挖掘臂动力学参数与液压缸摩擦力参数辨识

建立了挖掘臂单关节动力学模型及液压缸驱动力模型,将模型中的未知动力学参数及非线性摩擦力参数线性化表示。利用测量的系统压力及角度信息,分别采用带遗忘因子的递推最小二乘法及递推增广最小二乘法对系统参数进行辨识。对辨识所得的两个模型进行仿真,与实际系统对比分析结果表明,辨识模型能很好地逼近实际系统。误差对比分析结果表明:递推增广最小二乘法比带遗忘因子的递推最小二乘法误差减小约28%,对系统噪声有更好的鲁棒性。     

自适应卡尔曼滤波在无刷直流电机系统辨识中的应用

为了有效抑制量测噪声特性变化对系统辨识精度的影响以获得准确的无刷直流电机模型,提出了一种采用自适应卡尔曼滤波算法的无刷直流电机系统辨识方法。通过计算新息理论方差的极大似然最优估计,并将其引入卡尔曼滤波算法中修正滤波增益来抑制量测噪声特性变化对辨识结果的影响,使该滤波算法实现对模型参数的准确估计,提高辨识精度。实验结果表明,在量测噪声特性变化的情况下,该算法能够准确跟踪实际量测噪声特性的变化,参数估计平滑,相对于目前系统辨识广泛采用的带有遗忘因子的递推最小二乘算法,输出误差的均方根值减小了73.5%。该算法简单易行,计算量小,辨识结果可以很好地描述系统行为,便于在工程实践中应用。     

压电执行器的Bouc-Wen模型在线参数辨识

现有的定参数Bouc-Wen模型由于无法表征压电执行器迟滞具有的频移和时变性,极易产生较大的模拟误差。为了精确地模拟压电执行器的迟滞特性,本文建立了压电执行器的Bouc-Wen模型,并采用递推最小二乘在线辨识方法来实时辨识Bouc-Wen模型的参数。为了避免出现数据饱和现象,使用限定记忆来限定辨识方法所使用的数据组数。为验证该辨识方法的有效性,建立了相应的实验系统对其进行实验验证。实验结果表明,限定记忆递推最小二乘在线辨识方法能使Bouc-Wen模型也呈现频移和时变特性。以100 Hz的驱动电压为例,其最大绝对模拟误差从1.38μm降为0.51μm。因此,与传统的离线参数辨识方法相比,限定记忆递推最小二乘在线辨识方法能够有效地提高Bouc-Wen模型的模拟精度。     

基于实时反馈的机床热误差在线补偿模型

为建立一种能够适应机床不同工况且具有准确预测能力的热误差补偿模型,提出一种基于限定记忆递推最小二乘法辨识热误差模型参数的机床热误差预测建模方法。该方法随着机床工作状况的改变,根据实时反馈的温度和热误差数据,采用递推方法对模型参数进行即时修正,使热误差模型能够及时跟踪机床系统的热特性变化,实现以较高的预测精度对机床热误差进行补偿。通过数控车床主轴轴向热误差辨识建模及补偿实验可以看出,限定记忆递推最小二乘法比一步最小二乘法辨识精度有较大提高,最大残差值减小了52.3%,标准差减小了67%。实验结果表明,利用该方法进行机床热误差模型参数辨识具有较高的预测精度和鲁棒性,有效可行。       

采用L-M算法对变频器干扰环境下电液控制系统的参数辨识研究

针对变频器电磁干扰环境下,对存在纯延迟的电液控制系统参数辨识时,采用普通最小二乘方法精度不够、效率低等问题,提出采用L-M(Levenbreg-Marquardt)算法对系统参数进行辨识的方法,在延迟时间已知的条件下建立了电液控制系统的可辨识离散时域差分方程模型,在系统输出信号含有现场真实变频器电磁干扰时,分别采用L-M算法和普通最小二乘算法对系统参数进行了辨识,仿真结果表明,前者在参数辨识精度上比后者高5.43%,辨识速度快46.4%。     

动压缸电液伺服压力系统自适应差分进化辨识

根据轨道路基测试装置工作原理,建立了动压缸电液伺服压力系统AMESim模型,理论推导出该系统传递函数。针对标准差分进化算法早熟问题,构造了一种可以自动调节变异因子、变异算子和交叉因子的自适应差分进化算法。设计了基于该系统AMESim模型的参数辨识方案,进行了自适应差分进化算法与其他算法的对比仿真,验证了该算法具有良好的辨识精度和收敛性,给出了动压缸负载开环传递函数辨识参数,并通过自适应差分进化算法获得了伺服阀系统开环传递函数辨识参数。最后给出了动压缸电液伺服压力系统传函参数,通过与该系统AMESim模型对比仿真,验证了该辨识参数的有效性。     

