Suspension System Theory
72
The Open Mechanical Engineering Journal, 2009, 3, 72-79
Open Access
Research on Suspension System Based on Genetic Algorithm and Neural Network Control
Chuan-Yin Tang and Li-Xin Guo*
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China
Abstract: In this paper, a five degree of freedom half body vehicle suspension system is developed and the road roughness intensity is modeled as a filtered white noise stochastic process. Genetic algorithm and neural network control are used to control the suspension system. The desired objective is proposed as the minimization of a multi-objective function formed by the combination of not only sprung mass acceleration, pitching acceleration, suspension travel and dynamic load, but also the passenger acceleration. With the aid of software Matlab/Simulink, the simulation model is achieved. Simulation results demonstrate that the proposed active suspension system proves to be effective in the ride comfort and drive stability enhancement of the suspension system. A mechanical dynamic model of the five degree of freedom half body of vehicle suspension system is also simulated and analyzed by using software Adams.
INTRODUCTION Suspension is the term given to the system of springs, shock absorbers and linkages that connects a vehicle to its wheels. Suspension systems can not only contribute to the car 's handling and braking for good active safety and driving pleasure, but also keep vehicle occupants comfortable and reasonably well isolated from road noise, bumps, and vibrations. The suspension also protects the vehicle itself and any cargo or luggage from damage and wear. The ride quality of a vehicle is significantly influenced by its suspension system, the road surface roughness, and the speed of vehicle. A vehicle designer can do little to improve road surface roughness, so designing a good suspension system with good vibration performance under different road conditions
The Open Mechanical Engineering Journal, 2009, 3, 72-79
Open Access
Research on Suspension System Based on Genetic Algorithm and Neural Network Control
Chuan-Yin Tang and Li-Xin Guo*
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China
Abstract: In this paper, a five degree of freedom half body vehicle suspension system is developed and the road roughness intensity is modeled as a filtered white noise stochastic process. Genetic algorithm and neural network control are used to control the suspension system. The desired objective is proposed as the minimization of a multi-objective function formed by the combination of not only sprung mass acceleration, pitching acceleration, suspension travel and dynamic load, but also the passenger acceleration. With the aid of software Matlab/Simulink, the simulation model is achieved. Simulation results demonstrate that the proposed active suspension system proves to be effective in the ride comfort and drive stability enhancement of the suspension system. A mechanical dynamic model of the five degree of freedom half body of vehicle suspension system is also simulated and analyzed by using software Adams.
INTRODUCTION Suspension is the term given to the system of springs, shock absorbers and linkages that connects a vehicle to its wheels. Suspension systems can not only contribute to the car 's handling and braking for good active safety and driving pleasure, but also keep vehicle occupants comfortable and reasonably well isolated from road noise, bumps, and vibrations. The suspension also protects the vehicle itself and any cargo or luggage from damage and wear. The ride quality of a vehicle is significantly influenced by its suspension system, the road surface roughness, and the speed of vehicle. A vehicle designer can do little to improve road surface roughness, so designing a good suspension system with good vibration performance under different road conditions
References: Received: December 03, 2008 © Tang and Guo; Licensee Bentham Open. Revised: April 21, 2009 Accepted: September 28, 2009