Abstract
A fuzzy-H∞ control, improved with weighting functions, has been designed and applied to a novel model of a one-half semiactive lateral vehicle (OHSLV) suspension. The herein contribution resides in the development and computation of an H∞ controller with parallel distributed compensation (PDC) designed from a highly nonlinear system modelled via the Takagi–Sugeno (T-S) fuzzy approach. A fuzzy-H∞ controller is synthesized for an OHSLV T-S fuzzy model of a suspension with two magnetorheological (MR) dampers including actuators’ nonlinear dynamics. The realism of results has been improved by considering the MR damper’s behaviours (viscoplasticity, hysteresis, and saturation) and the handling of the phase angle of the sinusoidal disturbance, not included in other reported work. Time-domain tests remark transient time achievements, whereas precise performance criterion indices in the frequency domain are employed to assess the generated outcomes. The proposed solution complies with all performance criteria compared with a benchmark passive average suspension that fails in satisfying most of the performance criteria.
Highlights
Ground vehicle suspension systems provide a certain level of passenger comfort and vehicle stability by covering a set of basic functions such as supporting vehicle’s weight, keeping tires in contact with the road, holding an optimal height of the vehicle, and isolating passengers against vibrations from road’s disturbances, among others [1]
Considering that MR dampers have nonlinear phenomena such as saturation, hysteresis, and dynamics of a fluid going through an orifice [7], obtaining an accurate modelling becomes a critical task when applying them for vehicle suspensions
Pang et al [24] developed a fuzzy controller for an experimental MR semiactive suspension based on neural networks and particle swarm optimization
Summary
Ground vehicle suspension systems provide a certain level of passenger comfort and vehicle stability by covering a set of basic functions such as supporting vehicle’s weight, keeping tires in contact with the road, holding an optimal height of the vehicle, and isolating passengers against vibrations from road’s disturbances, among others [1]. Pang et al [24] developed a fuzzy controller for an experimental MR semiactive suspension based on neural networks and particle swarm optimization All these contributions are notable results in frequency and time domains, and their outcomes were compared against a passive or active benchmark suspension. Even though the state of the art provides important improvements in passenger comfort and vehicle stability, for the best of authors’ knowledge, none of them individually reports a solution that considers actuator’s nonlinear dynamics in controller computation, as well as time-domain and frequency-domain tests paired with performance indexes. It is out of the scope of this report to develop the MR damper’s characteristics and to review the Takagi–Sugeno fuzzy model of a one-half semiactive vehicle suspension: lateral approach
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