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。     

车辆半车悬架振动系统的参数辨识

在各类控制系统中广泛存在着时滞现象,尤其在振动控制系统中.本文以含有时滞状态反馈控制的四自由度半车悬架模型为例,在已知系统输入和输出数据的前提下,选取汽车车身质心位移响应作为输出,运用最小二乘法对系统的前后悬架时滞和位移增益参数进行辨识,以真实值和估计值之差的均方根值最小作为目标函数,从而验证该算法的有效性或可行性.     

滚珠丝杠式惯容器非线性建模与参数辨识

针对滚珠丝杠式惯容器存在的非线性力学特征,首先,分析了滚珠丝杠式惯容器的工作原理,对惯容器的主要部件进行了非线性力学分析,建立了考虑摩擦、间隙以及丝杠弹性力的惯容器非线性力学模型;然后,在数控液压伺服激振试验台上进行了惯容器的力学性能试验,得到了惯容器在不同惯容系数以及不同激振输入下的力学响应特性;最后,根据试验结果分析了摩擦和间隙对惯容器力学性能的影响,得到了便于参数辨识的惯容器力学模型结构,实现了惯容器输出力仿真与试验结果的分析和对比。结果显示,二者在一个周期内的均方根值误差不超过7%,非线性模型呈现出良好的拟合精度,证明了所述方法的可行性和有效性。     

上肢康复机器人动力学模型参数辨识

上肢康复机器人是一个非线性、多耦合的系统,对其建立精确的动力学方程较为困难,故无法直接应用于控制系统中。基于拉格朗日法建立康复机器人动力学方程,设计一条激励轨迹,并运用激励轨迹得到的关节力矩和关节角度等相关数据进行动力学参数辨识。使用递归最小二乘法辨识法得到较为精确的上肢康复机器人动力学方程,并通过设计相应的验证轨迹,证明了辨识得到的动力学方程的有效性。     

基于超级电容模型的参数辨识及其仿真研究

研究和分析了超级电容三分枝模型建立的理论基础,阐述了参数辨识的概念和递推最小二乘法应用于参数辨识的基本原理,在此基础上结合Matlab,编写了递推最小二乘法辨识程序,实现了对超级电容三分枝模型传递函数的辨识,最后通过对辨识结果的分析和仿真,验证了三分枝模型的正确性。     

基于增广最小二乘法的气动位置控制系统参数辨识研究

为实现气动位置控制系统的准确定位,对气动位置控制系统参数进行了数学建模,并推导出该系统的传递函数.采用AIC准则对系统进行了阶次辨识,对于不同的阶次(ň),找到了使AIC((ň))的(ň)作为模型最小的阶次;基于增广最小二乘法扩充了参数向量和数据向量的维数,实现了对有色噪声的无偏估计.实验结果表明,采样时间设定为10 ...     

基于最小二乘参数辨识的矿井漏电保护系统

针对影响矿井安全和稳定生产的电网漏电问题,首先分析了传统井下漏电保护方法的优缺点,然后对漏电故障发生时的相关暂态参数进行了研究,并建立了电网的漏电模型和方程,在此基础上,研究了基于最小二乘参数辨识的漏电支路选判原理和依据,随后对基于此原理的矿井漏电保护系统的运作原理等进行了探讨。研究内容和成果对煤矿井下选择性漏电保护系统的设计和改进具有参考价值。     

面向精度评价的并联机床参数辨识技术

基于并联机床外部标定,推导出辨识方程的残差近似等于标定后的位姿误差。为克服最小二乘法存在较大残差的缺陷,面向精度评价,提出最小最大优化的参数辨识技术,以残差最大绝对值最小为优化目标,直接控制误差范围。标定实例验证了最小二乘法缺陷的实际存在和最小最大优化抑制较大残差的效果。该参数辨识技术直接联系运动学标定和标定后的精度评价,简单有效的提高并联机床的整体精度。     

单臂SCARA手的动力学建模与参数辨识

针对单臂SCARA手臂机械结构,获取单臂高速大气机械手的动态特性对于机器人的精确运动和动力学控制有着重要的意义。首先将SCARA型机械手动力学分为上升、旋转和伸缩三个模块进行讨论。然后提出了其等效的运动学模型,并求出运动学的正反解,同时用拉格朗日法求出了动力学方程。最后利用参数辨识的方法求解出摩擦系数。利用了MATLAB机器人工具箱对机械手的运动学和动力学进行了仿真实验,通过比较整个动态转矩和电机扭矩证明了算法的有效性。经过实际测试,该动力学可靠有效。     

