期刊
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
卷 28, 期 4, 页码 1258-1271出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCST.2019.2908899
关键词
Force; Force control; Robot sensing systems; Service robots; End effectors; Robot kinematics; Data-driven control; force control; model predictive control; robot control
This paper presents a hierarchical data-driven/ model-based control architecture to robot implicit force control aiming at enhancing closed-loop performance. An inner data-driven controller, based on a recursive implementation of virtual reference feedback tuning, and relying on a model-based description of the robot compliance, prevents the deterioration of the force controller performance associated with environment modeling and identification. Then, an outer model predictive controller (MPC) acting as a reference governor selects on-line the optimal reference fed to the inner virtual reference feedback tuning closed-loop system in order to improve the closed-loop performance. The linear-time-invariant (LTI) formulation of the MPC allows to explicitly obtain the MPC control law and further demonstrate closed-loop stability. The effectiveness of the proposed control strategy is experimentally validated on force regulation experiments performed on different environment materials with a six-degree-of-freedom industrial robot equipped with a force/torque sensor.
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