新型仿生BELBIC和PSOBELBIC控制半主动悬架的设计与分析

Pankaj Sharma, Vinod Kumar
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引用次数: 4

摘要

乘客的舒适性、乘坐质量和操控性引起了汽车设计工程师的极大关注。研究人员正在研究并引入了人工神经网络、遗传算法、模糊和混合控制技术等方法,并对其进行了建模和分析,以检查其对上述参数的影响。介绍了一种基于人脑学习模式的新型控制器,即基于大脑情感学习的智能控制器(BELBIC)和基于粒子群优化算法优化的基于粒子群情感学习的大脑智能控制器(PSOBELBIC)。建立了四分之一乘用车和四自由度半挂车的六自由度数学模型并进行了仿真。PSOBELBIC的性能最好,在减速带和随机道路上分别提高了约26%和32.54%和52.79%和34.58%。
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Design and analysis of novel bio inspired BELBIC and PSOBELBIC controlled semi active suspension
Passenger comfort, quality of ride and handling brings a lot of attention and concern of the automotive design engineers. Researchers are working and have introduced approaches like artificial neural network, genetic algorithm, fuzzy and hybrid control techniques have been proposed, modelled and analysed to check their impact on the above mentioned parameters. A new controller based on learning mode of human brain known as brain emotional learning based intelligent controller (BELBIC) and particle swarm optimised brain emotional learning based intelligent controller (PSOBELBIC) optimised by particle swarm optimisation (PSO) is introduced here. Six degrees of freedom (DOF) mathematical model of quarter car along with passenger and four degree of freedom half car is modelled and simulated. The PSOBELBIC gives the best performance with overall improvement of about 26% and 32.54% for speed bumps and 52.79%, 34.58% against random road.
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来源期刊
International Journal of Vehicle Performance
International Journal of Vehicle Performance Engineering-Safety, Risk, Reliability and Quality
CiteScore
2.20
自引率
0.00%
发文量
30
期刊最新文献
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