State space estimation method for the identification of an industrial robot arm

In this paper, we study the identification of industrial robot dynamic models. Since the models are linear with respect to the parameters, the usual identification technique is based on the Least-Squares method. That requires a careful preprocessing of the data to obtain consistent estimates. In this paper, we carefully detail this process and propose a new procedure based on Kalman filtering and fixed interval smoothing. This new technique is compared to usual one with experimental data considering an industrial robot arm. The obtained results show that the proposed technique is a credible alternative, especially if the system bandwidth is unknown.

[1]  P. Young,et al.  An optimal IV technique for identifying continuous-time transfer function model of multiple input systems , 2007 .

[2]  Paul M.J. Van den Hof,et al.  Closed-Loop Issues in System Identification , 1997 .

[3]  G. Scorletti,et al.  From theoretical differentiation methods to low-cost digital implementation , 2010, 2010 IEEE International Symposium on Industrial Electronics.

[4]  Diego J. Pedregal,et al.  Environmental time series analysis and forecasting with the Captain toolbox , 2007, Environ. Model. Softw..

[5]  H. Unbehauen,et al.  Identification of continuous-time systems , 1991 .

[6]  J. R. Trapero,et al.  Recursive Estimation and Time-Series Analysis. An Introduction for the Student and Practitioner, Second edition, Peter C. Young. Springer (2011), 504 pp., Hardcover, $119.00, ISBN: 978-3-642-21980-1 , 2015 .

[7]  Maxime Gautier,et al.  A New Closed-Loop Output Error Method for Parameter Identification of Robot Dynamics , 2010, IEEE Transactions on Control Systems Technology.

[8]  Maxime Gautier.  Dynamic identification of robots with power model , 1997, Proceedings of International Conference on Robotics and Automation.

[9]  Siem Jan Koopman,et al.  Time Series Analysis by State Space Methods , 2001 .

[10]  Maxime Gautier,et al.  State Space Estimation Method for Robot Identification , 2016 .

[11]  Maxime Gautier,et al.  A Generic Instrumental Variable Approach for Industrial Robot Identification , 2014, IEEE Transactions on Control Systems Technology.

[12]  Wisama Khalil,et al.  Modeling, Identification and Control of Robots , 2003 .

[13]  Hugues Garnier,et al.  Identification of continuous-time errors-in-variables models , 2006, Autom..

[14]  P. Young,et al.  Stochastic, Dynamic Modelling and Signal Processing: Time Variable and State Dependent Parameter Estimation , 2000 .

[15]  J. Norton.  Optimal smoothing in the identification of linear time-varying systems , 1975 .

[16]  Hugues Garnier,et al.  Continuous-time model identification from sampled data: Implementation issues and performance evaluation , 2003 .