Trajectory Tracking of Underwater Hexapod Robot Based on Model Predictive Control

To achieve high-precision trajectory tracking control for an underwater hexapod robot, this paper proposes a hierarchical control architecture. Firstly, a multi-rigid-body dynamic model for the robot is established based on the Newton-Euler method and reasonably simplified. Secondly, a Central Pattern Generator (CPG) network with the Hopf oscillator as its core is designed to generate stable and coordinated crawling gaits. By introducing a steering parameter, a kinematic model connecting the CPG output is constructed. Furthermore, based on this dynamic and kinematic model, an upper-layer Model Predictive Controller (MPC) is designed. The optimized control quantities output by the MPC are mapped into the rhythmic parameters of the CPG network via a transfer function established by fitting experimental data, thus forming the complete MPC-CPG controller. Finally, the proposed method is validated through simulations of circular trajectory tracking. The results show that even in the presence of initial errors, the controller can converge rapidly, with trajectory position error consistently maintained within −0.1 m~0.1 m, and heading angle error confined to the range of −15~15°. The experiments fully demonstrate the effectiveness of the proposed MPC-CPG controller in ensuring trajectory tracking accuracy, motion smoothness, and system stability.