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基于粒子群算法的汽車自適應(yīng)巡航控制器設(shè)計(jì)
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國家自然科學(xué)基金資助項(xiàng)目（50505015）


Design of Vehicle Adaptive Cruise Controller Based on PSO Algorithm
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    摘要:

    提出了一種基于粒子群優(yōu)化算法的模糊自校正控制器參數(shù)優(yōu)化方法,?；诖罱ǖ腃arsim和Simulink聯(lián)合仿真環(huán)境，選取典型優(yōu)化工況,，利用粒子群優(yōu)化算法對(duì)控制器比例因子和隸屬度函數(shù)形狀參數(shù)在取值區(qū)間內(nèi)多次隨機(jī)選值,，并重構(gòu)控制器，發(fā)揮算法本身具有的記憶最佳取值點(diǎn)和各點(diǎn)間相互對(duì)比機(jī)制,，以跟蹤目標(biāo)函數(shù)為最優(yōu)，實(shí)現(xiàn)對(duì)控制器性能優(yōu)化問題的求解,。通過典型工況仿真和實(shí)車試驗(yàn)結(jié)果表明,，該方法優(yōu)化后的控制器具有更優(yōu)良的控制性能，可有效降低自適應(yīng)巡航系統(tǒng)與整車的性能匹配設(shè)計(jì)工作量,，為模糊控制器的參數(shù)確定提出了一套可行的研究途徑,。

    Abstract:

    Based on particle swarm optimization (PSO) algorithm, a fuzzy self-tuning controller parameters optimization method was developed. With the co-simulation of Carsim and Simulink, typical optimized working conditions were selected. The controller’s scaling factor value and the position of membership function shape points were randomly selected to ensure the controller optimal performance. The controller was also reconstructed. Optimum remembering points and contrast mechanism among these points were working in PSO algorithm with the optimum target function. The actual vehicle experiments were carried out under typical working conditions. The experiment results showed that the optimized controller had good control performance, which could decrease the design workload of performance matching between the adaptive cruise control and test vehicle. 

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高振海,吳濤,尤洋.基于粒子群算法的汽車自適應(yīng)巡航控制器設(shè)計(jì)[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2013,44(12):11-16. Gao Zhenhai, Wu Tao, You Yang. Design of Vehicle Adaptive Cruise Controller Based on PSO Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(12):11-16.

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  • 在線發(fā)布日期: 2013-12-05
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