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Optimal Seat and Suspension Design for a Half-Car with Driver Model Using Genetic Algorithm
Engineering Physics and Mathematics Department, Faculty of Engineering (Mataria), Helwan University, Cairo, Egypt
Automotive and Tractors Engineering Department, Faculty of Engineering (Mataria), Helwan University, Cairo, Egypt
Electrical Engineering Department, Faculty of Engineering, Al-Baha University, Al-Baha, KSA
Mechanical Design Department, Faculty of Engineering (Mataria), Helwan University, Cairo, Egypt
Mechanical Engineering Department, Faculty of Engineering, Al-Baha University, Al-Baha, KSA ;Engineering and Mechanical Design Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
- 1 Engineering Physics and Mathematics Department, Faculty of Engineering (Mataria), Helwan University, Cairo, Egypt
- 2 Automotive and Tractors Engineering Department, Faculty of Engineering (Mataria), Helwan University, Cairo, Egypt
- 3 Electrical Engineering Department, Faculty of Engineering, Al-Baha University, Al-Baha, KSA
- 4 Mechanical Design Department, Faculty of Engineering (Mataria), Helwan University, Cairo, Egypt
- 5 Mechanical Engineering Department, Faculty of Engineering, Al-Baha University, Al-Baha, KSA ;Engineering and Mechanical Design Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
Intelligent Control and Automation·Volume 04 (2013)·Pages 199–205·Published 24 May 2013·DOI10.4236/ica.2013.42024
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Abstract
This paper presents an optimal vehicle and seat suspension design for a half-car vehicle model to reduce human-body vibration (whole-body vibration). A genetic algorithm is applied to search for the optimal parameters of the seat and vehicle suspension. The desired objective is proposed as the minimization of a multi-objective function formed by the combination of seat suspension working space (seat suspension deflection), head acceleration, and seat mass acceleration to achieve the best comfort of the driver. With the aid of Matlab/Simulink software, a simulation model is achieved. In solving this problem, the genetic algorithms have consistently found near-optimal solutions within specified parameters ranges for several independent runs. For validation, the solution obtained by GA was compared to the ones of the passive suspensions through sinusoidal excitation of the seat suspension system for the currently used suspension systems.
KeywordsBiodynamic ResponseGenetic AlgorithmsMulti-Objective OptimizationMathematical Model
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