Fuzzy Inverse Model of Magnetorheological Dampers for Semi-Active Vibration Control of an Eleven-Degrees of Freedom Suspension System

Abstract

A semi-active controller-based Fuzzy logic for a suspension system with magnetorheological (MR) dampers is presented and evaluated. An Inverse Fuzzy Model (IFM) is constructed to replicate the inverse dynamics of the MR damper. The typical control strategies are Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) controllers with a Clipped optimal control algorithm, while inherent time-delay and non-linear properties of MR damper lie in these strategies. LQR part of LQG controller is also designed to produce the optimal control force. After that the LQG controller and the IFM models are linked to control strategy. The effectiveness of the IFM is illustrated and verified using simulated responses of a full-car model. The results demonstrate that by using the IFM model, the MR damper force can be commanded to follow closely the desirable optimal control force. The membership functions of IFM tuned by the results of Clipped optimal strategy. The results also show that the control system is effective and achieves better performance and less control effort than the optimal in improving the service life of the suspension system and the ride comfort of the car.

Hybrid Sliding Mode Control of Full-Car Semi-Active Suspension Systems

“…In terms of control, many control strategies have been proposed in the literature, including PID control [16], fuzzy Logic control [17,18], optimal control [19,20], LQ/LQG control [21], H ∞ control [22], and control strategies based on genetic algorithms and neural networks [23][24][25]. However, one of the challenging problems in the design and implementation of intelligent suspension control systems is that there is currently no solution supporting the export of generic suspension models and control components for integration into embedded Electronic Control Units (ECUs).…”

Hybrid Modelling and Sliding Mode Control of Semi-Active Suspension Systems for Both Ride Comfort and Road-Holding

“…Various control strategies and models have been proposed for semi-active vehicle suspension systems. This includes the use of non-parametric models [10][11][12] and parametric models [13][14][15][16], as well as different control strategies such as LQ (Linear-Quadratic)/LQG (Linear-Quadratic-Gaussian) control [17], H ∞ control [18], optimal control [19,20], fuzzy Logic control [21,22], PID control [23] and control strategies based on neural networks [24,25] and genetic algorithms [26]. However, the implementation of vehicle suspension models and controllers for embedded software requires very high efforts in creation, simplification, discretization and numerical solution, implementation, testing and fulfilling coding requirements, because no appropriate tool support is available and all parts have to be manually developed.…”

A fuzzy-based dynamic inversion controller with application to vibration control of vehicle suspension system subjected to uncertainties

“…In these systems, damping force generated by the magneto-rheological dampers (MRDs) to stamp out chassis vibration is controlled via the intensity of magnetic field by control strategies. [1][2][3][4][5][6][7] As the inherent features, the hysteretic response and time varying of physical parameters of the magneto-rheological (MR) fluid due to system temperature variation during the operating process always exist. 2,3 This results in the model error or uncertainty implicit the time varying tendency.…”