Improved cat swarm optimization for parameter estimation of mixed additive and multiplicative random error model

To estimate the parameters of the mixed additive and multiplicative(MAM)random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array,we introduce a derivative-free cat swarm optimization for parameter estimation.We embed the Powell method,which uses conjugate direction acceleration and does not need to derive the objective function,into the original cat swarm optimization to accelerate its convergence speed and search accuracy.We use the ordinary least squares,weighted least squares,original cat swarm optimization,particle swarm algorithm and improved cat swarm optimization to estimate the parameters of the straight-line fitting MAM model with lower nonlinearity and the DEM MAM model with higher nonlinearity,respectively.The experimental results show that the improved cat swarm optimization has faster convergence speed,higher search accuracy,and better stability than the original cat swarm optimization and the particle swarm algorithm.At the same time,the improved cat swarm optimization can obtain results consistent with the weighted least squares method based on the objective function only while avoiding multiple complex weight array derivations.The method in this paper provides a new idea for theoretical research on parameter estimation of MAM error models.

A Comparative Study on Kinematic Calibration for a 3-DOF Parallel Manipulator Using the Complete-Minimal,Inverse-Kinematic and Geometric-Constraint Error Models

Kinematic calibration is a reliable way to improve the accuracy of parallel manipulators, while the error model dramatically afects the accuracy, reliability, and stability of identifcation results. In this paper, a comparison study on kinematic calibration for a 3-DOF parallel manipulator with three error models is presented to investigate the relative merits of diferent error modeling methods. The study takes into consideration the inverse-kinematic error model, which ignores all passive joint errors, the geometric-constraint error model, which is derived by special geometric constraints of the studied RPR-equivalent parallel manipulator, and the complete-minimal error model, which meets the complete, minimal, and continuous criteria. This comparison focuses on aspects such as modeling complexity, identifcation accuracy, the impact of noise uncertainty, and parameter identifability. To facilitate a more intuitive comparison, simulations are conducted to draw conclusions in certain aspects, including accuracy, the infuence of the S joint, identifcation with noises, and sensitivity indices. The simulations indicate that the complete-minimal error model exhibits the lowest residual values, and all error models demonstrate stability considering noises. Hereafter, an experiment is conducted on a prototype using a laser tracker, providing further insights into the diferences among the three error models. The results show that the residual errors of this machine tool are signifcantly improved according to the identifed parameters, and the complete-minimal error model can approach the measurements by nearly 90% compared to the inverse-kinematic error model. The fndings pertaining to the model process, complexity, and limitations are also instructive for other parallel manipulators.

Positional Error Model of Line Segments with Modeling and Measuring Errors Using Brownian Bridge

Spatial linear features are often represented as a series of line segments joined by measured endpoints in surveying and geographic information science.There are not only the measuring errors of the endpoints but also the modeling errors between the line segments and the actual geographical features.This paper presents a Brownian bridge error model for line segments combining both the modeling and measuring errors.First,the Brownian bridge is used to establish the position distribution of the actual geographic feature represented by the line segment.Second,an error propagation model with the constraints of the measuring error distribution of the endpoints is proposed.Third,a comprehensive error band of the line segment is constructed,wherein both the modeling and measuring errors are contained.The proposed error model can be used to evaluate line segments’overall accuracy and trustability influenced by modeling and measuring errors,and provides a comprehensive quality indicator for the geospatial data.

Error Modeling and Sensitivity Analysis of a Parallel Robot with SCARA(Selective Compliance Assembly Robot Arm) Motions

Parallel robots with SCARA（selective compliance assembly robot arm） motions are utilized widely in the field of high speed pick-and-place manipulation. Error modeling for these robots generally simplifies the parallelogram structures included by the robots as a link. As the established error model fails to reflect the error feature of the parallelogram structures, the effect of accuracy design and kinematic calibration based on the error model come to be undermined. An error modeling methodology is proposed to establish an error model of parallel robots with parallelogram structures. The error model can embody the geometric errors of all joints, including the joints of parallelogram structures. Thus it can contain more exhaustively the factors that reduce the accuracy of the robot. Based on the error model and some sensitivity indices defined in the sense of statistics, sensitivity analysis is carried out. Accordingly, some atlases are depicted to express each geometric error’s influence on the moving platform’s pose errors. From these atlases, the geometric errors that have greater impact on the accuracy of the moving platform are identified, and some sensitive areas where the pose errors of the moving platform are extremely sensitive to the geometric errors are also figured out. By taking into account the error factors which are generally neglected in all existing modeling methods, the proposed modeling method can thoroughly disclose the process of error transmission and enhance the efficacy of accuracy design and calibration.

