Many control problems are naturally expressed in continuous time. Yet, in Iterative Learning Control of linear systems, sampling the output signal has proven to be a convenient strategy to simplify the learning process while sacrificing only marginally the overall performance. In this context, the control action is similarly discretized through zero-order hold - thus leading to a discrete-time system. With this paper, we want to investigate an alternative strategy, which is to track sampled outputs without masking the continuous nature of the input. Instead, we look at the whole input evolution as an element of a functional subspace. We show how standard results in linear Iterative Learning Control naturally extend to this context. As a result, we can leverage the infinite-dimensional nature of functional spaces to achieve exact tracking of strongly non-square systems (number of inputs less than outputs). We also show that constraints - like those imposed by intermittent control - can be naturally integrated within this framework.

%B 2021 60th IEEE Conference on Decision and Control (CDC) %I IEEE %P 5858–5863 %U https://ieeexplore.ieee.org/document/9683673 %R 10.1109/CDC45484.2021.9683673 %0 Journal Article %J Frontiers in Robotics and AI %D 2020 %T Control architecture for human-like motion with applications to articulated soft robots %A Angelini, Franco %A Della Santina, Cosimo %A Garabini, Manolo %A Bianchi, Matteo %A Bicchi, Antonio %B Frontiers in Robotics and AI %V 7 %G eng %0 Conference Paper %B Conference on Biomimetic and Biohybrid Systems %D 2020 %T Iterative learning control as a framework for human-inspired control with bio-mimetic actuators %A Angelini, Franco %A Bianchi, Matteo %A Garabini, Manolo %A Bicchi, Antonio %A Della Santina, Cosimo %B Conference on Biomimetic and Biohybrid Systems %I Springer %P 12–16 %0 Journal Article %J IEEE Robotics and Automation Letters %D 2020 %T Time generalization of trajectories learned on articulated soft robots %A Angelini, Franco %A Mengacci, Riccardo %A Della Santina, Cosimo %A Catalano, Manuel G %A Garabini, Manolo %A Bicchi, Antonio %A Grioli, Giorgio %B IEEE Robotics and Automation Letters %V 5 %P 3493–3500 %G eng