Model-based strategies are a promising solution to the grand challenge of equipping continuum soft robots with motor intelligence. However, finite-dimensional models of these systems are inherently inaccurate, thus posing pressing robustness concerns. Moreover, the actuation space of soft robots is usually limited. This article aims at solving both these challenges by proposing a robust model-based strategy for the shape control of soft robots with system uncertainty and input saturation. The proposed architecture is composed of two key components. First, we propose an observer that estimates deviations between the theoretical model and the soft robot, ensuring that the estimation error converges to zero within finite time. Second, we introduce a sliding mode controller to regulate the soft robot shape while fulfilling saturation constraints. This controller uses the observer’s output to compensate for the deviations between the real system and the established model. We prove the convergence of the closed-loop with theoretical analysis and the method’s effectiveness with simulations and experiments.

Model-Based Control for Soft Robots With System Uncertainties and Input Saturation / Shao, Xiangyu; Pustina, Pietro; Stölzle, Maximilian; Sun, Guanghui; De Luca, Alessandro; Wu, Ligang; Santina, Cosimo Della. - In: IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS. - ISSN 0278-0046. - (2023), pp. 1-10. [10.1109/TIE.2023.3303636]

Model-Based Control for Soft Robots With System Uncertainties and Input Saturation

Pustina, Pietro
;
De Luca, Alessandro
;
2023

Abstract

Model-based strategies are a promising solution to the grand challenge of equipping continuum soft robots with motor intelligence. However, finite-dimensional models of these systems are inherently inaccurate, thus posing pressing robustness concerns. Moreover, the actuation space of soft robots is usually limited. This article aims at solving both these challenges by proposing a robust model-based strategy for the shape control of soft robots with system uncertainty and input saturation. The proposed architecture is composed of two key components. First, we propose an observer that estimates deviations between the theoretical model and the soft robot, ensuring that the estimation error converges to zero within finite time. Second, we introduce a sliding mode controller to regulate the soft robot shape while fulfilling saturation constraints. This controller uses the observer’s output to compensate for the deviations between the real system and the established model. We prove the convergence of the closed-loop with theoretical analysis and the method’s effectiveness with simulations and experiments.
2023
disturbance observer; input saturation; model-based control; sliding mode control; soft robots
01 Pubblicazione su rivista::01a Articolo in rivista
Model-Based Control for Soft Robots With System Uncertainties and Input Saturation / Shao, Xiangyu; Pustina, Pietro; Stölzle, Maximilian; Sun, Guanghui; De Luca, Alessandro; Wu, Ligang; Santina, Cosimo Della. - In: IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS. - ISSN 0278-0046. - (2023), pp. 1-10. [10.1109/TIE.2023.3303636]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1688791
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