Gaussian-Process-Based Control of Underactuated Balance Robots With Guaranteed Performance

Abstract

The control of underactuated balance robots is aimed at performing both the external (actuated) subsystem trajectory tracking and internal (unactuated) subsystem balancing tasks. In this article, we propose a learning-based control design for underactuated balance robots. The key idea integrates a model predictive control method to design the desired internal subsystem trajectory and perform the external subsystem tracking task, while an inverse dynamics controller is used to stabilize the internal subsystem to its desired trajectory. The control design is based on Gaussian process (GP) regression models that are learned from experiments without requiring <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge about the robot dynamics or the demonstration of successful stabilization. GP regression models also provide estimates of modeling uncertainties of the robotic systems, and these estimations are used to enhance control robustness to modeling errors. The learning-based control design is analyzed with guaranteed stability and performance. The proposed design is demonstrated by experiments on a Furuta pendulum and an autonomous bikebot.