Fast and Stable Learning of Dynamical Systems Based on Extreme Learning Machine

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 49; no. 6; pp. 1175 - 1185
• Main Authors: Duan, Jianghua, Ou, Yongsheng, Hu, Jianbing, Wang, Zhiyang, Jin, Shaokun, Xu, Chao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-06-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Asymptotic stability; Control stability; Dynamical systems; Dynamics; Extreme learning machine (ELM); Handwriting; learn from demonstrations; Modelling; Neural networks; nonlinear dynamical system; Robot control; Robot dynamics; Robot motion; Robots; Stability analysis; Training; Trajectory
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2017.2705279

The approach of dynamical system (DS) is promising for modeling robot motion, and provides a flexible means of realizing robot learning and control. Accuracy, stability, and learning speed are the three main factors to be considered when learning robot movements from human demonstrations with DS. Some approaches yield stable dynamical systems, but these may result in a poor reproduction performance, while some approaches yield good reproduction performance but are quite complex and time-consuming. In this paper, we address the accuracy-stability-speed issues simultaneously. We present a learning method named the fast and stable modeling for dynamical systems, which is based on the extreme learning machine to efficiently and accurately learn the parameters of the DS as well as to ensure the asymptotic stability at the target. We confirm the proposed approach by performing both 2-D tasks of learning handwriting motions and a set of robot experiments.