Velocity-Form Data-Enabled Predictive Control of Soft Robots Under Unknown External Payloads

Data-driven control methods such as data-enabled predictive control (DeePC) have shown strong potential for efficient control of soft robots without explicit parametric models. However, in object manipulation tasks, unknown external payloads and disturbances can significantly alter the system dynamics and behavior, leading to offset errors and degraded control performance. In this paper, we present a novel velocity-form DeePC framework that achieves robust and optimal control of soft robots under unknown payloads. The proposed framework leverages input/output data in an incremental representation to mitigate performance degradation induced by unknown payloads, eliminating the need for weighted datasets or disturbance estimators. We validate the method experimentally on a planar soft robot and demonstrate its improved performance compared to standard DeePC in scenarios involving unknown payloads.