Intelligent Optimization of Force Tracking Parameters for MR Damper Modelling using Firefly Algorithm

• Main Authors: Ab Talib, M.H (Author), Darus, I.Z.M (Author), Hadi, M.S (Author), Jamali, A. (Author), Shaharuddin, N.M.R (Author), Yatim, H.M (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2019
• Subjects: Bioluminescence; Controller parameter; Controllers; Conventional methods; Damping; Existing controllers; firefly algorithm; Firefly algorithms; force tracking; Heuristic methods; intelligent optimization; Intelligent optimization; Intelligent optimization method; Low-power consumption; magnetorheological damper; Magneto-rheological dampers; Optimization; Parameter estimation; semi active suspension; Suspensions (components)
• Online Access: View Fulltext in Publisher; View in Scopus

 Magnetorheological (MR) damper system is commonly used to replace the conventional damper in the suspension system due to its low power consumption, fast time response and simple structure. Since inner loop controller is very important in defining the amount of current supplied to the MR damper system, many existing controllers are found not well-structured in terms of calculating the optimum value of the controller parameter. Poor control design using the conventional method will cause the output current obtained for the MR damper to be unpredictable. To overcome this problem, an intelligent optimization method known as firefly algorithm (FA) was used by this study to optimize the force tracking controller (FTC) parameters as to achieve the exact damping force of MR damper system. The MR damper was first developed using Spencer model and the required voltage input was then provided by the FTC. The controller parameters were tuned using intelligent FA method in order to find the optimum values which would identify the accuracy of the force tracking that followed the MR damping force. The simulation shows that the FTC with FA technique is able to track the desired force better than the heuristic method up to 1.71 % error considering a given desired input force. © 2019 IEEE.