Hybrid Vision/Magnetic-Force Finite-Time Convergent Neural Network Tracking Control of Electromagnetically Actuated Soft-Tethered Colonoscope Robot With Current Constraints

To solve the problems of discomfort and potential colon perforations of patients that arise when standard colonoscopes are used for colonoscopy, an electromagnetically actuated soft-tethered colonoscope robot (EASCR) is here introduced. Owing to EASCRs' highly nonlinear and complex application environments, the hybrid vision/magnetic-force tracking control for these types of robots remains a challenging research issue, and the lack of current constraints may also give rise to safety concerns. Therefore, a hybrid vision/magnetic-force fast convergent dual neural network (DNN) tracking controller for an EASCR with current constraints is developed to alleviate patient discomfort and ensure the safe and smooth progression of colonoscopy. First, EASCR motion/vision and electromagnetically actuated force nonlinear coupling models are established, and a quadratic programming visual servo-tracking control scheme with current constraints is designed. Second, a novel DNN solver for the nonlinear control scheme is developed, and its convergence in finite time is strictly proved. The results of simulations and experiments indicate that the designed control method can well control EASCRs with current constraints to achieve tracking tasks, and it has a stronger anti-disturbance ability, faster convergence, and higher convergence accuracy than existing methods.