标题： 学习滚动$μ$机器人的跟踪控制器 显示英文标题

标题： Learning a Tracking Controller for Rolling $μ$bots

摘要： 微米级机器人（$\mu$机器人）最近在新兴的医疗应用中展现出巨大的潜力。 准确控制$\mu$机器人对于其成功部署至关重要，但具有挑战性。 在本工作中，我们考虑在存在干扰和不确定性的情况下，使用$\mu$机器人跟踪参考轨迹的问题。 干扰主要来自布朗运动和其他环境现象，而不确定性则来源于模型参数中的误差。 我们将$\mu$机器人建模为一个不确定的全向移动机器人，由全局磁场进行控制。 为了补偿干扰和不确定性，我们开发了一个非线性不匹配控制器。 我们将模型不匹配误差定义为我们的模型预测速度与$\mu$机器人实际速度之间的差异。 我们采用高斯过程来学习模型不匹配误差作为施加控制输入的函数。 然后我们使用最小二乘法来选择一个控制动作，以最小化$\mu$机器人实际速度与参考速度之间的差异。 我们在仿真中展示了我们联合学习和控制算法的在线性能，其中我们的方法准确地学习了模型不匹配并提高了跟踪性能。 我们还在实验中验证了我们的方法，并表明某些误差指标最多减少了$40\%$。 显示英文摘要

摘要： Micron-scale robots ($\mu$bots) have recently shown great promise for emerging medical applications. Accurate controlling $\mu$bots, while critical to their successful deployment, is challenging. In this work, we consider the problem of tracking a reference trajectory using a $\mu$bot in the presence of disturbances and uncertainty. The disturbances primarily come from Brownian motion and other environmental phenomena, while the uncertainty originates from errors in the model parameters. We model the $\mu$bot as an uncertain unicycle that is controlled by a global magnetic field. To compensate for disturbances and uncertainties, we develop a nonlinear mismatch controller. We define the model mismatch error as the difference between our model's predicted velocity and the actual velocity of the $\mu$bot. We employ a Gaussian Process to learn the model mismatch error as a function of the applied control input. Then we use a least-squares minimization to select a control action that minimizes the difference between the actual velocity of the $\mu$bot and a reference velocity. We demonstrate the online performance of our joint learning and control algorithm in simulation, where our approach accurately learns the model mismatch and improves tracking performance. We also validate our approach in an experiment and show that certain error metrics are reduced by up to $40\%$.