基于GA PSO混合算法的钢杆磁特性参数识别方法

测量轴类零件的磁滞回线,利用其特征参数的变化表征零件表面硬度及硬化层深度,是具有工程应用前景的电磁无损检测新技术之一,其关键是轴类零件磁特性曲线测量装置的研制和磁特性参数高精度识别方法的研究。设计出一种基于闭环磁路的钢杆磁滞回线测量实验装置,并基于J-A磁滞模型,提出了一种遗传粒子群(GA-PSO)混合算法,实现了钢杆磁滞回线全局与局部特征参数的快速、高精度识别。实验测得的3种不同材质钢杆磁滞回线,对比分析了混合优化算法与单一算法(遗传、粒子群、模拟退火)的参数识别速度与精度,结果表明,混合算法全局识别结果的最小均方根误差仅为0.004 7,低于单一算法的相应结果;混合算法对局部特征参数(矫顽力、剩余磁感应强度)识别的相对误差均小于0.35%,优于单一算法识别精度。上述实验测试和磁特性参数识别方法,有望应用于销钉、螺栓等轴类构件表面硬化层的无损检测。     

两阶段闭环辨识算法在加热炉控制中的应用

在闭环条件下,系统控制输入信号通过反馈环节与不可测噪声相关,故用开环辨识算法处理闭环数据时所得参数估计常常是有偏的。为克服参数估计的有偏性,采用了基于开环转换的两阶段闭环辨识算法,并分析了中间辨识模型与最终辨识结果的关系。在此基础上,得到了中间辨识模型的选择依据,即在相同运算量下,选用ARX模型比选用FIR模型所得辨识结果更好。并应用于一台加热炉,应用结果验证了该辨识算法的有效性及模型选择的合理性。     

ESD模型参数识别的差分进化方法

提出了一种以差分进化算法对静电放电模型进行参数辨识的新方法。以基于Heidler雷电流方程的静电放电模型参数为辨识对象,分别以仿真和实验数据验证了该方法的可行性,并从电流波形整体和局部两方面对拟合效果进行了评估。结果表明,与遗传算法相比,差分进化算法的执行速度更快,所得的辨识参数精度更高,对电流波形的整体和局部关键点的拟合度均高于遗传算法。因此,差分进化算法比遗传算法更适用于解决静电放电模型参数辨识问题。从实例可以看出,差分进化算法不需要过多的初始参数值先验知识,而只需提供一个较宽的初始参数搜索范围即可获得良好的辨识结果。此外,本文还以差分进化算法对Bruce-Golden和Gaussian函数静电放电模型进行参数辨识,验证了该方法的适用性。     

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]     

An efficient identification approach for stable and unstable nonlinear systems using Colliding Bodies Optimization algorithm

This paper presents an efficient approach to identify different stable and practically useful Hammerstein models as well as unstable nonlinear process along with its stable closed loop counterpart with the help of an evolutionary algorithm as Colliding Bodies Optimization (CBO) optimization algorithm. The performance measures of the CBO based optimization approach such as precision, accuracy are justified with the minimum output mean square value (MSE) which signifies that the amount of bias and variance in the output domain are also the least. It is also observed that the optimization of output MSE in the presence of outliers has resulted in a very close estimation of the output parameters consistently, which also justifies the effective general applicability of the CBO algorithm towards the system identification problem and also establishes the practical usefulness of the applied approach. Optimum values of the MSEs, computational times and statistical information of the MSEs are all found to be the superior as compared with those of the other existing similar types of stochastic algorithms based approaches reported in different recent literature, which establish the robustness and efficiency of the applied CBO based identification scheme.     

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.     

