Abstract
Abstract This study proposes a novel variable universe fuzzy control design for vehicle semi-active suspension system with magnetorheological (MR) damper through the combination of fuzzy neural network (FNN) and particle swarm optimization (PSO). By constructing a quarter-vehicle test rig equipped with MR damper and then collecting the measured data, a non-parametric model of MR damper based on adaptive neuro-fuzzy inference system is first presented. And then a Takagi–Sugeno (T–S) fuzzy controller is designed to achieve the effective control of the input current in MR damper by using the contraction-expansion factors. Furthermore, an appropriate FNN controller is proposed to obtain the contraction-expansion factors, in which particle swarm optimization and back propagation are introduced as the learning and training algorithm for the FNN controller. Lastly, a simulation investigation is provided to validate the proposed control scheme. The results of this study can provide the technical foundation for the development of vehicle semi-active suspension system.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
