分轴燃气轮机辨识结果的拟合与分析

本文对分轴燃气轮机通过闭环辨识(用伪随机二进制信号)得到开环特性的可辨识问题作了分析,指出若在反馈处输入伪随机信号,可得到令人满意的辨识结果。在此基础上,拟合出了喷油量和压气机转速之间,喷油量和压气机出口压力之间在三个运行工况下的传递函数。结果表明,机组在不同工况具有不同的动态特性,且与压气机特性曲线上的(?G_e)/(?n_1)值有关。所得的拟合结果与理论模型有很好的吻合,对建立分轴燃气轮机的简化数学模型有一定的参考价值。     

基于粒子群算法的音圈电机参数辨识

在LED焊线机系统的基础上,分析并推导音圈电机的数学模型。在音圈电机的开环模式下,实验并采集数据。采用粒子群算法处理数据,从而得到音圈电机的各个参数。将实验数据与仿真数据对比,验证了模型的正确性。再将辨识的电机模型代入到闭环模式下,将仿真数据与实验数据对比,也验证了模型的正确性。     

基于BJ辅助变量法实现动力调谐陀螺仪的闭环辨识

为了在闭环工作条件下监测动力调谐陀螺仪的性能,研究了动力调谐陀螺仪的闭环辨识方法。首先,根据动力调谐陀螺仪闭环模型的复杂结构,对其进行化简和离散化处理,获取辨识模型集和模型阶数。接着,将辅助变量法(Ⅳ)应用于动力调谐陀螺仪的闭环辨识,并根据传统Ⅳ法的不足,提出了BJIV辨识法。该方法能够应用于BJ模型,使系统模型与噪声模型不再受辨识模型的限制。然后,利用仿真分析的方法,分析BJIV法辨识结果的无偏性与渐进离散特性。最后,采用提出的方法对某型号动力调谐陀螺仪进行单次和连续闭环辨识实验。仿真结果表明:BJIV法的辨识结果在有噪声存在的条件下是一致无偏的,渐进方差能够接近最优;闭环辨识实验结果表明:辨识拟合度优于90%,连续跟踪2h,获得了可靠的辨识结果。     

液压挖掘机振动掘削系统辨识模型及算法研究

介绍了液压挖掘机振动掘削的激振方式和实现机理.通过确立挖掘机铲斗与土壤的相互作用的动力学模型,再根据系统辨识理论,建立了振动掘削系统的输入输出数学模型;运用参数估计理论,主要探讨了不同辨识模型下最小二乘算法在参数辨识过程中运用.最后,通过MATLAB软件进行了仿真计算,并比较了不同算法的参数辨识效果.     

高速高精运动平台模型参数最小二乘辨识研究

针对高速高精运动控制对象模型参数辨识的问题,对辨识对象模型、最小二乘辨识算法和滤波器等内容进行了研究,提出了一种基于滤波器的最小二乘线性回归方程的辨识算法,即加入一个稳定的二阶传递函数,对系统输入输出数据进行了滤波处理,使辨识算法在噪声环境下能够减小失真且运行稳定.仿真与实验结果表明,最小二乘在有噪声的闭环运动控制系统中的辨识结果不收敛且失真,而加入滤波器的最小二乘算法能显著提高控制系统的稳定性及精确度,且满足其高速高精的要求.     

限定记忆最小二乘辨识与非对称缸液压系统

针对非对称液压缸系统,以PRMS信号为激励信号,对比了普通最小二乘法与限定记忆最小二乘法在开环辨识中的辨识效果.并将限定记忆最小二乘辨识方法所得的辨识模型同实际系统的输出进行了对比和验证,证实了限定记忆最小二乘辨识方法的有效性.     

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PMLSM的速度辨识控制方法

针对永磁同步直线电机的矢量控制问题,提出了基于模型参考自适应的速度辨识方法。通过分析永磁同步直线电机的数学模型,设计了并联模型参考自适应模型,并利用Popov超稳定性定理推导了模型参考自适应律,得到了速度辨识自适应算法,实现了对电机速度和位置的辨识。将速度辨识算法嵌入永磁同步直线电机的双闭环矢量控制模型中,建立了基于模型参考自适应速度辨识的双闭环矢量控制系统。在MATLAB中对电机及其控制系统进行建模,并在负载突变和速度突变两种工况条件下对系统模型进行了仿真,仿真结果表明,该方法能实现对速度和位置的高精度估算,稳定性较好。     

