02168nas a2200217 4500008004100000245009500041210006900136260001200205490001700217520144600234653001301680100001701693700001801710700001701728700001601745700001801761700001501779700002001794700001501814856012101829 2018 eng d00aDecentralized Trajectory Tracking Control for Soft Robots Interacting with the Environment0 aDecentralized Trajectory Tracking Control for Soft Robots Intera c06/20180 vEarly Access3 a

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.

10aRobotics1 aAngelini, F.1 aDella Santina1 aGarabini, M.1 aBianchi, M.1 aGasparri, G M1 aGrioli, G.1 aCatalano, M. G.1 aBicchi, A. uhttp://95.110.227.47/publications/decentralized-trajectory-tracking-control-soft-robots-interacting-environment.html02174nas a2200229 4500008004100000022001400041245007300055210006900128260001200197300001200209490000700221520152100228653001301749100001801762700001601780700001501796700001701811700002001828700001701848700001501865856006401880 2017 eng d a1070-993200aControlling Soft Robots: Balancing Feedback and Feedforward Elements0 aControlling Soft Robots Balancing Feedback and Feedforward Eleme c05/2017 a75 - 830 v243 a

Soft robots (SRs) represent one of the most significant recent evolutions in robotics. Designed to embody safe and natural behaviors, they rely on compliant physical structures purposefully designed to embody desirable and sometimes variable impedance characteristics. This article discusses the problem of controlling SRs. We start by observing that most of the standard methods of robotic control—e.g., high-gain robust control, feedback linearization, backstepping, and active impedance control—effectively fight against or even completely cancel the physical dynamics of the system, replacing them with a desired model. This defeats the purpose of introducing physical compliance. After all, what is the point of building soft actuators if we then make them stiff by control? An alternative to such approaches can be conceived by observing humans, who can obtain good motion accuracy and repeatability while maintaining the intrinsic softness of their bodies. In this article, we show that an anticipative model of human motor control, using a feedforward action combined with low-gain feedback, can be used to achieve human-like behavior. We present an implementation of such an idea that uses iterative learning control. Finally, we present the experimental results of the application of such learned anticipative control to a physically compliant robot. The control application achieves the desired behavior much better than a classical feedback controller used for comparison.

10aRobotics1 aDella Santina1 aBianchi, M.1 aGrioli, G.1 aAngelini, F.1 aCatalano, M. G.1 aGarabini, M.1 aBicchi, A. uhttp://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7930438