Motion control is essential for all autonomous mobile robots, and even more so for spherical robots. Due to the uniqueness of the spherical robot, its motion control must not only ensure accurate tracking of the target commands, but also minimize fluctuations in the robot's attitude and motors' current while tracking. In this paper, model predictive control (MPC) is applied to the control of spherical robots and an MPC-based motion control framework is designed. There are two controllers in the framework, an optimal velocity controller ESO-MPC which combines extend states observers (ESO) and MPC, and an optimal orientation controller that uses multilayer perceptron (MLP) to generate accurate trajectories and MPC with changing weights to achieve optimal control. Finally, the performance of individual controllers and the whole control framework are verified by physical experiments. The experimental results show that the MPC-based motion control framework proposed in this work is much better than PID in terms of rapidity and accuracy, and has great advantages over sliding mode controller (SMC) for overshoot, attitude stability, current stability and energy consumption.
翻译:由于球体机器人的独特性,其运动控制不仅必须确保准确跟踪目标命令,而且必须最大限度地减少机器人在跟踪时的态度和运动流的波动。在本文中,模型预测控制(MPC)应用于控制球体机器人,并设计了基于MPC的运动控制框架。框架中有两个控制器,一个最佳速度控制器ESO-MPC,它将扩展国家观察器(ESO)和MPC结合起来,另一个最佳方向控制器,它使用多层透视器(MLP)生成精确的轨迹和移动控制器,并改变重量以实现最佳控制。最后,个人控制器的性能和整个控制框架通过物理实验得到验证。实验结果表明,在这项工作中提议的基于MPC的运动控制框架在速度和准确性方面比PID要好得多,而且对于超标、姿态稳定性、当前稳定性和能源消耗而言,对滑动模式控制器(SMC)的优势很大。