基于闭环自适应辨识的速度环PI参数自整定

针对永磁同步电机伺服系统速度环比例积分（PI）参数整定过程中需要反复调节、效率低等问题,提出了一种基于闭环自适应卡尔曼滤波（AKF）系统辨识的伺服系统速度环PI参数自整定方法。首先根据输入信号激励速度闭环系统,分析不同频率激励作用下闭环辨识序列的信噪比与实际输出,然后引入AKF算法辨识闭环被控对象的离散模型,最后通过遗传算法仿真搜索最优速度环PI参数。仿真与实验结果表明：该算法能有效抑制量测噪声等扰动对系统辨识精度的影响,辨识结果能够反映实际系统的动态输入输出特性,优化后的速度环具有优良的响应性能和较高的精度,便于实际工业应用。     

线性系统辨识与仿真方法探讨

线性系统辨识的目的是确定对象系统的状态与参数，仿真是根据确定的状态与参数构成一个等于对象系统的仿真模型。本文讨论的是模型的参数估计与仿真。参数估计有许多方法，本文针对最小二乘法在线性系统参数估计上的应用进行了探讨，并编制了线性系统辨识和仿真软件，该软件在系统辨识和仿真方面有实用意义。     

一种相位相关的闭环频域辨识方法及其在最优PI控制器整定中的应用

最优PI控制器整定需要传递函数模型,且强调模型-90°在相位处的准确性。满足这种要求的模型化方法是应用PI控制器最优整定的关键步骤。为此,本文提出了一种基于双通道继电测试TRFT的相位相关闭环频域辨识策略。在该方法中,首先通过TRFT以闭环方式提取在指定相位的多点频率特性;其次,采用所提出的RNLS频域辨识算法估计过程对象的带有时滞的传递函数模型。最后的仿真取得了令人满意的结果,验证了所提出方法的有效性。本文所提出的建模方法与最优整定相结合构成了一个完整的工业应用框架。     

On-line identification of cascade control systems based on half limit cycle data

An on-line identification procedure is presented for cascade control systems in which both inner and outer loop process dynamics are modelled simultaneously by performing a single experiment. Departing from the conventional relay autotuning method where the controller is replaced by a relay, the proposed method is carried out on-line without breaking the closed-loop control. Exact analytical expressions are derived for process model parameters in terms of a few critical parameters of half period data of limit cycle output. Simulation examples are included to demonstrate the effectiveness of the proposed method.     

基于RBF神经网络的风力发电机组系统辨识研究

Aiming at the problems of difficult to establish the accurate mathematical model of wind power generation,identification of the wind turbine based on RBF neural network was presented. The dynamic process of the torque loop and the pitch loop was simulated,RBF neural network algorithm was adopted to identification the torque loop and the pitch loop. RBF basis function was adopted to form space. If the hidden layer RBF parameters was determined, the nonlinear mapping relation was determined. The output layer was the hidden layer nodes output linear weighted summation. The result indicate that identification of torque loop,the input is torque,the output is speed,the torque loop error rate is about 1 %. Identification of pitch loop,the input is pitch angle,the output is speed,the pitch loop eror rate is about 3%. The pitch loop is a very complicated nonlinear model,the model structure is influenced by many aspects,identification result error is bigger than the torque loop identification error,but the error rate is allowed. The algorithm has higher precision and efficiency. [ABSTRACT FROM AUTHOR]     

Real-time tracking control of electro-hydraulic force servo systems using offline feedback control and adaptive control

This paper focuses on an application of an electro-hydraulic force tracking controller combined with an offline designed feedback controller (ODFC) and an online adaptive compensator in order to improve force tracking performance of an electro-hydraulic force servo system (EHFS). A proportional-integral controller has been employed and a parameter-based force closed-loop transfer function of the EHFS is identified by a continuous system identification algorithm. By taking the identified system model as a nominal plant model, an H_∞ offline design method is employed to establish an optimized feedback controller with consideration of the performance, control efforts, and robustness of the EHFS. In order to overcome the disadvantage of the offline designed controller and cope with the varying dynamics of the EHFS, an online adaptive compensator with a normalized least-mean-square algorithm is cascaded to the force closed-loop system of the EHFS compensated by the ODFC. Some comparative experiments are carried out on a real-time EHFS using an xPC rapid prototype technology, and the proposed controller yields a better force tracking performance improvement.     

