@article {3326, title = {Decentralized Trajectory Tracking Control for Soft Robots Interacting with the Environment}, journal = {IEEE Transactions on Robotics (T-RO).}, volume = {Early Access}, year = {2018}, month = {06/2018}, 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 high gain 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 to use 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.
}, keywords = {Robotics}, author = {F. Angelini and C. Della Santina and M. Garabini and M. Bianchi and G M Gasparri and G. Grioli and M. G. Catalano and A. Bicchi} }