On the motion/stiffness decoupling property of articulated soft robots with application to model-free torque iterative learning control

Abstract

This article tackles the problem of controlling articulated soft robots (ASRs), i.e., robots with either fixed or variable elasticity lumped at the joints. Classic control schemes rely on high-authority feedback actions, which have the drawback of altering the desired robot softness. The problem of accurate control of ASRs, without altering their inherent stiffness, is particularly challenging because of their complex and hard-to-model nonlinear dynamics. Leveraging a learned anticipatory action, iterative learning control (ILC) strategies do not suffer from these issues. Recently, ILC was adopted to perform position control of ASRs. However, the limitation of position-based ILC in controlling variable stiffness robots is that whenever the robot stiffness profile is changed, a different input action has to be learned. Our first contribution is to identify a wide class of ASRs, whose motion and stiffness adjusting dynamics can be proved to be decoupled. This class is described by two properties that we define: strong elastic coupling, relative to motors and links of the system and their connections; and homogeneity, relative to the characteristics of the motors. Furthermore, we design a torque-based ILC scheme that, starting from a rough estimation of the system parameters, refines the torque needed for the joint positions tracking. The resulting control scheme requires minimum knowledge of the system. Experiments on variable stiffness robots prove that the method effectively generalizes the iterative procedure with respect to the desired stiffness profile and allows good tracking performance. Finally, potential restrictions of the method, e.g., caused by friction phenomena, are discussed.

Control Architecture for Human-Like Motion With Applications to Articulated Soft Robots

“…However, some systems, e.g., human musculoskeletal system, present a poly-articular structure. In Mengacci et al ( 2020 ), a few preliminary insights about the application of ILC to poly-articular systems have been discussed. Starting from these results, future work will also study the application of the proposed control architecture to poly-articular robots, achieving also a anatomical synergistic behavior.…”

“…However, future extension of this work will aim at learning the optimal impedance behavior too, imitating the human capabilities (Burdet et al, 2001 ). In Mengacci et al ( 2020 ) it is presented a method to decouple the control input to track a trajectory and the control input to regulate the robot impedance, removing the dependency between learned control input and desired stiffness profile. This, in combination with GPR, could be used to generalize the acquired control input w.r.t.…”

Editorial: Soft Robotic Modeling and Control: Bringing Together Articulated Soft Robots and Soft-Bodied Robots

“…Mengacci et al identify a class of articulated soft robots for which motion and stiffness can be regulated independently. The identified characteristics of this class are used to generalize torque profiles learned through a proposed iterative learning controller and are applied to control the stiffness and motion profiles of variable stiffness robots.…”

An Open-Source ROS-Gazebo Toolbox for Simulating Robots With Compliant Actuators

“…These variables regulate the equilibrium position of the link and its stiffness preset, respectively, and the motor positions are such that θ 1 = θ eq + θ sr and θ 2 = θ eq − θ sr . An example of this control solution is described in Mengacci et al (2020) .…”