光学精密工程
光學精密工程
광학정밀공정
OPTICS AND PRECISION ENGINEERING
2009年
10期
2600-2605
,共6页
非线性Volterra模型%自适应辨识%快速算法
非線性Volterra模型%自適應辨識%快速算法
비선성Volterra모형%자괄응변식%쾌속산법
nonlinear Volterra model%adaptive%identification%fast algorithm
在RFID倒扣封装设备研制中,高速倒扣机械手具有很强的非线性和时变特性,线性控制方法难以满足要求,因此本文提出了一种快速辨识算法,采用三阶非线性Volterra模型对机械手进行在线实时辨识.首先,利用不同阶输入向量的结构关系,由低阶输入向量直接构建高阶输入向量.接着,根据不同阶核的相关性从低阶核加速估计高阶核.最后,把线性变步长LMS方法引入到非线性自适应算法中,并用Lyapunov全局稳定理论进行证明.对实际系统的辨识实验表明:与常规方法比较,辨识时间从100 ms缩短为30 ms,辨识速度提高了3.3倍,辨识失调降低了93.3%,同时还具有更高的辨识精度,满足了对非线性系统辨识的精度要求和实时性要求.
在RFID倒釦封裝設備研製中,高速倒釦機械手具有很彊的非線性和時變特性,線性控製方法難以滿足要求,因此本文提齣瞭一種快速辨識算法,採用三階非線性Volterra模型對機械手進行在線實時辨識.首先,利用不同階輸入嚮量的結構關繫,由低階輸入嚮量直接構建高階輸入嚮量.接著,根據不同階覈的相關性從低階覈加速估計高階覈.最後,把線性變步長LMS方法引入到非線性自適應算法中,併用Lyapunov全跼穩定理論進行證明.對實際繫統的辨識實驗錶明:與常規方法比較,辨識時間從100 ms縮短為30 ms,辨識速度提高瞭3.3倍,辨識失調降低瞭93.3%,同時還具有更高的辨識精度,滿足瞭對非線性繫統辨識的精度要求和實時性要求.
재RFID도구봉장설비연제중,고속도구궤계수구유흔강적비선성화시변특성,선성공제방법난이만족요구,인차본문제출료일충쾌속변식산법,채용삼계비선성Volterra모형대궤계수진행재선실시변식.수선,이용불동계수입향량적결구관계,유저계수입향량직접구건고계수입향량.접착,근거불동계핵적상관성종저계핵가속고계고계핵.최후,파선성변보장LMS방법인입도비선성자괄응산법중,병용Lyapunov전국은정이론진행증명.대실제계통적변식실험표명:여상규방법비교,변식시간종100 ms축단위30 ms,변식속도제고료3.3배,변식실조강저료93.3%,동시환구유경고적변식정도,만족료대비선성계통변식적정도요구화실시성요구.
As part of the RFID flip chip package development,the high speed manipulator has obvious nonlinear and time-variable characters,so a nonlinear adaptive inverse control is needed.The key to this method is to identify the high speed manipulator by using a third-order Volterra nonlinear model in limited time and with sufficient accuracy.However,it is hard to satisfy real-time requirement with a conventional method.This paper proposes a fast identification algorithm to resolve the problem.Firstly,a high-order input vector is constructed from a low-order input vector according to the structural character.Next,it speeds up the estimates of high-order kernels based on low-order kernels according to their correlation.Finally,it uses a linear variable step-size LMS strategy in a nonlinear algorithm and proves convergence with the Lyapunov global stability theorem.In experiments with a manipulator based on conventional and proposed methods,respectively,the results show that this algorithm reduces the identification time from 100 ms to 30 ms,improves convergent speed 3.3 times and reduces misadjustment by 93.3%,as well as having great precision.It can satisfy both require-merits of real-time and identification precision.