Sliding‐mode observer‐based speed‐sensorless vector control of linear induction motor with a parallel secondary resistance online identification. Issue 8 (9th July 2018)

• Record Type: Journal Article
• Authors: Wang, Huimin; Liu, Yong‐chao; Ge, Xinglai
• Is Part Of: IET electric power applications. Volume 12:Issue 8(2018)
• Page Start: 1215
• Page End: 1224
• Publication Date: 2018-07-09
• Subjects: induction motor drives -- stability -- sensorless machine control -- linear induction motors -- parameter estimation -- magnetic levitation -- observers -- machine vector control -- Lyapunov methods -- angular velocity control -- model reference adaptive control systems -- rotors -- variable structure systems; medium–low‐speed maglev applications -- speed estimation scheme performance -- sliding mode observer -- state space‐vector model -- secondary resistance estimation algorithms -- speed estimation algorithm -- conventional speed estimation scheme -- sliding‐mode observer‐based speed‐sensorless‐vector control -- ensorless‐vector‐controlled linear induction motor drives -- LIM drives -- sliding mode model reference adaptive system observer -- parallel secondary resistance online identification -- dynamic end effect -- Popov hyperstability theory -- Lyapunov stability theory -- stability analysis -- single‐manifold SMO -- hardware‐in‐the‐loop tests; Electric power -- Periodicals; Electric power systems -- Periodicals; 621.305
• DOI: 10.1049/iet-epa.2018.0049
• Languages: English
• ISSNs: 1751-8660

Abstract

Abstract : This study proposes a speed estimation scheme for the sensorless‐vector‐controlled linear induction motor (LIM) drives for medium–low‐speed maglev applications, which is composed of two parts: (i) a sliding mode model reference adaptive system observer for speed estimation; and (ii) a parallel secondary resistance online identification for achieving the improvements of the proposed speed estimation scheme performance. The sliding mode observer (SMO) is established on the basis of the state space‐vector model of the LIM considering the dynamic end effect. Based on SMO, both speed and secondary resistance estimation algorithms are obtained by utilising Popov's hyperstability theory. Moreover, the Lyapunov stability theory is adopted for the stability analysis of the proposed speed estimation scheme. The effectiveness of the proposed speed estimation algorithm has been verified and compared with the performance of the conventional speed estimation scheme based on single‐manifold SMO by the simulation and hardware‐in‐the‐loop tests.