Design and Validation of a Fuzzy Logic Controller for Multisection Continuum Robots

The rise of multisection continuum robots (CRs) has captivated researchers and practitioners across diverse industries and medical fields. Researchers have devised a spectrum of model-based and learning-based strategies to conquer the modeling problem and elevate control performance. Despite the advancements in these approaches, they encounter challenges stemming from their complex design and intricate learning processes. This article introduces a simply structured, model-free fuzzy logic controller (FLC) for the closed-loop control of continuum robots. This controller boasts a built-in shape classification algorithm. This algorithm allows it to achieve robust control using only the feedback of end position and orientation, significantly reducing sensor dependence. It efficiently adapts to various nonlinearities such as hysteresis, cable elongation, and unexpected external disturbances. The experimental results conclusively demonstrate the accuracy and robustness of the proposed FLC. On a three-section, six-degree-of-freedom continuum robot, it achieved a minuscule trajectory tracking root mean square error from 0.31 to 1.00 mm, representing just 0.19% to 0.60% of the robot’s length. Additionally, the controller demonstrates robustness by successfully handling unexpected external disturbances during the trajectory tracking.