Establishing ANFIS and the use for predicting sliding control of active railway suspension systems subjected to uncertai
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ORIGINAL ARTICLE
Establishing ANFIS and the use for predicting sliding control of active railway suspension systems subjected to uncertainties and disturbances Sy Dzung Nguyen1,2 • Tae-Il Seo3
Received: 26 October 2015 / Accepted: 5 October 2016 Springer-Verlag Berlin Heidelberg 2016
Abstract The effectiveness of control of the active railway suspension system (ARSS) using a magnetorheological damper (MRD) with unknown track profile and load depends deeply on (1) the control strategy and the ability to adapt to noise, (2) system’s response delay compared with the real status of track profile impacting on it, and (3) uncertainty of the model used to describe the ARSS and external disturbance. Deriving from these, in order to improve the control efficiency, in this paper, we focus on three following factors. The first is to improve the accuracy of the MRD model. The second is to establish the ability to predict the track-profile’s status to update adaptively the optimal parameters of the control system. Finally, it is to build an uncertainty and disturbance observer (DUO) to compensate for noise. A novel algorithm for fuzzy C-means clustering (FCM) in an overlapping data space deriving from the Kernel space and the data potential field named PKFCM is proposed. Based on the PKFCM, the inverse MRD model is established as well as the design of a fuzzy-based predicting sliding controller (FPSC) for the ARSS is performed which is always updated by the optimal parameters adapting to the status of track profile. The
& Tae-Il Seo [email protected] Sy Dzung Nguyen [email protected] 1
Division of Computational Mechatronics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
2
Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
3
Department of Mechanical Engineering, Incheon National University, Incheon, South Korea
stability of the FPSC is proved theoretically while its performance is estimated by numerical surveys. Keywords Fuzzy C-means clustering ANFIS Active suspension Disturbance observer Sliding mode control
1 Introduction Nowadays the rail transport becomes an important form in many countries around the world. In order to increase effectiveness of this, many technical aspects need to be considered, including improving quality of the suspension system which performs an ability to store and dissipate vibration energy. Having essential advantages, the active suspension systems have been used widely for the high speed trains. In these, the controllers have to cope with nonlinear relations with time varying parameters, as well as uncertainty and external disturbance (UAD). To be one of the famous variable-structure control approaches, recently, sliding mode control (SMC) can be seen as a reasonable option for this operating condition [1–7]. However, reality shows that in inverse control or closed-loop control based on the basic SMC, system states are difficult to converge to the equilibrium in the finite time. On
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