X., Seethaler, R., Zhan, C., Lu, D. and Zhao, W., “A novel contouring error estimation method for contouring control,” IEEE-ASME Trans. Mechatron. 24(4), 19021907 (2019).CrossRefGoogle Scholar
Hu, C., Wang, Z., Zhu, Y. and Zhang, M., “Accurate three-dimensional contouring error estimation and compensation scheme with zero-phase filter,” Int J. Mach. Tools Manuf. 128(11), 3340 (2018).CrossRefGoogle Scholar
Zhang, T., Wu, C. and Zou, Y., “Chord error constraint based integrated control strategy for contour error compensation,” Front. Mech. Eng. 15(4), 645658 (2020).CrossRefGoogle Scholar
Izadbakhsh, A., Khorashadizadeh, S. and Ghandali, S., “Robust adaptive impedance control of robot manipulators using Szasz-Mirakyan operator as universal approximator,” ISA Trans. 106(5), 111 (2020).CrossRefGoogle ScholarPubMed
Wang, Y., Zhang, W., Dong, H. and Yu, L., “A LADRC based fuzzy PID approach to contour error control of networked motion control system with time-varying delays,” Asian J. Control 22(5), 19731985 (2020).CrossRefGoogle Scholar
Li, K., Boonto, S. and Nuchkrua, T., “On-line self tuning of contouring control for high accuracy robot manipulators under various operations,” Int. J. Control Autom. Syst. 18(7), 18181828 (2020).CrossRefGoogle Scholar
Chen, M., Sun, Y. and Xu, J., “A new analytical path-reshaping model and solution algorithm for contour error pre-compensation in multi-axis computer numerical control machining,” J. Manuf. Sci. Eng.-Trans. ASME 142(6), 061006 (2020).CrossRefGoogle Scholar
Li, J., Wang, Y., Li, Y. and Luo, W., “Reference trajectory modification based on spatial iterative learning for contour control of two-axis NC systems,” IEEE-ASME Trans. Mechatron. 25(3), 12661275 (2020).CrossRefGoogle Scholar
Wang, Z., Hu, C. and Zhu, Y., “Dynamical model based contouring error position-loop feedforward control for multiaxis motion systems,” IEEE Trans. Ind. Inform. 15(8), 46864695 (2019).CrossRefGoogle Scholar
Duong, T., Rodriguez-Ayerbe, P., Lavernhe, S., Tournier, C. and Dumur, D., “Contour error pre-compensation for five-axis high speed machining: offline gain adjustment approach,” Int. J. Adv. Manuf. Technol. 100(9–12), 31133125 (2019).CrossRefGoogle Scholar
Cho, C., Song, Y., Lee, C. and Kim, H., “Neural network-based real time PID gain update algorithm for contour error reduction,” Int. J. Precis. Eng. Manuf. 19(11), 16191625 (2018).CrossRefGoogle Scholar
Liu, Y., Liang, L., Chu, T. and Wu, M., “N-PD cross-coupling synchronization control based on adjacent coupling error analysis,” J. Cent. South Univ. 25(5), 11541164 (2018).CrossRefGoogle Scholar
Wang, Z., Hu, C., Zhu, Y., He, S., Zhang, M. and Mu, H., “Newton-ILC contouring error estimation and coordinated motion control for precision multiaxis systems with comparative experiments,” IEEE Trans. Ind. Electron. 65(2), 14701480 (2018).CrossRefGoogle Scholar
Ouyang, P., Dam, T. and Pano, V., “Cross-coupled PID control in position domain for contour tracking,” Robotica 33(6), 13511374 (2015).CrossRefGoogle Scholar