Speed governing behavior on parallel two-wheeled electric scooter by life-sized humanoid robot based on learning control with online tuning for PID gain during control

Abstract

This paper proposes the online learning controller for PID gain tuning to regulate the speed of robot on parallel two-wheeled electric scooter. We define this speed regulation as “speed governing behavior”. The proposed system based on Iterative Feedback Tuning (IFT) contributes the online continuity during learning control not to interrupt and reset/restart the controller. PID gain update during control has the problem that the integral gain change affects the divergence of control input. To this problem, we propose the digital integrator which modifies the previous integrated value of control error. This proposed integrator solves the impulsive control input without any anti-windup or reference shaping. In conclusion, this paper shows the experimental results based on the online learning control and demonstrates the speed governing behavior by life-sized humanoid robot. We finally aim the driving of parallel two-wheeled electric scooter by speed control of humanoid robot.