注塑机电磁感应加热系统在线参数辨识研究

注塑机电磁加热系统以其加热效率高、加热速度快正逐渐被工业界所采用,但由于其模型参数的时变性,目前还很难对其加热温度进行精确控制。通过利用等效变压器原理和热传导基本定律建立了注塑机电磁感应加热系统模型,分组采用带遗忘因子的递推最小二乘估计算法,提出了一种注塑机电磁感应加热系统分组在线参数辨识算法。利用自主搭建的实验台,进行了注塑机电磁感应加热系统在线参数辨识。辨识结果表明,该模型能够合理反映注塑机电磁感应加热过程中相关参数的变化趋势,且该算法可以客观、有效地反映时变系统相关参数的变化,此在线参数算法的提出为注塑机电磁感应加热系统精确温度控制奠定了基础。     

基于最小二乘辨识模型的PID自整定应用研究

针对工业数据模型辨识精度不高及PID不能在线自整定问题,提出一种闭环在线自整定控制策略。控制策略利用递推最小二乘辨识方法获得模型,基于该模型采用改进的遗传算法进行PID自整定,构成闭环在线自整定系统。通过MATLAB中的App designer平台验证了所提方法的可行性和有效性。仿真结果表明:辨识得到传递函数可以很好地拟合跟踪工业数据,PID自整定参数闭环阶跃响应控制效果良好。     

车辆加速度自适应控制器的设计

由于车辆本身的非线性和外界环境对它的影响,车辆的加速度控制具有较大的不确定性.利用最小二乘递推算法和广义最小方差控制率设计了针对车辆加速度控制的自适应控制器.仿真结果表明,该方法能够有效地实现在不确定性条件下车辆实际加速度对期望加速度的良好跟踪.     

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100Hz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.     

Recursive least square vehicle mass estimation based on acceleration partition

Vehicle mass is an important parameter in vehicle dynamics control systems. Although many algorithms have been developed for the estimation of mass, none of them have yet taken into account the different types of resistance that occur under different conditions. This paper proposes a vehicle mass estimator. The estimator incorporates road gradient information in the longitudinal accelerometer signal, and it removes the road grade from the longitudinal dynamics of the vehicle. Then, two different recursive least square method （RLSM） schemes are proposed to estimate the driving resistance and the mass independently based on the acceleration partition under different conditions. A 6 DOF dynamic model of four In-wheel Motor Vehicle is built to assist in the design of the algorithm and in the setting of the parameters. The acceleration limits are determined to not only reduce the estimated error but also ensure enough data for the resistance estimation and mass estimation in some critical situations. The modification of the algorithm is also discussed to improve the result of the mass estimation. Experiment data on asphalt road, plastic runway, and gravel road and on sloping roads are used to validate the estimation algorithm. The adaptability of the algorithm is improved by using data collected under several critical operating conditions. The experimental results show the error of the estimation process to be within 2.6%, which indicates that the algorithm can estimate mass with great accuracy regardless of the road surface and gradient changes and that it may be valuable in engineering applications. This paper proposes a recursive least square vehicle mass estimation method based on acceleration partition.     

基于响应面方法的材料参数反求

板料在冲压过程中处于复杂的各向异性三维弹塑性变形状态,若采用基于传统的单向拉伸试验得到的材料参数进行计算,必然会引入较大的计算误差。提出一种直接从冲压成形过程反求材料参数的方法。该方法将参数反求的问题转换为求试验测量值与仿真计算值之差最小二乘最小的优化问题。试验测量值为冲头的力–位移曲线,而待求的参数包括材料的特性参数和本构参数。采用移动最小二乘响应面法作为优化方法以减少调用正问题计算的次数。由于对冲压成形过程的模拟耗时较多,减少优化迭代的次数意义重大。     

系统辨识在轮式移动机器人转向系统中的应用

应用系统辨识方法对轮式移动机器人转向系统的数学模型进行了研究.首先对系统施加合适的输入信号,记录系统的输出信号,并进行试验建模,最后推导出被控对象的数学模型.经过验证,辨识模型能够反映实际系统的特性,这种辨识方法是研究轮式移动机器人转向系统特性的一种可行方法.     

