Trajectory Tracking Control of Hexapod Robot Based on Model Prediction and Central Pattern Generator

In order to imitate animals in their excellent athletic ability and adaptability to environments, a trajectory tracking control method is proposed for the bionic hexapod robot. Firstly, the kinematics model of the robot is established, then the velocity and the angular velocity of the robot are combined with the CPG (central pattern generator) parameters by the steering parameter, and the transfer function is designed. Then the model predictive controller and the CPG network are combined through the transfer function, a CPG-based model predictive controller (MPC-CPG) is proposed, and its stability is proved. Finally, simulations and experiments are carried out for the robot to track the circular trajectory and the linear trajectory. Experiments show that under the condition of initial error, the robot can quickly eliminate the position error and the heading angle error by the MPC-CPG controller, and track the reference trajectory. The position error in trajectory tracking is always kept in -0.1~0.1 m, and the heading angle error is kept in -27°~20°. With the MPC-CPG controller, the robot not only has a high trajectory tracking accuracy, but also shows good motion smoothness and coordination, which further verifies the effectiveness of the proposed MPC-CPG controller.