一种基于最小二乘的锅炉汽包水位控制递推辨识算法研究

传统锅炉汽包水位采用常规PID控制,其控制参数是固定不变的,控制效果往往难以满足要求,会造成系统不稳定甚至失控。现讨论基于最小二乘的递推辨识算法,能在线估计系统模型参数,根据不同工况,实时跟踪参数。给出了两种算法的数值仿真,仿真结果表明,与传统PID控制算法相比,最小二乘递推辨识算法超调小,响应速度快,具有较好的控制效果。     

基于最小二乘模型的Bayes参数辨识方法

现实中的系统都具有一定的非线性,并且这种非线性在非线性通道补偿和非线性系统故障诊断等领域是不可忽略的。针对有白噪声干扰的输出误差非线性系统,将数学模型与基于最小二乘的Bayes算法相结合,用数学模型参数代替辨识模型信息向量中的未知项,用基于白噪声的最小二乘模型进行不可预测辨识,从而提出了基于最小二乘模型的Bayes参数辨识方法。介绍了Bayes基本原理及2种常用的方法,经过理论分析和MATLAB仿真研究证明,该方法原理简单、计算量小、速度快、抗干扰能力强,可以对较高精度非线性系统进行参数估计和在线辨识。     

小波神经网络用于光纤陀螺漂移误差辨识

提出了采用小波消噪和小波神经网络两个模型对光纤陀螺漂移误差进行辨识。应用小波分析方法消除高频噪声,改善信噪比,把消噪信号作为神经网络期望输出,然后采用带遗忘因子的递推最小二乘 (DRLS) 算法训练网络并调整权值。该算法不进行任何矩阵运算,在保持收敛速度快和精度高的前提下,极大地减少了计算量,提高了小波神经网络的实时性能,仿真结果表明辨识误差在1.5%以内。     

基于递推最小二乘算法的模糊系统在车削工件直径误差预测中的应用

根据车削过程中工件直径误差的特点,采用基于递推最小二乘算法的模糊系统,预测车削过程中由弹性变形等因素引起的工件直径误差,通过递推最小二乘算法训练Mamdani型模糊系统,以确定合理的系统参数.根据工件直径误差与切削深度、进给量等的关系,设计车削实验,得到训练数据和测试数据,用训练数据训练模糊系统,进而用测试数据测试,误差较小,从而验证在一定的工件结构和工况条件下,用基于递推最小二乘算法的Mamdani型模糊系统进行车削工件直径误差预测的可行性.与回归分析进行比较,结果显示在一定的工件结构和工况条件下,基于递推最小二乘算法的Mandani型模糊系统对于预测车削工件直径误差有比较明显的效果.     

A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used.     

A fast method for online closed-loop system identification

In the modern control schemes broadly applied presently in the servo drive system of machine tools, the sampling frequency has been growing larger and larger becomes higher and higher, so it is important to keep up-to-date with the variance of the actual system parameters. As a solution to the problem, a novel method developed from the recursive extended least squares (RELS) algorithm in terms of the computation of functions, operates in such a way that the parameters of the system model are revised only when several proper new groups of data are obtained. The simulation and experimentation of online direct closed-loop system identification indicate that, by selecting the updating step, this method is able to effectively cut down the identifying cost time while obtaining satisfactory accuracy of estimation.     

机械振动结构的ARMRX建模及模态参数识别

由振动系统的运动方程建立了描述系统输入、输出和噪声特性的受控自回归滑动平均模型(ARMAX)。采用基于限定记忆的增广最小二乘算法估计其参数,克服了以往传统方法计算的复杂性,以及难以在线的缺点。仿真结果表明本文方法鲁棒性较好。     

A comparative study of adaptation algorithms for nonlinear system identification based on second order Volterra and bilinear polynomial filters

Nonlinear filtering techniques have recently become very popular in the field of signal processing. In this study we have considered the modeling of nonlinear systems using adaptive nonlinear Volterra filters and bilinear polynomial filters. The performance evaluation of these nonlinear filter models for the problem of nonlinear system identification has been carried out for several random input excitations and for measurement noise corrupted output signals. The coefficients of the two candidate filter models for are designed using several well known adaptive algorithms, such as least mean squares (LMS), recursive least squares (RLS), least mean p-norm (LMP), normalized LMP (NLMP), least mean absolute deviation (LMAD) and normalized LMAD (NLMAD) algorithms. Detailed simulation studies have been carried out for comparative analysis of Volterra model and bilinear polynomial filter, using these candidate adaptation algorithms, for system identification tasks and the superior solutions are determined.     

