Development of a Fuzzy Sky-hook Algorithm for a Semi-active ER Vehicle Suspension Using Inverse Model

Abstract

Due to the inherent nonlinear nature of Electro-rheological (ER) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the nonlinear damping force model is made to identify the properties of the ER damper using higher order spectrum. The higher order spectral analysis is used to investigate the nonlinear frequency coupling phenomena with the damping force signal according to the sinusoidal excitation of the damper.Also, this paper presents an inverse model of the ER damper, i.e., the model can predict the required voltage so that the ER damper can produce the desired force for the requirement of vibration control of vehicle suspension systems. The inverse model has been constructed by using piecewise linear damping force model.In this paper, the fuzzy logic control based on heuristic knowledge is combined with the skyhook control. And it is simulated for a quarter car model. The acceleration of the sprung mass is included in the premise part of the fuzzy rules to reduce the vertical acceleration RMS value of the sprung mass. Then scaling factors and membership functions are tuned using genetic algorithm to obtain optimal performance. J. S. Joh is with

A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers

“…Recently, semi-active dampers using ER or MR fluids have been effectively used in industry as smart dampers in which the damping force can be controlled or tuned by the intensity of electric field or magnetic field. The most prominent application field of smart dampers is in vehicle engineering such as suspension systems of road vehicles [1][2][3][4][5][6][7][8] and railway vehicles [9]. In addition, smart dampers can be usefully applied to floating raft isolation systems [10], wind-excited building structures [11] and for seismic isolation of structures [12,13].…”

“…They even can deal with inaccurate databases to deduce and continue learning in order to improve their efficiency based on an updated-training database. Thus, these methods have been widely used in identification, prediction and control of ER/MR damper suspensions; see, for example, [3,15,16] using FL, [1,13,17] using NN, and [18,19] using NF. In practice, when an NN is used, depending on each specific application, a reasonable structure for each net should be established.…”

“…(Some figures may appear in colour only in the online journal) Nomenclature A (•) Hyperplane in the output data space Ā, Ā(•) , Ã0 Matrices a Damping coefficient of the passive suspension 3 Author to whom any correspondence should be addressed.…”