Modeling and identification for high-performance robot control: an RRR-robotic arm case study

This paper explains a procedure for getting models of robot kinematics and dynamics that are appropriate for robot control design. The procedure consists of the following steps: 1) derivation of robot kinematic and dynamic models and establishing correctness of their structures; 2) experimental estimation of the model parameters; 3) model validation; and 4) identification of the remaining robot dynamics, not covered with the derived model. We give particular attention to the design of identification experiments and to online reconstruction of state coordinates, as these strongly influence the quality of the estimation process. The importance of correct friction modeling and the estimation of friction parameters are illuminated. The models of robot kinematics and dynamics can be used in model-based nonlinear control. The remaining dynamics cannot be ignored if high-performance robot operation with adequate robustness is required. The complete procedure is demonstrated for a direct-drive robotic arm with three rotational joints.

[1]  Maarten Steinbuch,et al.  Closed-form kinematic and dynamic models of an industrial-like RRR robot , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[2]  M. Steinbuch,et al.  Control design for robust performance of a direct-drive robot , 2003, Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003..

[3]  P. R. Bélanger,et al.  Estimation of Angular Velocity and Acceleration from Shaft-Encoder Measurements , 1998, Int. J. Robotics Res..

[4]  Wisama Khalil,et al.  On the identification of the inertial parameters of robots , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.

[5]  Arjan van der Schaft,et al.  Non-linear dynamical control systems , 1990 .

[6]  Seung-Jean Kim,et al.  A frequency-domain approach to identification of mechanical systems with friction , 2001, IEEE Trans. Autom. Control..

[7]  Jan Swevers,et al.  Modification of the Leuven integrated friction model structure , 2002, IEEE Trans. Autom. Control..

[8]  Carlos Canudas de Wit,et al.  Friction Models and Friction Compensation , 1998, Eur. J. Control.

[9]  B. de Jager,et al.  Modeling and identification of an RRR-robot , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[10]  Ronald H. Brown,et al.  Analysis of algorithms for velocity estimation from discrete position versus time data , 1992, IEEE Trans. Ind. Electron..

[11]  A. G. de Jager,et al.  Grey-box modeling of friction: An experimental case-study , 1999 .

[12]  H. Harry Asada,et al.  Direct-Drive Robots: Theory and Practice , 1987 .

[13]  Spyros G. Tzafestas,et al.  The handwriting problem [man-machine motion analogy in robotics] , 2003, IEEE Robotics Autom. Mag..

[14]  Maarten Steinbuch,et al.  Frequency domain identification of dynamic friction model parameters , 2002, IEEE Trans. Control. Syst. Technol..

[15]  Mark W. Spong,et al.  Robotica: a Mathematica package for robot analysis , 1994, IEEE Robotics & Automation Magazine.

[16]  Veljko Potkonjak,et al.  Redundancy problem in writing: from human to anthropomorphic robot arm , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[17]  Bruno Siciliano,et al.  Modeling and Control of Robot Manipulators , 1995 .

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

[19]  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).

[20]  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.

[21]  Giuseppe Carlo Calafiore,et al.  Robot Dynamic Calibration: Optimal Excitation Trajectories and Experimental Parameter Estimation , 2001 .

[22]  John M. Hollerbach.  UNDERSTANDING MANIPULATOR CONTROL BY SYNTHESIZING HUMAN HANDWRITING. , 1979 .

[23]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[24]  Jan Swevers,et al.  An integrated friction model structure with improved presliding behavior for accurate friction compensation , 1998, IEEE Trans. Autom. Control..

[25]  Peter I. Corke,et al.  A robotics toolbox for MATLAB , 1996, IEEE Robotics Autom. Mag..

[26]  Dragan Kostic,et al.  Illustrating man-machine motion analogy in robotics - The handwriting problem , 2003 .

[27]  B. de Jager,et al.  RRR-robot design: basic outlines, servo sizing, and control , 1997, Proceedings of the 1997 IEEE International Conference on Control Applications.

[28]  Gene H. Golub,et al.  Matrix computations , 1983 .

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

[30]  Carlos Canudas de Wit,et al.  Adaptive friction compensation with partially known dynamic friction model , 1997 .

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

[32]  Shir-Kuan Lin,et al.  Minimal linear combinations of the inertia parameters of a manipulator , 1995, IEEE Trans. Robotics Autom..

[33]  Maarten Steinbuch,et al.  Frequency domain iterative learning control for direct-drive robots , 2003, 2003 European Control Conference (ECC).

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

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

[36]  M. Vukobratovic,et al.  Dynamics of Manipulation Robots: Theory and Application , 1982 .

[37]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

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

[39]  J. Hollerbach.  An oscillation theory of handwriting , 2004, Biological Cybernetics.

[40]  A. De Luca.  Feedforward/feedback laws for the control of flexible robots , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

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

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

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

[44]  Carlos Canudas de Wit,et al.  A survey of models, analysis tools and compensation methods for the control of machines with friction , 1994, Autom..

[45]  Krzysztof Kozlowski,et al.  Experimental Identification of Robot and Load Dynamics , 1996 .

[46]  Veljko Potkonjak,et al.  Dynamics of Manipulation Robots , 1982 .

[47]  H. Nijmeijer,et al.  Robust control of robots via linear estimated state feedback , 1994, IEEE Trans. Autom. Control..

[48]  G. Goodwin,et al.  Improving the tracking performance of mechanical systems by adaptive extended friction compensation , 2006 .