Online identification of a robot using batch adaptive control

Abstract A technique to identify parameters of a robot dynamic model is presented in this paper. It is based on a batch adaptive control algorithm that, using a model of the robot dynamics, realizes a repetitive robot trajectory. The tracking error decreases due to a feedforward control input generated from the dynamic model. This feedforward input is computed after adaptation of the model parameters at the end of each trial. As the algorithm is effective, even if the model parameters are all initially set to zero, it can be used to recover their physical values. For that purpose, an identification experiment is carried out during which the robot is excited persistently. The estimation technique admits an online implementation without a delay between trials and is quite appealing for use in practice. Its merits are experimentally demonstrated on a spatial direct-drive robotic manipulator with 3 rotational joints.

[1]  B. de Jager,et al.  RRR-robot design: basic outlines, servo sizing, and control , 1997 .

[2]  B. van Beek,et al.  An experimental facility for nonlinear robot control , 1999 .

[3]  Laura E. Ray,et al.  Adaptive friction compensation using extended Kalman–Bucy filter friction estimation , 2001 .

[4]  M. Gautier,et al.  Exciting Trajectories for the Identification of Base Inertial Parameters of Robots , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[5]  Suguru Arimoto.  Control Theory of Nonlinear Mechanical Systems , 1996 .

[6]  Jan Swevers,et al.  Optimal robot excitation and identification , 1997, IEEE Trans. Robotics Autom..

[7]  Suguru Arimoto,et al.  Control Theory of Nonlinear Mechanical Systems , 1996 .

[8]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[9]  Koichi Osuka,et al.  Base parameters of manipulator dynamic models , 1990, IEEE Trans. Robotics Autom..

[10]  B. De Jager,et al.  An experimental facility for nonlinear robot control , 1999, Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328).

[11]  Maarten Steinbuch,et al.  Modeling and identification for high-performance robot control: an RRR-robotic arm case study , 2004, IEEE Transactions on Control Systems Technology.

[12]  Giuseppe Carlo Calafiore,et al.  Robot dynamic calibration: Optimal excitation trajectories and experimental parameter estimation , 2001, J. Field Robotics.

[13]  B. de Jager,et al.  Experimentally supported control design for a direct drive robot , 2002, Proceedings of the International Conference on Control Applications.

[14]  M. Gautier,et al.  Exciting Trajectories for the Identification of Base Inertial Parameters of Robots , 1992 .

[15]  Brian Armstrong,et al.  On Finding Exciting Trajectories for Identification Experiments Involving Systems with Nonlinear Dynamics , 1989, Int. J. Robotics Res..

[16]  Spyros G. Tzafestas,et al.  Concerning the primary and secondary objectives in robot task definition — the “learn from humans” principle , 2000 .

[17]  Krzysztof Kozłowski,et al.  Modelling and Identification in Robotics , 1998 .

[18]  C. S. G. Lee,et al.  Robotics: Control, Sensing, Vision, and Intelligence , 1987 .

[19]  Toshiharu Sugie,et al.  Iterative learning control for robot manipulators using the finite dimensional input subspace , 2002, IEEE Trans. Robotics Autom..

[20]  Henrik Gordon Petersen,et al.  A new method for estimating parameters of a dynamic robot model , 2001, IEEE Trans. Robotics Autom..

[21]  Koichi Osuka,et al.  Base parameters of manipulator dynamic models , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.