Vibration Control of MR-Damped Half Truck Suspension System Using Proportional Integral Derivative Controller Tuned by Ant Colony Optimization

2024-01-2289

04/09/2024

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WCX SAE World Congress Experience
Authors Abstract
Content
Proportional integral derivative (PID) control technique is a famous and cost-effective control strategy, in real implementation, applied in various engineering applications. Also, the ant colony optimization (ACO) algorithm is extensively applied in various industrial problems. This paper addresses the usage of the ACO algorithm to tune the PID controller gains for a semi-active heavy vehicle suspension system integrated with cabin and seat. The magnetorheological (MR) damper is used in main suspension as a semi-active device to enhance the ride comfort and vehicle stability. The proposed semi-active suspension consists of a system controller that calculate the desired damping force using a PID controller tuned using ACO, and a continuous state damper controller that predict the input voltage that is required to track the desired damping force. The ACO algorithm is used to solve the nonlinear optimization problem to search the PID controller gains by finding the optimal problem solution. A mathematical model of an eight degree-of-freedom MR-damped heavy vehicle suspension system is derived and simulated using Matlab/Simulink software. The proposed ACO PID controlled suspension is compared to both MR semi active (ON-OFF) and conventional passive system. System performance criteria are evaluated under different road disturbances to quantify the success of the proposed controller. The simulated results reflect that the proposed ACO PID controller of the MR-damped heavy vehicle suspension offers a significant enhancement in ride comfort and vehicle stability.
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DOI
https://doi.org/10.4271/2024-01-2289
Pages
25
Citation
Gad, S., Metered, H., Bassiuny, A., and Abdel-Ghany, A., "Vibration Control of MR-Damped Half Truck Suspension System Using Proportional Integral Derivative Controller Tuned by Ant Colony Optimization," SAE Technical Paper 2024-01-2289, 2024, https://doi.org/10.4271/2024-01-2289.
Additional Details
Publisher
Published
Apr 09
Product Code
2024-01-2289
Content Type
Technical Paper
Language
English