Robust state dependent Riccati equation variable impedance control for robotic force-tracking tasks
- PDF / 2,110,016 Bytes
- 13 Pages / 595.276 x 790.866 pts Page_size
- 59 Downloads / 194 Views
REGULAR PAPER
Robust state dependent Riccati equation variable impedance control for robotic force‑tracking tasks Loris Roveda1 · Dario Piga1 Received: 26 August 2020 / Accepted: 19 October 2020 / Published online: 15 November 2020 © The Author(s) 2020
Abstract Industrial robots are increasingly used in highly flexible interaction tasks, where the intrinsic variability makes difficult to pre-program the manipulator for all the different scenarios. In such applications, interaction environments are commonly (partially) unknown to the robot, requiring the implemented controllers to take in charge for the stability of the interaction. While standard controllers are sensor-based, there is a growing need to make sensorless robots (i.e., most of the commercial robots are not equipped with force/torque sensors) able to sense the environment, properly reacting to the established interaction. This paper proposes a new methodology to sensorless force control manipulators. On the basis of sensorless Cartesian impedance control, an Extended Kalman Filter (EKF) is designed to estimate the interaction exchanged between the robot and the environment. Such an estimation is then used in order to close a robust high-performance force loop, designed exploiting a variable impedance control and a State Dependent Riccati Equation (SDRE) force controller. The described approach has been validated in simulations. A Franka EMIKA panda robot has been considered as a test platform. A probing task involving different materials (i.e., with different stiffness properties) has been considered to show the capabilities of the developed EKF (able to converge with limited errors) and controller (preserving stability and avoiding overshoots). The proposed controller has been compared with an LQR controller to show its improved performance. Keywords Sensorless force control · SDRE control · Interaction force estimation · Extended Kalman Filter · Variable impedance control · Industrial robots
1 Introduction 1.1 Context Robots are nowadays involved in various tasks and domains Ben-Ari and Mondada (2018), Yang et al. (2018). Due to the high variability of the operative conditions and target tasks, it is not possible to pre-program all the possible situations. Therefore, the robot is required to learn/adapt itself to the target perceived context Dattaprasad and Rao (2018), embedding intelligence for decision-making purposes. Within the * Loris Roveda [email protected] Dario Piga [email protected] 1
Istituto Dalle Molle di studi sull’Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI), 6928 Manno, Switzerland
manufacturing scenario, robots have to improve flexibility (i.e., adapting to new production) while guaranteeing target performance Polverini et al. (2016a), Mohamed (2018). Taking into account interaction tasks (i.e., robot physically in contact with an external environment), the capability to adapt to new situations becomes critical Hogan (1984
Data Loading...