Nonlinear modeling and identification of the electro-hydraulic control system of an excavator arm using BONL model

Electro-hydraulic control systems are nonlinear in nature and their mathematic models have unknown parameters. Existing research of modeling and identification of the electro-hydraulic control system is mainly based on theoretical state space model, and the parameters identification is hard due to its demand on internal states measurement. Moreover, there are also some hard-to-model nonlinearities in theoretical model, which needs to be overcome. Modeling and identification of the electro-hydraulic control system of an excavator arm based on block-oriented nonlinear（BONL） models is investigated. The nonlinear state space model of the system is built first, and field tests are carried out to reveal the nonlinear characteristics of the system. Based on the physic insight into the system, three BONL models are adopted to describe the highly nonlinear system. The Hammerstein model is composed of a two-segment polynomial nonlinearity followed by a linear dynamic subsystem. The Hammerstein-Wiener（H-W） model is represented by the Hammerstein model in cascade with another single polynomial nonlinearity. A novel Pseudo-Hammerstein-Wiener（P-H-W） model is developed by replacing the single polynomial of the H-W model by a non-smooth backlash function. The key term separation principle is applied to simplify the BONL models into linear-in-parameters struc~tres. Then, a modified recursive least square algorithm（MRLSA） with iterative estimation of internal variables is developed to identify the all the parameters simultaneously. The identification results demonstrate that the BONL models with two-segment polynomial nonlinearities are able to capture the system behavior, and the P-H-W model has the best prediction accuracy. Comparison experiments show that the velocity prediction error of the P-H-W model is reduced by 14%, 30% and 75% to the H-W model, Hammerstein model, and extended auto-regressive （ARX） model, respectively. This research is helpful in controller design, system monitoring and diagnosis.     

Identification of Maximum Road Friction Coefficient and Optimal Slip Ratio Based on Road Type Recognition

The identification of maximum road friction coefficient and optimal slip ratio is crucial to vehicle dynamics and control.However,it is always not easy to identify the maximum road friction coefficient with high robustness and good adaptability to various vehicle operating conditions.The existing investigations on robust identification of maximum road friction coefficient are unsatisfactory.In this paper,an identification approach based on road type recognition is proposed for the robust identification of maximum road friction coefficient and optimal slip ratio.The instantaneous road friction coefficient is estimated through the recursive least square with a forgetting factor method based on the single wheel model,and the estimated road friction coefficient and slip ratio are grouped in a set of samples in a small time interval before the current time,which are updated with time progressing.The current road type is recognized by comparing the samples of the estimated road friction coefficient with the standard road friction coefficient of each typical road,and the minimum statistical error is used as the recognition principle to improve identification robustness.Once the road type is recognized,the maximum road friction coefficient and optimal slip ratio are determined.The numerical simulation tests are conducted on two typical road friction conditions（single-friction and joint-friction）by using CarSim software.The test results show that there is little identification error between the identified maximum road friction coefficient and the pre-set value in CarSim.The proposed identification method has good robustness performance to external disturbances and good adaptability to various vehicle operating conditions and road variations,and the identification results can be used for the adjustment of vehicle active safety control strategies.     

基于RLS-DE算法的多变量径向磁轴承系统辨识

磁轴承的系统模型是提高其控制精度、稳定性及可靠性的基础,为了准确获取多变量径向磁轴承的系统模型,提出一种基于"递推最小二乘-差分进化"算法的辨识方法。该方法在剔除与转子转速同频的不平衡量的基础上,首先采用递推最小二乘法对径向磁轴承系统模型进行初步辨识,之后在初步辨识得到的模型参数的小范围内初始化差分进化算法的种群,通过反复进行差分进化算法的变异、交叉和选择操作,直至得到系统模型的最优参数。对该方法进行了仿真和试验验证,仿真结果表明,在递推最小二乘法辨识的基础上,通过差分进化算法小范围搜索得到的辨识模型输出误差的方差下降了92.86%;试验结果表明输出误差的方差能下降80.13%。验证了该方法高精度辨识径向磁轴承系统模型的有效性。