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基于多模型迭代的車(chē)輛狀態(tài)融合估計(jì)方法
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國(guó)家自然科學(xué)基金重點(diǎn)項(xiàng)目(U1564201、U1664258)、江蘇省“六大人才高峰”項(xiàng)目(2014-JXQC-004)、江蘇省“333”工程項(xiàng)目(BRA2016445)、江蘇省重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(產(chǎn)業(yè)前瞻與共性關(guān)鍵技術(shù))(BE2016149)和江蘇省高校自然科學(xué)基金項(xiàng)目(16KJB580012)


Vehicle State Fusion Estimation Method Based on Multi-model Iteration
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    摘要:

    為了提高車(chē)輛行駛狀態(tài)估計(jì)的可靠性,提出一種基于多模型觀測(cè)器誤差補(bǔ)償與迭代的車(chē)輛狀態(tài)融合估計(jì)方法?;谌杂啥溶?chē)輛動(dòng)力學(xué)模型設(shè)計(jì)了車(chē)輛狀態(tài)強(qiáng)跟蹤濾波估計(jì)算法;同時(shí),根據(jù)四輪輪速耦合關(guān)系,考慮到數(shù)據(jù)擾動(dòng)和病態(tài)矩陣的影響,設(shè)計(jì)了車(chē)輛狀態(tài)的嶺估計(jì)算法。為進(jìn)一步提高估計(jì)系統(tǒng)的可靠性,提出了動(dòng)力學(xué)模型觀測(cè)器與運(yùn)動(dòng)學(xué)模型觀測(cè)器補(bǔ)償與迭代的估計(jì)方式,設(shè)計(jì)了模糊控制器,根據(jù)實(shí)時(shí)的質(zhì)心側(cè)偏角和滑移率的偽量測(cè)值,判斷強(qiáng)跟蹤濾波器和嶺估計(jì)器估計(jì)結(jié)果所占權(quán)重,利用閉環(huán)估計(jì)系統(tǒng)的迭代與融合提高估計(jì)性能。仿真和道路實(shí)驗(yàn)結(jié)果表明,所提出的車(chē)輛狀態(tài)融合估計(jì)方法能夠兼顧強(qiáng)跟蹤濾波算法與嶺估計(jì)算法的優(yōu)勢(shì),根據(jù)車(chē)輛縱向滑移和質(zhì)心側(cè)偏角動(dòng)態(tài)調(diào)節(jié)強(qiáng)跟蹤估計(jì)與嶺估計(jì)結(jié)果的權(quán)重系數(shù),從而在保證估計(jì)精度的同時(shí)提高了估計(jì)系統(tǒng)的多工況適應(yīng)能力。

    Abstract:

    In order to improve the reliability of vehicle running state estimation, a vehicle state fusion estimation method based on multi-model observer error compensation and iteration was proposed. A strong-tracking filter estimation algorithm was presented for vehicle state estimation based on three-degree-of-freedom vehicle dynamics model, meanwhile, using the coupling relationship of four wheel speed, a ridge estimation algorithm for vehicle state estimation was designed considering the influence of data disturbance and ill-conditioned matrix. To further improve the reliability of estimation system, an estimation strategy with the error compensation and iteration between the dynamic-model-based observer and the kinematic-model-based observer was developed, a fuzzy controller was designed which was used to judge the weight of strong tracking filter and ridge estimator according to the real-time pseudo measurement value of sideslip angle and longitudinal slip rate, and then estimation performance was improved by iteration and fusion of the closed-loop estimation system. The results of the simulation and road test showed that the proposed vehicle state fusion estimation method can integrate the advantages of strong tracking filter algorithm and ridge estimation algorithm, dynamically adjust the weight coefficients of strong tracking filter and ridge estimation results according to the vehicle longitudinal slip ratio and sideslip angle, guarantee the estimation accuracy and synchronously improve the adaptability of the estimation system under multiple conditions.

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陳特,陳龍,蔡英鳳,徐興,江浩斌.基于多模型迭代的車(chē)輛狀態(tài)融合估計(jì)方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2018,49(6):385-392. CHEN Te, CHEN Long, CAI Yingfeng, XU Xing, JIANG Haobi. Vehicle State Fusion Estimation Method Based on Multi-model Iteration[J]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(6):385-392.

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  • 收稿日期:2017-12-18
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  • 在線發(fā)布日期: 2018-06-10
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