Adaptive Generalized Predictive Controller and Cartesian Force Control for Robot Arm Using Dynamics and Geometric Identification
Shohei Hagane*1, Liz Katherine Rincon Ardila*1, Takuma Katsumata*2, Vincent Bonnet*3, Philippe Fraisse*4, and Gentiane Venture*1
*1Tokyo University of Agriculture and Technology
2-24-16 Nakacho, Koganei City, Tokyo 184-8588, Japan
*2Yahoo Japan Corporation
Kioi Tower, Tokyo Garden Terrace Kioicho, 1-3 Kioicho, Chiyoda-ku, Tokyo 102-8282, Japan
*3Electrical Engineering and Robotics, University of Paris-Est Créteil
61 Avenue Général de Gaulle, 94000 Créteil, France
*4Université de Montpellier
LIRMM, 161 Rue ADA, 34095 Montpellier, France
In realistic situations such as human-robot interactions or contact tasks, robots must have the capacity to adapt accordingly to their environment, other processes and systems. Adaptive model based controllers, that requires accurate dynamic and geometric robot’s information, can be used. Accurate estimations of the inertial and geometric parameters of the robot and end-effector are essential for the controller to demonstrate a high performance. However, the identification of these parameters can be time-consuming and complex. Thus, in this paper, a framework based on an adaptive predictive control scheme and a fast dynamic and geometric identification process is proposed. This approach was demonstrated using a KUKA lightweight robot (LWR) in the performance of a force-controlled wall-painting task. In this study, the performances of a generalized predictive control (GPC), adaptive proportional derivative gravity compensation, and adaptive GPC (AGPC) were compared. The results revealed that predictive controllers are more suitable than adaptive PD controllers with gravitational compensation, owing to the use of well-identified geometric and inertial parameters.
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