Mechanical Design and Data-Enabled Predictive Control of a Planar Soft Robot

Soft robots offer a unique combination of flexibility, adaptability, and safety, making them well-suited for a diverse range of applications. However, the inherent complexity of soft robots poses great challenges in their modeling and control. In this letter, we present the mechanical design and data-driven control of a pneumatic-driven soft planar robot. Specifically, we employ a
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ata-
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nabl
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redictive
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ontrol (DeePC) strategy that directly utilizes system input/output data to achieve safe and optimal control, eliminating the need for tedious system identification or modeling. In addition, a dimension reduction technique is introduced into the DeePC framework, resulting in significantly enhanced computational efficiency with minimal to no degradation in control performance. Comparative experiments are conducted to validate the efficacy of DeePC in the control of the fabricated soft robot.