基于模型预测—中枢模式发生器的六足机器人轨迹跟踪控制

为了模仿动物卓越的运动能力和环境适应能力，提出了六足仿生机器人的轨迹跟踪控制方法。首先建立了机器人的运动学模型，接着通过转向参数将机器人的速度和角速度与中枢模式发生器（CPG）参数结合起来，设计了转换函数。然后通过转换函数将模型预测控制器和CPG网络结合起来，提出了基于CPG的模型预测控制器（MPC-CPG），并证明了其稳定性。最后对机器人跟踪圆周轨迹和直线轨迹进行了仿真和实验。实验表明，在有初始误差的条件下，机器人在MPC-CPG控制器的作用下能够快速地消除位置误差和航向角误差，跟踪上参考轨迹。轨迹跟踪的位置误差始终保持在-0.1～0.1 m，航向角误差保持在-27?～20?。在MPC-CPG控制器的作用下，机器人不仅具有较高的轨迹跟踪精度，同时还表现出良好的运动平滑性和协调性，进一步验证了所提出的MPC-CPG控制器的有效性。

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.