Dynamic modelling of planar mechanisms using point coordinates

In the present study, the dynamic modelling of planar mechanisms that consist of a system of rigid bodies is carried out using point coordiantes. The system of rigid bodies is replaced by a dynamically equivalent constrained system of particles. Then for the resulting equivalent system of particles, the concepts of linear and angular momentums are used to generate the equations of motion without either introducing any rotational coordinates or distributing the external forces and force couples over the particles. For the open loop case, the equations of motion are generated recursively along the open chains. For the closed loop case, the system is transformed to open loops by cutting suitable kinematic joints with the addition of cut-joints kinematic constraints. An example of a multi-branch closed-loop system is chosen to demonstrate the generality and simplicity of the proposed method.     

一种基于卡尔曼滤波的非线性模型在线模糊辨识算法

文章提出一种用于非线性模型在线辨识的模糊算法。该算法将非线性输入输出系统用时变线性系统模型来拟和，并把此非线性系统模型表示成模糊模型的形式，用在线调节模糊模型的方法来辨识时变线性模型的相关参数。本文将递推模糊聚类方法与卡尔曼滤波法用于在线调整模糊模型参数。仿真算例表明了此算法的有效性。     

Adaptive identification of hysteresis and creep in piezoelectric stack actuators

The adaptive identification of the non-linear hysteresis and creep effects in a piezoelectric actuator is proposed in this paper. Model uncertainties related to the hysteresis and creep effects, most prominently in the high frequency zone (to 100 Hz), large operating amplitude and/long operating time, can make a piezoelectric actuator-driven micro-positioning system unstable in the closed loop. Furthermore, these uncertainties may lead to inaccurate open-loop control and frequently cause harmonic distortion when a piezoelectric actuator is driven with a sinusoidal input voltage signal. In order to solve the above issues, it is important to determine an accurate non-linear dynamic model of a piezoelectric actuator. An unscented Kalman filter-based adaptive identification algorithm is presented, which accurately determines the non-linear dynamics of a piezoelectric stack type actuator such that the non-linear hysteresis and creep effects can be accurately predicted. Since hysteresis and creep are dominant in open loop, the actuator is driven in an open-loop mode in this investigation.     

基于 RP-EKF 的无人机动力系统参数辨识

针对无人机动力系统电池电压波动导致系统噪声大、辨识结果精度低的问题,本研究提出了一种基于反向预测-增广卡尔曼滤波(RP-EKF)的无人机动力系统参数辨识方法。首先构建增广参数矩阵,将压降噪声模型考虑入辨识环节,其次提出反向预测卡尔曼滤波算法,设定新息平方比阈值,计算原始预测新息平方与反向预测新息平方的比值,通过对比预测新息比与阈值完成过程噪声调整并实现估计模型修正。实验结果表明,本文提出的基于RP-EKF的参数辨识方法,平均误差为39.22 rpm,均方根误差为55.85 rpm,平均相对偏差为0.85%,相比于最小二乘算法与卡尔曼滤波算法,本文方法辨识结果平均误差分别提高41.51%和22.26%,均方根误差提高49.63%和13.0%,平均相对偏差提高41.7%和22.7%。本文提出的算法拥有更高的辨识精度。     

基于刚度轴偏角预估机制的 MEMS 多环谐振陀螺全闭环控制方法

针对微机电系统多环谐振陀螺正交闭环回路存在控制误差问题,提出一种基于刚度轴偏角预估机制的多环陀螺全闭环控制方法。该方法通过对微机电系统多环谐振陀螺刚度轴偏角预估,实现正交闭环回路参数自动优化调整。同时,提出了基于刚度轴偏角预估机制的全数字化闭环控制方法,实现微机电系统多环谐振陀螺的驱动、检测、正交、模态匹配环路的全闭环控制。该方法可提升正交闭环回路信噪比,增强陀螺正交漂移的抑制能力,降低陀螺零偏输出,改善陀螺的零偏不稳定性。实验结果表明,采用本文提出的基于刚度轴偏角预估机制的全闭环控制方法后,微机电系统多环谐振陀螺的零偏输出由0.201°/s降低为0.021 3°/s,零偏不稳定性由39.42°/h降低为1.237°/h,分别降低了9.44倍和31.86倍,验证了该方法对提升微机电系统多环谐振陀螺仪性能的有效性。