A Tension-Based Motion Controller for an SMA Soft Manipulator Using Shape-Based Force Estimation

Shape memory alloy (SMA) driven soft manipulators (SDSMs), characterized by great system integration and lightweight structure, present significant application potential in confined working environments. However, the compact structure poses an obstacle to the integration of effective force-sensing systems, limiting the development of advanced control methods considering the constraints of the tension of the SMAs. In this article, the kinematic and static models of an SDSM were established to estimate the external force only using the self-sensing shape information. Simulations and experimental results validate the feasibility of the models and the force estimation method, demonstrating accurate external load perception under different bending states. Then, a motion-planning algorithm considering static constraints was proposed to solve both the inverse kinematics of the SDSM and the tensions of the SMAs. Finally, a tension-based motion controller of the SDSM was built based on the calculated tensions and state-space equations of the SMAs. A root mean square error of 0.0145 in bending angle tracking and a tip position tracking repeatability of less than 0.4 mm underscore the effectiveness of the proposed motion controller. This work shows significant implications for enhancing the environmental interaction capabilities of SDSMs and will further broaden their application prospects.