基于MPC的摆驱动球形机器人最优运动控制框架
An MPC-based Optimal Motion Control Framework for Pendulum-driven Spherical Robots
AI总结：
针对球形机器人运动控制需兼顾指令跟踪与姿态、电流波动的问题，提出一种结合ESO与MLP的基于MPC的最优运动控制框架，实验证明其在快速性、准确性及能耗等指标上优于PID和SMC。
摘要：
运动控制对所有自主移动机器人至关重要，对球形机器人更是如此。由于球形机器人的独特性，其运动控制不仅要确保准确跟踪目标指令，还必须在跟踪过程中最小化机器人姿态和电机电流的波动。本文将模型预测控制（MPC）应用于球形机器人的控制，并设计了一种基于MPC的运动控制框架。该框架包含两个控制器：一个是结合了扩展状态观测器（ESO）与MPC的最优速度控制器ESO-MPC；另一个是最优姿态控制器，该控制器使用多层感知机（MLP）生成精确轨迹，并采用变权重MPC实现最优控制。最后，通过物理实验验证了各个控制器及整个控制框架的性能。实验结果表明，本文提出的基于MPC的运动控制框架在快速性和准确性方面远优于PID，并在超调量、姿态稳定性、电流稳定性和能耗方面相比滑模控制器（SMC）具有巨大优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