New approach of friction identification for electro-hydraulic servo system based on evolutionary algorithm and statistical logics with experiments

High-precision control of an Electro-hydraulic servo system (EHSS) should fully consider practical non-linearities, such as dynamic friction, in controller design. The LuGre model is widely used to describe non-linear friction, but parameter identification for this model remains a challenging task. This study proposes a novel identification approach for the LuGre model based on the Evolutionary algorithm (EA) and statistical logics. Identification experiments are also conducted for a practical EHSS. Static parameters are identified with constant velocity experiments. Moreover, the non-symmetry of friction in positive and negative rotary directions is fully considered. Dynamic parameters are identified with the pre-sliding process in the breakaway experiment. The EA is utilized to enhance the optimal estimation of friction parameters. Statistical logics are used to predict the deviations in the estimated results with high confidence interval. Furthermore, extensive simulation and experiment results validate the effectiveness of the proposed approach.

     

直流伺服电机驱动的自动离合器控制

采用直流伺服电机驱动的自动离合器执行机构，与高速开关阀控制液压缸的执行机构相比，可使电控机械自动变速系统的结构大大简化，同时可提高离合器结合速度的控制精度。设计了基于模糊控制和PI控制双闭环控制的伺服电机驱动离合器执行机构，建立了数学模型，并进行了系统仿真。结果表明：直流伺服电机驱动离合器，能很好地控制离合器的结合速度，提高车辆起步品质。     

Reduced-order model based active disturbance rejection control of hydraulic servo system with singular value perturbation theory

Hydraulic servomechanism is the typical mechanical/hydraulic double-dynamics coupling system with the high stiffness control and mismatched uncertainties input problems, which hinder direct applications of many advanced control approaches in the hydraulic servo fields. In this paper, by introducing the singular value perturbation theory, the original double-dynamics coupling model of the hydraulic servomechanism was reduced to a integral chain system. So that, the popular ADRC (active disturbance rejection control) technology could be directly applied to the reduced system. In addition, the high stiffness control and mismatched uncertainties input problems are avoided. The validity of the simplified model is analyzed and proven theoretically. The standard linear ADRC algorithm is then developed based on the obtained reduced-order model. Extensive comparative co-simulations and experiments are carried out to illustrate the effectiveness of the proposed method.     

机械装配模型中齿轮副的自动识别

探讨了从CAD装配模型中识别齿轮副的自动识别算法.在对齿轮特征进行深入研究的基础上,首先提出了将三维实体零件特征的识别转化为齿轮面的二维识别的思想;然后给出了通过判断一个零件的面的特征、面上环的特征、环上的公共边端点的凸凹性等拓扑元素的特征来识别零件是否齿轮的方法.该算法不受装配语义的影响,对于CAD/CAE的无缝集成具有重要的意义.     

Application of memetic algorithm in modelling discrete-time multivariable dynamics systems

Evolutionary algorithm (EA) such as genetic algorithm (GA) has demonstrated to be an effective method for identification of single-input–single-output (SISO) system. However, for multivariable systems, increasing the orders and the non-linear degrees of the model will result in excessively complex model and the identification procedure for the systems is more often difficult because couplings between inputs and outputs. There are more possible structures to choose from and more parameters are required to obtain a good fit. In this work, a new model structure selection in system identification problems based on a modified GA with an element of local search known as memetic algorithm (MA) is adopted. This paper describes the procedure and investigates the performance and the effectiveness of MA based on a few case studies. The results indicate that the proposed algorithm is able to select the model structure of a system successfully. A comparison of MA with other algorithms such as GAs demonstrates that MA is capable of producing adequate and parsimonious models effectively.     

一种T-S模糊模型的自组织辨识算法及应用

提出了一种新的具有自适应学习能力的T-S模糊模型辨识算法.该算法通过使同一条规则的高斯函数的宽度参数彼此相等建立与支持向量机等效的T-S模糊模型,在此基础上,利用模糊聚类算法和支持向量机分别建立前后件辨识模型,并利用一种改进粒子群优化算法优化输出误差函数使前后件参数联合辨识,从而获得T-S模糊模型的结构和参数.仿真结果表明,相比其它方法,文中方法具有较高的逼近精度和较好的泛化能力,由此算法获得的直拉单晶炉热场模型具有0.1171的均方差,完全符合均方差小于0.5的要求.     

