Decentralized Trajectory Tracking Control for Soft Robots Interacting With the Environment

Abstract

Despite the classic nature of the problem, trajectory tracking for soft robots, i.e., robots with compliant elements deliberately introduced in their design, still presents several challenges. One of these is to design controllers which can obtain sufficiently high performance while preserving the physical characteristics intrinsic to soft robots. Indeed, classic control schemes using highgain feedback actions fundamentally alter the natural compliance of soft robots effectively stiffening them, thus de facto defeating their main design purpose. As an alternative approach, we consider here using a low-gain feedback, while exploiting feedforward components. In order to cope with the complexity and uncertainty of the dynamics, we adopt a decentralized, iteratively learned feedforward action, combined with a locally optimal feedback control. The relative authority of the feedback and feedforward control actions adapts with the degree of uncertainty of the learned component. The effectiveness of the method is experimentally verified on several robotic structures and working conditions, including unexpected interactions with the environment, where preservation of softness is critical for safety and robustness.

Modeling Previous Trial Effect in Human Manipulation through Iterative Learning Control

“…The results presented in this work open promising perspective in robot control, e.g., to devise control laws in autonomous robotic manipulation of external environment as preliminary depicted in refs. [29,39]. We believe that including the modeling of human behavior could be a key step forward for a new generation of robotic manipulators successfully and autonomously interacting with the external world, in a way similar to the human one and taking advantage from sensory-motor memory collected during task execution.…”

SoftHandler: An Integrated Soft Robotic System for Handling Heterogeneous Objects

“…This technology allows either soft behavior, when robustness and adaptivity are preferred, or rigid behavior, when accuracy and precision are needed. In [10], we employed an early proof-ofconcept of a VSA delta robot as an experimental platform to validate a specific control algorithm. However, no description of its design or validation of its performance was reported.…”

“…Moreover, model-based feedback techniques usually replace the system's physical dynamics with a desired one, nullifying the advantages obtained by embedding physical compliance in the robot. This phenomenon is even more pronounced in model-free, high-gain feedback methods, which have the drawback of increasing joint stiffness, hindering the mechanical impedance of the system [10].…”

“…In [10], a feedforward control method based on an ILC approach is presented. This solution merges a low-gain feedback component and an iteratively refined feedforward term.…”

Observer-Based Decentralized Tracking Control with Preview Action for a Class of Nonlinear Interconnected Systems

“…Remark 6. So far, there have been many interesting results concerning decentralized tracking control in the literature, such as [5,9,12,39,[42][43][44][45][46][47]. Note that most of them are developed via backstepping control, adaptive dynamic programming, iterative learning control, etc.…”