A Fuzzy LQR PID Control for a Two-Legged Wheel Robot with Uncertainties and Variant Height

Abstract

This paper proposes a fuzzy LQR PID control for a two-legged wheeled balancing robot for keeping stability against uncertainties and variant heights. The proposed control includes the fuzzy supervisor, LQR, PID, and two calibrations. The fuzzy LQR is conducted to control the stability and motion of the robot while its posture changes with respect to time. The fuzzy supervisor is used to adjust the LQR control according to the robotic height. It consists of one input and one output. The input and output have three membership functions, respectively, to three postures of the robot. The PID control is used to control the posture of the robot. The first calibration is used to compensate for the bias value of the tilting angle when the robot changes its posture. The second calibration is applied to compute the robotic height according to the hip angle. In order to verify the effectiveness of the proposed control, a practical robot with the variant height is constructed, and the proposed control is embedded in the control board. Finally, two experiments are also conducted to verify the balancing and moving ability of the robot with the variant posture.

Adaptive Parallel Iterative Learning Control with A Time-Varying Sign Gain Approach Empowered by Expert System

“…During the initial execution, the learning control input is initialized to zero, resulting in 𝑢 0 = 𝑢 0 𝐶 . Upon activation of the learning mechanism, the control input is subsequently updated according to 𝑢 𝑗 = 𝑢 𝑗 𝐿 + 𝑢 𝑗 𝐶 , where 𝑢 𝑗 𝐿 is derived from the update specified in (5).…”

Time-Varying Sign Gain with Expert System in Serial Iterative Learning Control Architecture

“…However, recent attention has shifted towards algorithmic control of electric motor rotation, with PID control emerging as a fundamental approach [10], [11].…”