Bouc-Wen model parameter identification for a MR fluid damper using computationally efficient GA

A non-symmetrical Bouc-Wen model is proposed in this paper for magnetorheological (MR) fluid dampers. The model considers the effect of non-symmetrical hysteresis which has not been taken into account in the original Bouc-Wen model. The model parameters are identified with a Genetic Algorithm (GA) using its flexibility in identification of complex dynamics. The computational efficiency of the proposed GA is improved with the absorption of the selection stage into the crossover and mutation operations. Crossover and mutation are also made adaptive to the fitness values such that their probabilities need not be user-specified. Instead of using a sufficiently number of generations or a pre-determined fitness value, the algorithm termination criterion is formulated on the basis of a statistical hypothesis test, thus enhancing the performance of the parameter identification. Experimental test data of the damper displacement and force are used to verify the proposed approach with satisfactory parameter identification results.     

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。     

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies.     

基于改进模拟退火算法的关节臂式坐标测量机参数辨识

结构参数误差是影响关节臂式坐标测量机精度的主要因素之一,精确辨识其结构参数可以有效地提高测量机的精度。建立基于Denavit-Hartenberg模型的六自由度关节臂式坐标测量机坐标变换方程,分析基于锥孔的参数辨识原理,提出一种改进的模拟退火算法用于测量机的结构参数辨识,该算法在接近最优解时将减小搜索范围以提高搜索效率和求解精度,并保留中间过程的最优解。以单点重复精度为目标函数,利用改进的模拟退火算法对研制的六自由度关节臂式坐标测量机的结构参数进行辨识。实验结果表明,经过参数辨识后,测量机的单点重复精度提高了7.87倍,长度测量精度提高了5.59倍。     

DAMAGE IDENTIFICATION USING CHANGES OF EIGENFREQUENCIES AND MODE SHAPES

In the present paper, we describe an approach to identify the location and the extent of the damage introduced into the steel frame, using a two-step procedure. In the first step, the measured dynamic response of the original undamaged structure was used to generate a reference finite element (FE) model of the structure. The selected parameters were identified by means of a mathematical optimisation algorithm (‘updating procedure'), minimising an objective function containing the test/analyses differences of eigenfrequencies and mode shapes. The uncertain model parameters had to be chosen with care in order to retain the physical significance of the updated model. In the next step, the experimental modal data of the damaged structure were used to identify the extent of the damages. This was based on comparing the changes of stiffness parameters identified from the undamaged and the damaged structure. With the identified parameters, the FE model was able to reproduce the experimental data as close as possible and allowed the identification of the extent of the damage.     

低速风洞绳牵引并联支撑系统的运动学参数标定

提出一种用两个倾角计且基于逐次逼近算法对风洞绳牵引并联支撑系统WDPSS-8的运动学参数进行标定的方法，利用该方法对WDPSS-8的49个主要运动学参数进行了辨识，通过计算机仿真对辨识结果进行了验证，从而为提高WDPSS-8的缩比模型的姿态角精度奠定了理论基础。     

压电作动器非对称迟滞模型的建立和参数辨识

现有的众多基于传统Bouc-Wen改进的压电陶瓷非对称迟滞模型存在参数冗余,降低了模型参数辨识的准确性,而且常用的粒子群算法(PSO)在辨识压电陶瓷非对称迟滞模型参数方面收敛慢且容易陷入局部最优值。为此,首先提出了一种归一化的非对称迟滞模型,采用两个多项式达到非对称效果,利用归一化Bouc-Wen消除参数冗余;然后采用参数和变异策略自适应的差分进化算法进行迟滞参数辨识;建立了相应的测试系统,对压电陶瓷作动器进行了实验研究。结果表明,相比于传统的Bouc-Wen模型,所提出的模型能更精确地描述压电陶瓷实际电压位移曲线,而且消除了参数的冗余,降低了参数辨识的难度。相比于粒子群算法和传统差分进化算法,自适应差分进化算法能更快更精准地找到最优参数值。