Iterative Learning Control for Compliant Underactuated Arms

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 53; no. 6; pp. 1 - 13
• Main Authors: Pierallini, Michele, Angelini, Franco, Mengacci, Riccardo, Palleschi, Alessandro, Bicchi, Antonio, Garabini, Manolo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-06-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Arms; Control algorithms; Control theory; Feedback loops; Feedforward control; Feedforward systems; Flexible structures; Iterative learning control; iterative learning control (ILC); Machine learning; Manipulators; Modulus of elasticity; Robot arms; Robot control; robotics; Robots; Soft robotics; Stiffness; System dynamics; Tracking; Trajectory
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2023.3234403

Operations involving safe interactions in unstructured environments require robots with adapting behaviors. Compliant manipulators are a promising technology to achieve this goal. Despite that, some classical control problems such as following a trajectory are still open. A typical solution is to compensate the system dynamics with feedback loops. However, this solution increases the effective robot stiffness and jeopardizes the safety property provided by the compliant design. On the other hand, purely feedforward approaches can achieve good tracking performance while preserving the robot intrinsic compliance. However, a feedforward control framework for robots with passive elastic joints is still missing. This article presents an iterative learning control algorithm for purely feedforward trajectory tracking for compliant underactuated arms. Each arm is composed of active elastic joints and a generic number of passive ones connected through rigid links. We prove the convergence of the iterative method, also in the presence of uncertainties and bounded disturbances. Different output functions are analyzed providing conditions, based on the system inertial properties that ensure the algorithm applicability. Additionally, an automatic selection of the learning gain is proposed. Finally, we extensively validate the theoretical results with simulations and experiments.