Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper

Abstract

This paper presents a new approach for intelligent fuzzy logic (IFL) controller tuning via firefly algorithm (FA) and particle swarm optimization (PSO) for a semi-active (SA) suspension system using a magneto-rheological (MR) damper. The SA suspension system's mathematical model is established based on quarter vehicles. The MR damper is used to change a conventional damper system to an intelligent damper. It contains a magnetic polarizable particle suspended in a liquid form. The Bouc-Wen model of a MR damper is used to determine the required damping force based on forcedisplacement and force-velocity characteristics. The performance of the IFL controller optimized by FA and PSO is investigated for control of a MR damper system. The gain scaling of the IFL controller is optimized using FA and PSO techniques in order to achieve the lowest mean square error (MSE) of the system response. The performance of the proposed controllers is then compared with an uncontrolled system in terms of body displacement, body acceleration, suspension deflection, and tire deflection. Two bump disturbance signals and sinusoidal signals are implemented into the system. The simulation results demonstrate that the PSO-tuned IFL exhibits an improvement in ride comfort and has the smallest MSE for acceleration analysis. In addition, the FA-tuned IFL has been proven better than IFL-PSO and uncontrolled systems for both road profile conditions in terms of displacement analysis.

A Hydraulic Semi-Active Suspension Based on Road Statistical Properties and Its Road Identification

“…The road identification method proposed is based on Formula (8). It is assumed that the road input is regarded as white noise, but the actual pavement is not.…”

“…According to the measured acceleration signals and the test vehicle parameters, the PSD G q (n 0 ) values are identified using Formula (8). The results are shown in Table 2.…”

“…The analysis results show that using the vehicle parameters ω 0 , r k , r m , the speed signal v, the stepper motor feedback signal θ', and the body vertical acceleration signal .. z 2 , the PSD G q (n 0 ) for the vehicle under current running condition can be calculated using Equation (8). Thus, the current running condition can be identified based on Equation (8).…”

“…The controllable damper is the key component of semi-active suspensions for a variety of vehicles [8,9]. Generally speaking, the existing controllable dampers include three types: the MR (magneto-rheological) damper [10], the ER (electro-rheological) damper [11], and the controllable hydraulic damper [12].…”

GA-based multi-objective optimization of active nonlinear quarter car suspension system—PID and fuzzy logic control

“…From above discussions, it is observed that researchers (Taskin et al 2017;Huang and Chao 2000;Salem and Aly 2009;Rajendiran and Lakshmi 2016) have successfully implemented the FLC for ride control applications, whereas (Kalaivani et al 2014) optimized FLC using a single optimization function. Talib and Darus (2014) implemented a semi-active suspension system using FLC and PID controllers. Performance of both the controllers is optimized by the PSO algorithm for mean square error.…”