Sarcouncil Journal of Applied Sciences Aims & Scope

Abstract: This study presents a comprehensive framework for the closed-loop system identification and control of a 3D-printed soft robotic actuator using MATLAB and vision-based feedback. The research aimed to develop an accurate and adaptable control mechanism capable of handling the nonlinear deformation behavior of soft robots fabricated through additive manufacturing. The soft actuator, constructed from flexible thermoplastic polyurethane (TPU), was subjected to controlled pneumatic inputs, and its dynamic response was captured using a high-resolution vision system. MATLAB’s System Identification Toolbox was employed to derive data-driven models that effectively represented the robot’s input-output behavior, while frequency-domain analyses using Bode and Nyquist plots confirmed model accuracy and stability. A Proportional-Integral-Derivative (PID) controller integrated with vision feedback was implemented in a closed-loop configuration to ensure real-time trajectory correction. Experimental results demonstrated a substantial improvement in motion tracking precision, with reduced overshoot and a 70% decrease in steady-state error compared to open-loop performance. The study highlights the effectiveness of combining system identification with vision-based control for soft robotic systems, providing a cost-efficient, flexible, and robust solution for dynamic modeling and feedback control. This approach contributes significantly to the advancement of intelligent soft robotics, offering promising applications in precision manipulation, biomedical devices, and human-robot interaction environments.