A novel composite adaptive flap controller design by a high-efficient modified differential evolution identification approach

The optimal tuning of adaptive flap controller can improve adaptive flap control performance on uncertain operating environments, but the optimization process is usually time-consuming and it is difficult to design proper optimal tuning strategy for the flap control system (FCS). To solve this problem, a novel adaptive flap controller is designed based on a high-efficient differential evolution (DE) identification technique and composite adaptive internal model control (CAIMC) strategy. The optimal tuning can be easily obtained by DE identified inverse of the FCS via CAIMC structure. To achieve fast tuning, a high-efficient modified adaptive DE algorithm is proposed with new mutant operator and varying range adaptive mechanism for the FCS identification. A tradeoff between optimized adaptive flap control and low computation cost is successfully achieved by proposed controller. Simulation results show the robustness of proposed method and its superiority to conventional adaptive IMC (AIMC) flap controller and the CAIMC flap controllers using other DE algorithms on various uncertain operating conditions. The high computation efficiency of proposed controller is also verified based on the computation time on those operating cases.     

On-line identification and control of pneumatic servo drives via a mixed-reality environment

This paper presents a method to identify and control electro-pneumatic servo drives in a real-time environment. Acquiring the system’s transfer function accurately can be difficult for nonlinear systems. This causes a great difficulty in servo-pneumatic system modeling and control. In order to avoid the complexity associated with nonlinear system modeling, a mixed-reality environment (MRE) is employed to identify the transfer function of the system using a recursive least squares (RLS) algorithm based on the auto-regressive moving-average (ARMA) model. On-line system identification can be conducted effectively and efficiently using the proposed method. The advantages of the proposed method include high accuracy in the identified system, low cost, and time reduction in tuning the controller parameters. Furthermore, the proposed method allows for on-line system control using different control schemes. The results obtained from the on-line experimental measured data are used to determine a discrete transfer function of the system. The best performance results are obtained using a fourth-order model with one-step prediction.     

基于遗传算法的挠性转子系统的参数识别

介绍了在外电磁激励力务件下的转子不平衡参数识别方程模型，并提出基于遗传算法的参数优化识别方法。并根据识别方程，把不平衡力和轴承特性参数作为输入量，测点处振动响应为输出量，以输出振动响应与实测振动响应的误差最小为优化目标，通过使用遗传算法来识别不平衡力和轴承特性参数。实现在线无试重动平衡。     

Unbalance vibratory displacement compensation for active magnetic bearings

As the dynamic stiffness of radial magnetic bearings is not big enough,when the rotor spins at high speed,unbalance displacement vibration phenomenon will be produced.The most effective way for reducing the displacement vibration is to enhance the radial magnetic bearing stiffness through increasing the control currents,but the suitable control currents are not easy to be provided,especially,to be provided in real time.To implement real time unbalance displacement vibration compensation,through analyzing active magnetic bearings(AMB) mathematical model,the existence of radial displacement runout is demonstrated.To restrain the runout,a new control scheme-adaptive iterative learning control(AILC) is proposed in view of rotor frequency periodic uncertainties during the startup process.The previous error signal is added into AILC learning law to enhance the convergence speed,and an impacting factor influenced by the rotor rotating frequency is introduced as learning output coefficient to improve the rotor control effects.As a feed-forward compensation controller,AILC can provide one unknown and perfect compensatory signal to make the rotor rotate around its geometric axis through power amplifier and radial magnetic bearings.To improve AMB closed-loop control system robust stability,one kind of incomplete differential PID feedback controller is adopted.The correctness of the AILC algorithm is validated by the simulation of AMB mathematical model adding AILC compensation algorithm through MATLAB soft.And the compensation for fixed rotational frequency is implemented in the actual AMB system.The simulation and experiment results show that the compensation scheme based on AILC algorithm as feed-forward compensation and PID algorithm as close-loop control can realize AMB system displacement minimum compensation at one fixed frequency,and improve the stability of the control system.The proposed research provides a new adaptive iterative learning control algorithm and control strategy for AMB displacement minimum compensation,and provides some references for time-varied displacement minimum compensation.     

基于加权排列熵和DE-ELM的滚动轴承故障诊断

针对滚动轴承振动信号非平稳非线性的特征,提出一种基于加权排列熵和差分进化算法优化极限学习机(DE-ELM)的滚动轴承故障诊断方法。首先利用自适应噪声的完全集合经验模态分解处理轴承振动信号得到固有模态函数(IMF),然后计算主要IMF分量的加权排列熵组成故障特征向量,最后利用差分优化算法(DE)优化极限学习机隐含层输入权值和偏置,并将故障特征向量作为DE-ELM的输入。实验证明,加权排列熵能够精确提取故障特征,DE-ELM算法能有效提高故障分类精度。与多种方法相比,该方法更加准确可靠。     

功能梯度材料的低温挤压成形过程建模与实验研究

使用低温挤压成形方法制造功能梯度材料（FGM）过程中,膏体材料存在气泡、结块和一定程度的液相迁移现象,导致挤压成形过程难以控制。使用系统辨识的方法建立了FGM成形过程的差分模型,运用残差的方差分析方法对系统模型的阶次进行估计,运用递推最小二乘法对参数进行辨识,得到FGM成形的控制模型。设计了自适应控制器,对FGM成形过程进行实时控制。实验验证了控制模型的准确性和控制方法的有效性。