Error model identification of inertial navigation platform based on errors-in-variables model

Because the real input acceleration cannot be obtained during the error model identification of inertial navigation platform, both the input and output data contain noises. In this case, the conventional regression model and the least squares （LS） method will result in bias. Based on the models of inertial navigation platform error and observation error, the errors-in-variables （EV） model and the total least squares （TLS） method axe proposed to identify the error model of the inertial navigation platform. The estimation precision is improved and the result is better than the conventional regression model based LS method. The simulation results illustrate the effectiveness of the proposed method.

Ridge estimation iterative solution of ill-posed mixed additive and multiplicative random error model with equality constraints

The reasonable prior information between the parameters in the adjustment processing can significantly improve the precision of the parameter solution. Based on the principle of equality constraints, we establish the mixed additive and multiplicative random error model with equality constraints and derive the weighted least squares iterative solution of the model. In addition, aiming at the ill-posed problem of the coefficient matrix, we also propose the ridge estimation iterative solution of ill-posed mixed additive and multiplicative random error model with equality constraints based on the principle of ridge estimation method and derive the U-curve method to determine the ridge parameter. The experimental results show that the weighted least squares iterative solution can obtain more reasonable parameter estimation and precision information than existing solutions, verifying the feasibility of applying the equality constraints to the mixed additive and multiplicative random error model. Furthermore, the ridge estimation iterative solution can obtain more accurate parameter estimation and precision information than the weighted least squares iterative solution.

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.

Error modeling and analysis of inclinometer based on digital accelerometer

Measuring accuracy of inclinometer based on accelerometer is mainly influenced by the adopted accelerometer sensor.To improve the measuring accuracy of the inclinometer,the structure of the measuring system is given and measuring principle is analyzed,and the error model is established in this paper.Furthermore,the model is verified by simulation and experiment,which not only gives the smallest errors of the measured pitch and roll,but also lays foundation for sensor selection,error analysis and error compensation.The results show that the error model is of practical value.

Simulation of Current Measurement Using Magnetic Sensor Arrays and Its Error Model

Magnetic sensor arrays are proposed to measure electric current in a non-contact way. In order to achieve higher accuracy, signal processing techniques for magnetic sensor arrays are utilized. Simulation techniques are necessary to study the factors influencing the accuracy of current measurement. This paper presents a simulation method to estimate the impact of sensing area and position of sensors on the accuracy of current measurement. Several error models are built up to support computer-aided design of magnetic sensor arrays.

Research on in-light alignment error model based on quaternion and rotation vector

Euler angle error model, rotation vector error model （RVE） and quaternion error model （QE） were qualitatively and quantitatively compared and an in-flight alignment filter algorithm was designed. This algorithm used extended Kalman filter （EKF） based on RVE and QE separately avoi- ding the accuracy problem of the Euler angle model and used Rauch-Tung-Striebel（RTS） smoothing method to refine the accuracy recuperating the coning error for simplified RVE. Simulation results show that RVE and QE are more adapt for nonlinear filter estimation than the Euler angle model. The filter algorithm designed has more advantages in convergence speed, accuracy and stability comparing with the algorithm based on the three models separately.

Research on identifying the dynamic error model of strapdown gyro on 3-axis turntable

The dynamic errors of gyros are the important error sources of a strapdown inertial navigation system. In order to identify the dynamic error model coefficients accurately, the static error model coefficients which lay a foundation for compensating while identifying the dynamic error model are identified in the gravity acceleration fields by using angular position function of the three-axis turntable. The angular acceleration and angular velocity are excited on the input, output and spin axis of the gyros when the outer axis and the middle axis of a three-axis turntable are in the uniform angular velocity state simultaneously, while the inner axis of the turntable is in different static angular positions. 8 groups of data are sampled when the inner axis is in 8 different angular positions. These data are the function of the middle axis positions and the inner axis positions. For these data, harmonic analysis method is applied two times versus the middle axis positions and inner axis positions respectively so that the dynamic error model coefficients are finally identified through the least square method. In the meantime the optimal angular velocity of the outer axis and the middle axis are selected by computing the determination value of the information matrix.

