Title: Theoretical investigation of the rail vehicle suspension system using different optimised controllers by harmony search algorithm incorporating magnetorheological dampers

Authors: Shaimaa Ahmed Ali; Hassan Metered; A.M. Bassiuny; A.M. Abdel Ghany

Addresses: Helwan University, Cairo, Egypt; High Institute of Engineering, Culture and Science City, Cairo, Egypt ' Faculty of Engineering, Helwan University, Mataria Campus, Ain Shams, 11718, Cairo, Egypt ' Faculty of Engineering, Heliopolis University, Cairo, Egypt; Faculty of Engineering, Helwan University, Cairo, Egypt ' Helwan University, Cairo, Egypt; Higher Engineering Institute, Thebes Academy, Cairo, Egypt

Abstract: Magnetorheological (MR) dampers are highly valuable semi-active devices for vibration control applications rather than active actuators in terms of reliability and implementation cost. This paper offers a deeply theoretical investigation into the use of proportional integral derivative (PID), fractional order PID (FOPID), and single-neuron PID (SNPID) for the first time in conjunction with the damper controller of a rail semi-active MR vehicle suspension. The different gains of the three mentioned controllers are tuned and optimised using the harmony search (HS) algorithm to achieve good suspension performance in the vertical direction. The self-adaptive global best harmony search (SGHS) method is selected to optimise controllers' gains due to its effectiveness in reducing tuning time and minimising the objective function value. A quarter-rail vehicle model consisting of six degrees of freedom (6-DOF) is derived and simulated using MATLAB/Simulink software. System performance criteria are evaluated in the time and frequency domains to evaluate the effectiveness of proposed controllers. The simulated results show that the SNPID significantly improves ride comfort over the applied controllers.

Keywords: magnetorheological dampers; rail vehicle suspension; self-adaptive global best harmony search algorithm; SGHS; PID; fractional order PID; FOPID; single-neuron PID.

DOI: 10.1504/IJVP.2024.137692

International Journal of Vehicle Performance, 2024 Vol.10 No.2, pp.144 - 176

Received: 26 Aug 2023
Accepted: 30 Sep 2023
Published online: 02 Apr 2024 *

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