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Establishing ANFIS and the Use for Predicting Sliding Control of Active Railway Suspension Systems Subjected to Uncertainties and Disturbances


Sy Dzung Nguyen, Tae-Il Seo*

Source title: 
International Journal of Machine Learning and Cybernetics, 9(5): 853-865, 2016 (ISI)
Academic year of acceptance: 

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 stability of the FPSC is proved theoretically while its performance is estimated by numerical surveys.