External and internal convertible (EIC) form-based motion control is one of the effective designs of simultaneously trajectory tracking and balance for underactuated balance robots. Under certain conditions, the EIC-based control design however leads to uncontrolled robot motion. We present a Gaussian process (GP)-based data-driven learning control for underactuated balance robots with the EIC modeling structure. Two GP-based learning controllers are presented by using the EIC structure property. The partial EIC (PEIC)-based control design partitions the robotic dynamics into a fully actuated subsystem and one reduced-order underactuated system. The null-space EIC (NEIC)-based control compensates for the uncontrolled motion in a subspace, while the other closed-loop dynamics are not affected. Under the PEIC- and NEIC-based, the tracking and balance tasks are guaranteed and convergence rate and bounded errors are achieved without causing any uncontrolled motion by the original EIC-based control. We validate the results and demonstrate the GP-based learning control design performance using two inverted pendulum platforms.

翻译：内外可转换（EIC）形式的运动控制是实现欠驱动平衡机器人同时进行轨迹跟踪与平衡的有效设计之一。然而，在某些条件下，基于EIC的控制设计会导致机器人运动失控。本文针对具有EIC建模结构的欠驱动平衡机器人，提出了一种基于高斯过程（GP）的数据驱动学习控制方法。利用EIC结构特性，我们提出了两种基于GP的学习控制器。基于部分EIC（PEIC）的控制设计将机器人动力学划分为一个全驱动子系统和一个降阶的欠驱动系统。基于零空间EIC（NEIC）的控制设计则在一个子空间中补偿失控运动，同时不影响其他闭环动力学。在PEIC与NEIC框架下，轨迹跟踪与平衡任务得以保证，且收敛速率与有界误差均能实现，避免了原始EIC控制中可能出现的失控运动。我们通过两个倒立摆平台验证了结果，并展示了基于GP的学习控制设计的性能。