Machine learning-based modeling and control of underactuated balance robots
Creators: name: Han, Feng
Publisher: name: No Publisher Supplied
Year: 2024

description: Underactuated balance robot is a special class of mechanical systems whose number of control inputs is less than the number of degrees of freedom. Control of an underactuated balance robot requires trajectory tracking of the actuated subsystem and simultaneous balance control of the unactuated, unstable subsystem. Inverting the non-minimum phase unactuated subsystem is generally needed in the control design of underactuated balance robot. The dynamics coupling between actuated and unactuated subsystems, modeling errors, and disturbances from the environment bring additional challenges to control systems design. This dissertation aims to design machine learning and data-driven model-based control of underactuated balance robots. Dynamics model of underactuated balance robot can be presented into an external and internal convertible (EIC) form. Through a balance equilibrium manifold (BEM) design, an EIC-based control system achieves simultaneously exponential convergence to the desired trajectory and the BEM for the actuated and unactuated subsystems, respectively. However, the EIC-based control design requires accurate robot dynamics model. Machine learning-enabled, data-driven-based control design has potential to capture model uncertainties, and complex robot environment interactions. In particular, Gaussian process (GP) regression has been successfully demonstrated to extract robot dynamics from experiment data, even without any prior knowledge of robots' dynamics. We first present GP model-based performance-guaranteed control design and analysis for underactuated balance robots with $(n+m)$ dimensions, $n,m in mathbb{N}$. The GP models are used to capture the coupling effects between the $n$-dimensional actuated and $m$-dimensional unactuated subsystems through a constructed BEM. To avoid directly inverting the unactuated subsystem dynamics, the BEM is computed numerically through optimization. We identify the limitations of the EIC-based control by applying a time-varying coordinate transformation. It is shown that motion of some coordinates of the actuated subsystem are free of control inputs. To overcome the limitations, two new designs are proposed to expand the workspace of the learning-based EIC control framework. We design the control input for free motion variables in null space under the time-vary coordinate transformation such that the dynamics of other coordinates remain unchanged. Alternatively, a partial EIC-based controller is also presented to mitigate effect of the uncontrolled motions. Within the partial EIC framework, we virtually redesign the coupling dynamics between actuated subsystem and unactuated subsystem. The unactuated coordinates' motion only depends on the motion of the same number of actuated coordinates. The EIC-based control is only performed in partial coordinates of the robot system. Both controllers not only preserve the structured EIC-based control design with minimal revision but also improve closed-loop system performance. We also present a cascaded EIC (CEIC) structure for highly underactuated balanced robots, that is, $n