24 August 2023
Continuum soft robots are made of continuously deformable elements and are inspired by invertebrate animals. With their peculiar characteristics, they are expected to execute tasks that are currently not achievable for standard rigid robots—for example, interacting with uncertain environments, possibly involving humans. Nevertheless, the soft robot's highly deformable nature that makes these tasks possible also makes their control challenging. As a result, soft robot motor intelligence is still minimal today.
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.
In this work, EMERGE partners from Delft University of Technology and collaborators aim 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.
Source: X. Shao et al., "Model-Based Control for Soft Robots With System Uncertainties and Input Saturation," in IEEE Transactions on Industrial Electronics, vol. 71, no. 7, pp. 7435-7444, July 2024, doi: 10.1109/TIE.2023.3303636.
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