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耦合神經(jīng)網(wǎng)絡(luò)輪胎模型EPS自適應(yīng)控制
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國(guó)家自然科學(xué)基金資助項(xiàng)目(50875112)、江蘇省自然科學(xué)基金資助項(xiàng)目(BK2010337)、江蘇省高校自然科學(xué)基金資助項(xiàng)目(09KJA580001)和高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金資助項(xiàng)目(20093227110013、20103227120011)


Adaptive Control of EPS of Tyre Model Based on Neural Network
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    摘要:

    建立了基于神經(jīng)網(wǎng)絡(luò)的輪胎模型。同時(shí)在EPS工況分類的基礎(chǔ)上建立了工況推理準(zhǔn)則，滿足準(zhǔn)確的判斷要求，進(jìn)而根據(jù)EPS所處的不同工況更改模糊PID的模糊規(guī)則，實(shí)現(xiàn)了多種助力特性的自適應(yīng)切換。建立了整車多體動(dòng)力學(xué)模型，進(jìn)行了蛇形道路下的SIMPACK/Matlab聯(lián)合仿真,并與基于dSPACE平臺(tái)的實(shí)車試驗(yàn)進(jìn)行對(duì)比分析。結(jié)果表明，基于該模型設(shè)計(jì)的控制策略可以有效降低駕駛員的操縱轉(zhuǎn)矩和提高車輛的回正性能。

    Abstract:

    The tyre model based on neural network was built. Inference rules of working condition were established based on the classification of electric power steering (EPS) conditions, which could lead to a correct judgment. Furthermore, according to the different working conditions of EPS, the fuzzy rules of fuzzy PID were changed to achieve active switching of multi-assist characteristics. The whole multi-body dynamic vehicle model was built. The SIMPACK-Matlab co-simulation was carried out on the snakelike road, and then compared with the car test based on dSPACE. The simulation result showed that the control strategy based on the proposed model could effectively reduce the driver’s operating torque and improve the returnability of vehicle. 

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黃晨,陳龍,江浩斌,王志忠,夏天.耦合神經(jīng)網(wǎng)絡(luò)輪胎模型EPS自適應(yīng)控制[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2013,44(10):47-51. Huang Chen, Chen Long, Jiang Haobin, Wang Zhizhong, Xia Tian. Adaptive Control of EPS of Tyre Model Based on Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(10):47-51.

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  • 在線發(fā)布日期: 2013-10-14
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