On stability for learning human control strategy by demonstrations using SVM. Issue 1 (2nd December 2019)

• Record Type: Journal Article
• Authors: Wang, Zhiyang; Ou, Yongsheng
• Is Part Of: Assembly automation. Volume 40:Issue 1(2020)
• Page Start: 118
• Page End: 131
• Publication Date: 2019-12-02
• Subjects: Stability analysis -- Domain of attraction -- Learning human control strategy by demonstrations -- Manufacturing and robotics; Automation -- Periodicals; Automatic machinery -- Periodicals; Assembly-line methods -- Periodicals; Industrial engineering -- Periodicals; 670.42705
• DOI: 10.1108/AA-11-2018-0236
• Languages: English
• ISSNs: 0144-5154

Abstract

Abstract : Purpose: This paper aims to deal with the trade-off of the stability and the accuracy in learning human control strategy from demonstrations. With the stability conditions and the estimated stability region, this paper aims to conveniently get rid of the unstable controller or controller with relatively small stability region. With this evaluation, the learning human strategy controller becomes much more robust to perturbations. Design/methodology/approach: In this paper, the criterion to verify the stability and a method to estimate the domain of attraction are provided for the learning controllers trained with support vector machines (SVMs). Conditions are formulated based on the discrete-time system Lyapunov theory to ensure that a closed-form of the learning control system is strongly stable under perturbations (SSUP). Then a Chebychev point based approach is proposed to estimate its domain of attraction. Findings: Some of such learning controllers have been implemented in the vertical balance control of a dynamically stable, statically unstable wheel mobile robot.