Evolutionary algorithm-based PID controller tuning for nonlinear quarter-car electrohydraulic vehicle suspensions
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- 作者：Muhammed Dangor (1)
Olurotimi A. Dahunsi (1)
Jimoh O. Pedro (1)
M. Montaz Ali (2) - 关键词：Force feedback ; PID control ; Active vehicle suspension system ; Genetic algorithm ; Particle swarm optimization ; Differential evolution
- 刊名：Nonlinear Dynamics
- 出版年：2014
- 出版时间：December 2014
- 年：2014
- 卷：78
- 期：4
- 页码：2795-2810
- 全文大小：1,580 KB
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Olurotimi A. Dahunsi (1)
Jimoh O. Pedro (1)
M. Montaz Ali (2)
1. School of Mechanical, Industrial and Aeronautical Engineering, University of the Witwatersrand, 1 Jan Smut Avenue, Private Bag 03, Johannesburg WITS 2050, South Africa
2. Faculty of Science, and TCSE, Faculty of Engineering and Built Environment, School of Computational and Applied Mathematics, University of the Witwatersrand, 1 Jan Smut Avenue, Private Bag 03, Johannesburg, WITS 2050, South Africa - ISSN：1573-269X
文摘
The basic challenge associated with the design of vehicle suspension system is the attainment of an optimal trade-off between the various design objectives. This study presents the design of proportional-integral-derivative (PID) controller for a quarter-car active vehicle suspension system (AVSS) using evolutionary algorithms (EA) such as the particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE). Each of the EA-based PID controllers showed overall improvement in suspension travel, ride comfort, settling time and road holding from the manually tuned controller and the passive vehicle suspension system. These improvements were, however, achieved at the cost of increased actuator force, power consumption and spool-valve displacement. DE-optimized PID control resulted in the best minimized suspension performance, followed by the GA and PSO, respectively. Frequency-domain analysis showed that all the signals were attenuated within the whole body vibration frequency range and the EA-optimized controllers had RMS frequency weighted body acceleration of the vehicle within allowable limits for vibration exposure. Robustness analysis of the DE-optimized PID-controlled AVSS to model uncertainties is carried out in the form of variation in vehicle sprung mass loading, tyre stiffness and speed.