【摘要】：大型模鍛成形過(guò)程是一個(gè)復(fù)雜的非線(xiàn)性時(shí)變過(guò)程,包括鍛件流變成形過(guò)程與液壓系統(tǒng)驅(qū)動(dòng)過(guò)程,以及還存在油液泄漏等眾多不確定性因素,導(dǎo)致精準(zhǔn)鍛造過(guò)程控制異常困難。為此,在結(jié)合基于機(jī)理模型控制與數(shù)據(jù)控制優(yōu)點(diǎn)的基礎(chǔ)上,提出了基于物理模型結(jié)合在線(xiàn)順序極限學(xué)習(xí)機(jī)的智能控制方法。該方法首先使用已知的系統(tǒng)信息推導(dǎo)出名義控制律;其次,針對(duì)模型不確定性部分,使用在線(xiàn)順序極限學(xué)習(xí)機(jī)設(shè)計(jì)出該在線(xiàn)模型的補(bǔ)償控制律;最后,建立了基于機(jī)理模型與數(shù)據(jù)模型的集成控制器,獲得了最佳控制律。仿真結(jié)果表明,新方法能有效地控制復(fù)雜的鍛造過(guò)程,且比現(xiàn)有的方法有更好的控制精度。
[Abstract]:Large-scale die forging process is a complex nonlinear time-varying process, including forging rheological forming process and hydraulic system driving process, as well as the existence of oil leakage and many other uncertain factors, resulting in the precision forging process control is extremely difficult. Based on the advantages of mechanism-based model control and data control, an intelligent control method based on physical model and on-line sequential learning machine is proposed. Firstly, the nominal control law is derived by using the known system information, secondly, the compensation control law of the online model is designed by using the on-line sequential limit learning machine for the uncertain part of the model. Finally, an integrated controller based on mechanism model and data model is established, and the optimal control law is obtained. The simulation results show that the new method can effectively control the complex forging process and has better control precision than the existing methods.
【作者單位】： 中南大學(xué)機(jī)電工程學(xué)院高性能復(fù)雜制造國(guó)家重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家重點(diǎn)基礎(chǔ)研究發(fā)展計(jì)劃(“973”計(jì)劃)項(xiàng)目(2011CB706802) 國(guó)家自然科學(xué)基金資助項(xiàng)目(51205420) 新世紀(jì)人才計(jì)劃基金(NCET-13-0593) 湖南省自然科學(xué)基金資助項(xiàng)目(14JJ3011)
【分類(lèi)號(hào)】：TG316.3
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本文編號(hào)：2375454