Bioinspired Central Pattern Generator and T-S Fuzzy Neural Network-Based Control of a Robotic Manta for Depth and Heading Tracking

Aiming at the difficult problem of motion control robotic manta with pectoral fin flexible deformation, this paper proposes a scheme that combines bioinspired Central Pattern Generator (CPG) and T-S Fuzzy neural network (NN)-based control. An improved CPG drive is presented for multi-stage structure manta. Considering unknown dynamics external environmental disturbances, sensor-based classic NN controller designed heading depth Finally, pool test demonstrates effectiveness robustness proposed controller: can track an error ±6 cm ±6°, satisfying accuracy requirements.