Iterative identification of discrete-time output-error model with time delay

The problem of discrete-time model identification of industrial processes with time delay was investigated.An iterative and separable method is proposed to solve this problem,that is,the rational transfer function model parameters and time delay are alternately fixed to estimate each other.The instrumental variable technique is applied to guarantee consistent estimation against measurement noise.A noteworthy merit of the proposed method is that it can handle fractional time delay estimation,compared to existing methods commonly assuming that the time delay is an integer multiple of the sampling interval.The identifiability analysis for time delay is addressed and correspondingly,some guidelines are provided for practical implementation of the proposed method.Numerical and experimental examples are presented to illustrate the effectiveness of the proposed method.

A Revision of <i>AIC</i>for Normal Error Models

Conventional Akaike’s Information Criterion (AIC) for normal error models uses the maximum-likelihood estimator of error variance. Other estimators of error variance, however, can be employed for defining AIC for normal error models. The maximization of the log-likelihood using an adjustable error variance in light of future data yields a revised version of AIC for normal error models. It also gives a new estimator of error variance, which will be called the “third variance”. If the model is described as a constant plus normal error, which is equivalent to fitting a normal distribution to one-dimensional data, the approximated value of the third variance is obtained by replacing (n-1) (n is the number of data) of the unbiased estimator of error variance with (n-4). The existence of the third variance is confirmed by a simple numerical simulation.

Kinematic error modeling and error compensation of desktop 3D printer

Desktop 3D printers have revolutionized how designers and makers prototype and manufacture certain products.Highly popular fuse deposition modeling(FDM)desktop printers have enabled a shift to low-cost consumer goods markets,through reduced capital equipment investment and consumable material costs.However,with this drive to reduce costs,the computer numerical control(CNC)systems implemented in FDM printers are often compromised by poor accuracy and contouring errors.This condition is most critical as users begin to use 3D-printed components in load-bearing applications or to perform mechanical functions.Improved methods of low-cost 3D printer calibration are needed before their open-design potential can be realized in applications,including 3D-printed orthotics and prosthetics.This paper applies methodologies associated with high-precision CNC machining systems,namely,kinematic error modeling and compensation coupled with standardized test methods from ISO230-4,such as the ballbar for kinematic and dynamic error measurements,to examine the influence and feasibility for use on low-cost CNC/3D printing platforms.Recently,the U.S.Food and Drug Administration's"Technical considerations for additive manufactured medical devices"highlighted the need to develop standards specific to additive manufacturing in regulated manufacturing environments.This paper shows the benefits of the methods described within ISO230-4 for error assessment,alongside applying kinematic error modeling and compensation to the popular kinematic configuration of an Ultimaker 3D printer.A Renishaw ballbar QC10 is used to quantify the Ultimaker's errors and thereby populate the error model.This method quantifies machine errors and populates these in a mathematical model of the CNC system.Then,a post-processor can be used to compensate the printing code.Subsequently,the ballbar is used to demonstrate the dramatic impact of the error compensation model on the accuracy and contouring of the Ultimaker printer with 58%reduction in overall circularity error and 90%reduction in squareness error.

Application of Genetic Algorithm in Estimation of Gyro Drift Error Model

Extended Kalman Filter(EKF)algorithm is widely used in parameter estimation for nonlinear systems.The estimation precision is sensitively dependent on EKF’s initial state covariance matrix and state noise matrix.The grid optimization method is always used to find proper initial matrix for off-line estimation.However,the grid method has the draw back being time consuming hence,coarse grid followed by a fine grid method is adopted.To further improve efficiency without the loss of estimation accuracy,we propose a genetic algorithm for the coarse grid optimization in this paper.It is recognized that the crossover rate and mutation rate are the main influencing factors for the performance of the genetic algorithm,so sensitivity experiments for these two factors are carried out and a set of genetic algorithm parameters with good adaptability were selected by testing with several gyros’experimental data.Experimental results show that the proposed algorithm has higher efficiency and better estimation accuracy than the traversing grid algorithm.

