Data-driven Machine Tools Motion Control Error Modeling Method Using Control Signal as Intermediate Value

doi:10.3901/JME.2022.23.178

Abstract

Abstract: Existing data-driven feed drive system modeling methods use end-to-end model, which employs machine learning algorithms to construct the model between the reference trajectory information (displacement, velocity, acceleration etc.) and the servo error directly, reducing the complexity of modeling. However, this kind of modeling method ignores the non-linear disturbance in motion control system reflected in the control signal, limiting the accuracy of constructed model. To solve the problem, a data-driven motion control error modeling method using control signal as intermediate value is proposed. Proposed method samples the reference trajectory information (velocity, acceleration, jerk etc.), control signal, tracking error, and constructs a reverse feature. Then, the control signal prediction network based on reference trajectory information and tracking error prediction network using control signal and reference trajectory information are constructed and trained, setting up the proposed model. Utilizing the feature that control signal can effectively reflect the nonlinear disturb, the accuracy of data-driven motion control error model is improved. In the validation and testing, the reference trajectory information is input into control signal prediction network to get the predicted control signal. And, the predicted control signal and reference trajectory information are input into tracking error prediction network, attaining the utilization of control signal as intermediate value in the data-driven modeling of feed drive system. Proposed modeling method is validated through experiments. Compared with traditional end-to-end modeling method without control signal, the proposed method shows better prediction accuracy. The prediction accuracy improves 16.33% and 20.42% respectively in X axis and Y axis, and the motion control contour accuracy improves 85.59% after error compensation, proving the effectiveness and feasibility of proposed method.

Key words: data-driven, feed drive system, machine learning, neural network, tracking error

CLC Number:

TG156

GUAN Yinxin, YANG Jixiang, DING Han. Data-driven Machine Tools Motion Control Error Modeling Method Using Control Signal as Intermediate Value[J]. Journal of Mechanical Engineering, 2022, 58(23): 178-187.

References

[1] KOREN Y. Computer control of manufacturing systems[M]. New York:McGraw-Hill,1983.
[2] ERKORKMAZ K,ALTINTAS Y. High speed CNC system design. Part II:Modeling and identification of feed drives[J]. International Journal of Machine Tools and Manufacture,2001,41(10):1487-1509. DOI:10.1016/S0890-6955(01)00003-7.
[3] YANG M,YANG J,DING H. A two-stage friction model and its application in tracking error pre-compensation of CNC machine tools[J]. Precision Engineering,2018,51:426-436. DOI:10.1016/j.precisioneng.2017.09.014.
[4] OKWUDIRE C,ALTINTAS Y. Minimum tracking error control of flexible ball screw drives using a discrete-time sliding mode controller[J]. Journal of Dynamic Systems,Measurement,and Control,2009,131(5):051006. DOI:10.1115/1.3155005.
[5] ARMSTRONG-HÉLOUVRY B,DUPONT P,DE WIT C C. A survey of models,analysis tools and compensation methods for the control of machines with friction[J]. Automatica,1994,30(7):1083-1138. DOI:10.1016/0005-1098(94)90209-7.
[6] YOUNKIN G. Modeling machine tool feed servo drives using simulation techniques to predict performance[J]. IEEE Transactions on Industry Applications,1991,27(2):268-274. DOI:10.1109/28.73610.
[7] ALTINTAS Y,VERL A,BRECHER C,et al. Machine tool feed drives[J]. CIRP Annals,2011,60(2):779-796. DOI:10.1016/j.cirp.2011.05.010.
[8] CHEN J S,HUANG Y K,CHENG C C. Mechanical model and contouring analysis of high-speed ball-screw drive systems with compliance effect[J]. The International Journal of Advanced Manufacturing Technology,2004,24(3-4):241-250. DOI:10.1007/s00170-003-1777-9.
[9] DEHNAVI H,MOVAHHEDY M R,NAEBI A,et al. Prediction of the effect of heat generation in ballscrew on the accuracy of CNC milling machine[C]//2012 UKSim 14th International Conference on Computer Modelling and Simulation. Cambridge,United Kingdom:IEEE,2012:278-282[2022-03-03]. http://ieeexplore.ieee.org/document/6205461/. DOI:10.1109/UKSim.2012.47.
[10] ERWINSKI K,PAPROCKI M,WAWRZAK A,et al. Neural network contour error predictor in CNC control systems[C]//201621st International Conference on Methods and Models in Automation and Robotics (MMAR). Miedzyzdroje,Poland:IEEE,2016:537-542[2022-02-15]. http://ieeexplore.ieee.org/document/7575193/. DOI:10.1109/MMAR.2016.7575193.
[11] LI J,QI C,LI Y,et al. Prediction and compensation of contour error of CNC systems based on LSTM neural-network[J]. IEEE/ASME Transactions on Mechatronics,2022,27(1):572-581. DOI:10.1109/TMECH.2021.3068354.
[12] JIANG Y,CHEN J,ZHOU H,et al. Nonlinear time-series modeling of feed drive system based on motion states classification[J]. Journal of Intelligent Manufacturing,2020,31(8):1935-1948. DOI:10.1007/s10845-020-01546-5.
[13] GUO C,CHEN L,DING J. A novel dynamics model of ball-screw feed drives based on theoretical derivations and deep learning[J]. Mechanism and Machine Theory,2019,141:196-212. DOI:10.1016/j.mechmachtheory. 2019.07.011.
[14] JIANG Y,CHEN J,ZHOU H,et al. Residual learning of the dynamics model for feeding system modelling based on dynamic nonlinear correlate factor analysis[J]. Applied Intelligence,2021[2021-06-02]. http://link.springer.com/10.1007/s10489-020-02096-2. DOI:10.1007/s10489-020-02096-2.
[15] WERBOS P J. Backpropagation through time:what it does and how to do it[J]. Proceedings of the IEEE,1990,78(10):1550-1560. DOI:10.1109/5.58337.
[16] HOCHREITER S,SCHMIDHUBER J. Long short-term memory[J]. Neural Computation,1997,9(8):1735-1780. DOI:10.1162/neco.1997.9.8.1735.
[17] HU Q,CHEN Y,JIN X,et al. A real-time C3 continuous tool path smoothing and interpolation algorithm for five-axis machine tools[J]. Journal of Manufacturing Science and Engineering,2020,142(4):041002. DOI:10.1115/1.4046091.
[18] YANG J,ALTINTAS Y. A generalized on-line estimation and control of five-axis contouring errors of CNC machine tools[J]. International Journal of Machine Tools and Manufacture,2015,88:9-23. DOI:10.1016/j.ijmachtools.2014.08.004.