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

Automated summary

This paper proposes a control scheme combining the bioinspired Central Pattern Generator (CPG) and T-S Fuzzy neural network (NN)-based control to address the difficult problem of motion control of a robotic manta with pectoral fin flexible deformation. The improved CPG drive network is applied to the multi-stage fin structure, and a sensor-based classic T-S Fuzzy NN controller is designed for heading and depth control. The effectiveness and robustness of the proposed controller are demonstrated through a pool test.