Advanced Control Strategy for Soft Robotic Manipulators Using Data-Driven Dynamics and Predictive Optimization

Abstract

This research presents a novel control approach for soft robotic manipulators by leveraging a data-driven dynamics model and predictive optimization strategy. By utilizing a finite-dimensional approximation of nonlinear system behavior through advanced mathematical transformations, the controller enhances the stability and accuracy of the manipulator’s motion. The integration of
predictive control techniques allows real-time trajectory adjustments, ensuring precise handling and minimizing errors in dynamic environments. The proposed method demonstrates improved performance in tracking complex motion patterns and provides scalable solution for soft robotics applications in industrial and biomedical settings.