Paper Info
Title
Riding And Speed Governing For Parallel Two-Wheeled Scooter Based On Sequential Online Learning Control By Humanoid Robot
Abstract
The sequential online tuning for controller gains is required for the continuous action of the riding into parallel two-wheeled scooter and the speed governing after riding by humanoid robot. The implemented controllers are different between the riding and the speed governing, and these tuning strategies are also different. In particular, the riding requires the immediate tuning in the short riding phase and the speed governing requires the accurate tuning to regulate the speed of humanoid robot. To the above requirements, this paper proposes the Sequential Online Learning Control (SOLC) method composed of the cascade connection of SGD-based open-loop Learning Control (SLC) and Mini-batch-based closed-loop Learning Control (MLC). SLC contributes the damping gain online tuning for the foot torque control during execution of riding, and MLC contributes the PID gains online tuning for the speed governing control. Finally, we show the validity of SOLC through the sequential experiment of riding and speed governing for parallel two-wheeled scooter by life-sized humanoid robot HRP2-JSK.
Year
DOI
Venue
2018
10.1109/IROS.2018.8593685
2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Field
DocType
ISSN
Online learning,Control theory,Torque,PID controller,Computer science,Control engineering,Cascade,Control system,Humanoid robot
Conference
2153-0858
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Kohei Kimura102.03
Shunichi Nozawa28515.81
Hiroto Mizohana301.01
Kei Okada4534118.08
Masayuki Inaba52186410.27