Error Model of Curves in GIS and Digitization Experiment

A stochastic error process of curves is proposed as the error model to describe the errors of curves in GIS. In terms of the stochastic process, four characteristics concerning the local error of curves, namely, mean error function, standard error function, absolute error function, and the correlation function of errors , are put forward. The total error of a curve is expressed by a mean square integral of the stochastic error process. The probabilistic meanings and geometric meanings of the characteristics mentioned above are also discussed. A scan digitization experiment is designed to check the efficiency of the model. In the experiment, a piece of contour line is digitized for more than 100 times and lots of sample functions are derived from the experiment. Finally, all the error characteristics are estimated on the basis of sample functions. The experiment results show that the systematic error in digitized map data is not negligible, and the errors of points on curves are chiefly dependent on the curvature and the concavity of the curves.

Calibration of parallel kinematics machine using generalized distance error model

This paper focus on the accuracy enhancement of parallel kinematics machine through kinematics calibration. In the calibration processing, well-structured identification Jacobian matrix construction and end-effector position and orientation measurement are two main difficulties. In this paper, the identification Jacobian matrix is constructed easily by numerical calculation utilizing the unit virtual velocity method. The generalized distance errors model is presented for avoiding measuring the position and orientation directly which is difficult to be measured. At last, a measurement tool is given for acquiring the data points in the calibration processing. Experimental studies confirmed the effectiveness of method. It is also shown in the paper that the proposed approach can be applied to other typed parallel manipulators.

Ranging Error Correction Based on a Prior Error Model

Wireless technology provides accurate positioning in indoor environments using time of arrival(TOA) based ranging techniques. However, the positioning accuracy is degraded due to the ranging errors caused by multipath and non-line-of-sight(NLOS) propagation. In this paper, a ranging error correction method is proposed to improve positioning performance. A TOA ranging error model(TREM) is built to provide the prior information for ranging error correction first. The mean value of TREM within a certain interval is used as the ranging error correction value(RECV). As the RECV may be unreasonable sometimes, we adjust it according to the actual positioning situation and then exploit the final RECV to correct ranging data. The experimental results show that the proposed method could well reduce ranging errors and the positioning performance is obviously improved when using corrected ranging data.

Anovelprofile error model to calculate contact error

A new profile model based on multi-scale asperities is developed and contact error is cal- culated. After stratified sampling, the model can get the distribution law of entity points on each cross section. Asperity radius of curvature is estimated by the relationship between circle radius and the section interval. Contact error is related to surface form error. A model equation of contact error plane is calculated through a method based on static equilibrium theory. Three contact asperities which determine the contact error plane on the rough surface are studied. The simulation results show that contact error can be accurately calculated according to the profile error model.

Thermal Error Modeling Method with the Jamming of Temperature-Sensitive Points'Volatility on CNC Machine Tools

Aiming at the deficiency of the robustness of thermal error compensation models of CNC machine tools, the mechanism of improving the models＇ robustness is studied by regarding the Leaderway-V450 machining center as the object. Through the analysis of actual spindle air cutting experimental data on Leaderway-V450 machine, it is found that the temperature-sensitive points used for modeling is volatility, and this volatility directly leads to large changes on the collinear degree among modeling independent variables. Thus, the forecasting accuracy of multivariate regression model is severely affected, and the forecasting robustness becomes poor too. To overcome this effect, a modeling method of establishing thermal error models by using single temperature variable under the jamming of temperature-sensitive points＇ volatility is put forward. According to the actual data of thermal error measured in different seasons, it is proved that the single temperature variable model can reduce the loss of fore- casting accuracy resulted from the volatility of tempera- ture-sensitive points, especially for the prediction of cross quarter data, the improvement of forecasting accuracy is about 5 μm or more. The purpose that improving the robustness of the thermal error models is realized, which can provide a reference for selecting the modelingindependent variable in the application of thermal error compensation of CNC machine tools.