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基于RBF神經(jīng)網(wǎng)絡(luò)的混合輸入機(jī)構(gòu)自適應(yīng)控制
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of Hybrid Machine Based on RBF Neural Network
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    摘要:

    提出一種伺服電動(dòng)機(jī)對(duì)常速電動(dòng)機(jī)運(yùn)動(dòng)進(jìn)行閉環(huán)跟蹤的控制策略,,控制伺服電動(dòng)機(jī)的運(yùn)動(dòng),以實(shí)現(xiàn)對(duì)常速電動(dòng)機(jī)速度波動(dòng)的補(bǔ)償。由于系統(tǒng)精確模型難以獲得,設(shè)計(jì)了基于名義模型的徑向基函數(shù)網(wǎng)絡(luò)自適應(yīng)控制器,進(jìn)行混合輸入機(jī)構(gòu)軌跡的跟蹤,,應(yīng)用徑向基函數(shù)(RBF)神經(jīng)網(wǎng)絡(luò)對(duì)系統(tǒng)中摩擦、外部擾動(dòng)和動(dòng)力耦合等不確定因素的和進(jìn)行逼近,網(wǎng)絡(luò)輸出權(quán)值由自適應(yīng)算法學(xué)習(xí)確定,,并對(duì)該控制器進(jìn)行穩(wěn)定性分析。仿真結(jié)果表明,,所設(shè)計(jì)的控制器穩(wěn)定有效, 具有較強(qiáng)的魯棒性,。

    Abstract:

    The precision of trajectory tracking of hybrid machine was debased because of velocity fluctuation in constant velocity motor and uncertainties in the system. The strategy for control servo motor grounded on tracking the motion was proposed, to compensate the velocity fluctuation of constant velocity motor. Considering the uncertainties of the system, an adaptive sliding mode controller based on RBF neural network was designed for trajectory tracking of hybrid machine. The sum of uncertainties of the system was compensated by control gain with RBF neural network, and the output weight of in network was adjusted by adaptive algorithm. The simulation result indicated the robustness and effectiveness of the controller.

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李仁軍,劉宏昭,原大寧.基于RBF神經(jīng)網(wǎng)絡(luò)的混合輸入機(jī)構(gòu)自適應(yīng)控制[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2010,41(4):204-208. of Hybrid Machine Based on RBF Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(4):204-208.

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