Fuzzy Logic Controller by Particle Swarm Optimization Discoverer for Semi-Active Suspension System

• Published in: Lecture Notes in Electrical Engineering
• Main Author: Ab Talib M.H.; Rosli N.H.M.; Darus I.Z.M.; Yatim H.M.; Hadi M.S.; Ardani M.I.; Saufi M.S.R.M.; Yamin A.H.M.
• Format: Conference paper
• Language: English
• Published: Springer Science and Business Media Deutschland GmbH 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151061648&doi=10.1007%2f978-981-19-8703-8_17&partnerID=40&md5=d127872366c1c611758c5a37cd289291

Semi-active suspension systems utilizing magneto-rheological damper have been used especially in the vehicle due to their simple design and control with the effective outcome. Nevertheless, the FL controller design without considering the intelligent algorithm utilizing the FL gain scaling leads to the undesirable condition of the vehicle body. Thus, this study is conducted to develop and evaluate the performance of the particle swarm optimization discoverer (PSOD) in tuning the fuzzy logic (FL) controller in a semi-active suspension system while being compared to the original particle swarm optimization (PSO) and passive system. Taking an acceleration of the suspension system response as an objective function, the PSOD strategy is an attempt to find and search for an optimum value of the gains that able to be a sort of contact information for improving the targeted value obtained from the FL controller. The application of this system is simulated in MATLAB Simulink. The effectiveness of the PSOD was shown by the simulation result with as high as 63.79% and 59.82% of improvement in terms of sprung displacement and sprung acceleration, respectively. This result indicates that the PSOD could provide improvement for vehicle ride comfort and effective improvement solution over the PSO. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.