Stable Learning-Based Tracking Control of Underactuated Balance Robots

Abstract

We present a Gaussian process (GP)-based tracking control of underactuated balance robots in which an actuated subsystem is required to follow a desired trajectory, while an unactuated, unstable subsystem needs to be kept balanced. The GP models are used to capture the coupling effects between the actuated/unactuated subsystems through a constructed balance equilibrium manifold (BEM). Optimization-based algorithm is used to obtain the BEM estimation. The control design takes advantage of the structural property of the robot dynamics and is built on the GP models with a data selection algorithm. Stability analysis is given to guarantee the tracking control performance. The control design and comparison with other controllers are demonstrated through experiments on a rotary pendulum.