An adaptive disturbance compensation method for force-sensorless control systems applied to robotic milling

Abstract

This paper introduces a novel disturbance compensation method for a robust, active-force-controlled end-effector. This system integrates a disturbance observer (DOB) and a reaction force observer (RFOB) while employing a numerical optimization-based extremum-seeking algorithm. Conventional force/torque sensors, when employed in serially connected manipulator systems, often trigger unstable responses due to the presence of non-collocated modes. Furthermore, conventional reaction force observers may fail to accurately estimate the contact force between the robot and its environment when external disturbance terms are not perfectly modeled a priori. In response to these challenges, recent research has reintegrated force/torque sensors into the control architecture, employing filter-based sensor fusion techniques to mitigate disturbance effects. However, these approaches fail to address the inherent stability challenges caused by the mounting and serial connection of force/torque sensors within the robot manipulator system, which in turn increases the design complexity of reaction-force-observer-based force control systems. To overcome these limitations, this paper proposes adaptive disturbance compensation methods that leverage position feedback information related to the depth of cut in milling processes. The proposed method adaptively compensates for time-varying disturbances, such as tool wear and abrupt feed rate changes, ensuring consistent performance under dynamic conditions. Compared to conventional position-controlled and force-controlled methods, the proposed approach exhibits improved robustness, precision, and versatility, positioning it as a promising solution for advancing robotic milling technologies toward practical industrial applications.