An instrumental variable method for robot identification based on time variable parameter estimation

Brunot, Mathieu ; Janot, Alexandre ; Young, Peter ; Carrillo, Francisco

Kybernetika, Tome 54 (2018) no. 1, pp. 202-220

Abstract (VO)

This paper considers the data-based identification of industrial robots using an instrumental variable method that uses off-line estimation of the joint velocities and acceleration signals based only on the measurement of the joint positions. The usual approach to this problem relies on a ‘tailor-made’ prefiltering procedure for estimating the derivatives that depends on good prior knowledge of the system's bandwidth. The paper describes an alternative Integrated Random Walk SMoothing (IRWSM) method that is more robust to deficiencies in such a priori knowledge and exploits an optimal recursive algorithm based on a simple integrated random walk model and a Kalman filter with associated fixed interval smoothing. The resultant IDIM-IV instrumental variable method, using this approach to signal generation, is evaluated by its application to an industrial robot arm and comparison with previously proposed methods.

Détail

DOI : 10.14736/kyb-2018-1-0202

Classification : 70E60, 93B30
Keywords: industrial robot system; system identification; instrumental variable method; parameter estimation; Kalman filter; fixed interval smoothing

Bibliographie

[1] Bélanger, P .R., Dobrovolny, P., Helmy, A., Zhang, X.: Estimation of angular velocity and acceleration from shaft-encoder measurements. Int. J. Robotics Research 17 (1998), 1225-1233. | DOI

[2] Brunot, M., Janot, A., Carrillo, F.: State Space Estimation Method for the Identification of an Industrial Robot Arm. In: Proc. IFAC World Congress 50 (2017) 1, pp. 9815-9820.

[3] Brunot, M., Janot, A., Carrillo, F., Garnier, H., Vandanjon, P.-O., Gautier, M.: Physical parameter identification of a one-degree-of-freedom electromechanical system operating in closed loop. In: Proc. 17th IFAC Symposium on System Identification, 2015, pp. 823-828. | DOI

[4] Coca, D., Billings, S. A.: A direct approach to identification of nonlinear differential models from discrete data. Mech. Systems Signal Process. 13(5), (1999), 739-755. | DOI

[5] Dridi, M., Scorletti, G., Smaoui, M., Tournier, D.: From theoretical differentiation methods to low-cost digital implementation. In: IEEE International Symposium on Industrial Electronics 2010, pp. 184-189. | DOI

[6] Durbin, J., Koopman, S. J.: Time Series Analysis by State Space Methods. Oxford University Press, 2012. | DOI | MR

[7] Garnier, H., Gilson, M., Young, P. C., Huselstein, E.: An optimal IV technique for identifying continuous-time transfer function model of multiple input systems. Control Engrg. Practice 15 (2007), 471-486. | DOI

[8] Garnier, H., Mensler, M., Richard, A.: Continuous-time model identification from sampled data: implementation issues and performance evaluation. Int. J. Control, 76 (2003), 1337-1357. | DOI | MR

[9] Gautier, M.: Dynamic identification of robots with power model. In: Proc. IEEE International Conference on Robotics and Automation 3 (1997), 1922-1927. | DOI

[10] Gautier, M., Janot, A., Vandanjon, P.-O.: A new closed-loop output error method for parameter identification of robot dynamics. IEEE Trans. Control Systems Technol. 21 (2013), 428-444. | DOI

[11] Gautier, M., Khalil, W.: Exciting trajectories for the identification of base inertial parameters of robots. Int. J. Robotics Research 11 (1992), 362-375. | DOI

[12] Gilson, M., Garnier, H., Young, P. C., Hof, P. M. J. Van den: An instrumental variable approach for rigid industrial robots identification. IET Control Theory Appl. 5 (2011), 1147-1154. | DOI | MR

[13] Janot, A., Vandanjon, P.-O., Gautier, M.: An instrumental variable approach for rigid industrial robots identification. Control Engrg. Practice 25 (2014), 85-101. | DOI

[14] Janot, A., Vandanjon, P.-O., Gautier, M.: A generic instrumental variable approach for industrial robot identification. IEEE Trans. Control Systems Technol. 22 (2014), 132-145. | DOI

[15] Khalil, W., Dombre, E.: Modeling, Identification and Control of Robots. Butterworth-Heinemann, 2004. | MR

[16] Mahata, K., Garnier, H.: Identification of continuous-time errors-in-variables models. Automatica 42 (2006), 1477-1490. | DOI | MR

[17] Marcassus, N., Vandanjon, P.-O., Janot, A., Gautier, M.: Minimal resolution needed for an accurate parametric identification-application to an industrial robot arm. In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems 2007, pp. 2455-2460. | DOI

[18] Norton, J. P.: Optimal smoothing in the identification of linear time-varying systems. In: Proc. of the Institution of Electrical Engineers 122 (1975), pp. 663-668. | DOI

[19] Rao, G. P., Unbehauen, H.: Identification of continuous-time systems. IEE Proc. Control Theory Appl. 153 (2006), 185-220. | DOI

[20] Söderström, T., Stoica, P.: Instrumental Variable Methods for System Identification. Springer, 1983. | DOI | MR

[21] Favergues, Stäubli: Arm - TX Series 40 Family. Stäubli, 2015.

[22] Wooldridge, J. M.: Introductory Econometrics: A Modern Approach. Fourth edition. South-Western, 2008.

[23] Young, P. C.: An instrumental variable method for real-time identification of a noisy process. Automatica, 6 (1970), 271-287. | DOI

[24] Young, P. C.: Recursive Estimation and Time-Series Analysis: An Introduction for The Student and Practitioner. Second edition. Springer Science and Business Media, 2012. | MR

[25] Young, P. C.: Refined instrumental variable estimation: Maximum likelihood optimization of a unified {Box-Jenkins} model. Automatica 52, (2015), 35-46. | DOI | MR

[26] Young, P. C., Foster, M., Lees, M.: A Direct Approach to the Identification and Estimation of Continuous-Time Systems From Discrete-Time Data Based on Fixed Interval Smoothing. In: Proc. 12th IFAC World Congress 10 (1993), pp. 27-30. | DOI

[27] Young, P. C., Jakeman, A. J.: Refined instrumental variable methods of time-series analysis: Parts I, II and III. Int. J. Control 29, 1-30; 30, 621-644, 31, (1979-1980), 741-764. | DOI