ABSTRACT Linear‐quadratic Gaussian (LQG) control is a well‐established method for optimal control through state estimation, particularly in stabilizing an inverted pendulum on a cart. In standard laboratory setups, sensor redundancy enables direct measurement of configuration variables using displacement sensors and rotary encoders. However, in outdoor environments, dynamically stable mobile platforms—such as Segways, hoverboards, and bipedal robots—often have limited sensor availability, restricting state estimation primarily to attitude stabilization. Since the tilt angle cannot be directly measured, it is typically estimated through sensor fusion, increasing reliance on inertial sensors and necessitating a lightweight, self‐contained perception module. Prior research has not incorporated accelerometer data into the LQG framework for stabilizing pendulum‐like systems, as jerk states are not explicitly modeled in the Newton–Euler formalism. In this paper, we address this gap by leveraging local differential flatness to incorporate higher‐order dynamics into the system model. This refinement enhances state estimation, enabling a more robust LQG controller that predicts accelerations for dynamically stable mobile platforms.
Engelsman et al. (Sun,) studied this question.