Error Compensation Module Research Articles

Vision-aided intelligent and adaptive vehicle pose estimation during GNSS outages

In this paper, a novel multi-information fusion methodology is proposed for vehicle pose estimation. The purpose is to improve the pose estimation accuracy during global navigation satellite system (GNSS) outages, mainly from two aspects: (1) extra observations of accurate velocities and angular rates without cumulative errors; (2) adaptive and intelligent fusion. Firstly, a multidimensional motion perception network (MMPN) is designed in order to estimate the velocities and angular rates. At the same time, the motion state is also output. The inputs of the network are two consecutive images. Thus, the estimated velocities and angular rates are not affected by cumulative errors. Besides, because the corresponding velocities and angular rates measured by high-quality inertial measurement unit (IMU) with low noise and drift are used as the training samples, the accuracy of the estimated velocities and angular rates are guaranteed. Further, an unscented Kalman filter with federal structure (UKF-F) with different motion models is designed for pose estimation. The local filters of UKF-F use the current probabilities of belonging to the motion patterns output by the MMPN as the information-sharing factors. Thus, the fusion is adaptive to the actual vehicle motion state. In addition, a gate recurrent unit (GRU)-based error compensation module is introduced to further reduce the position errors during GNSS outages. The GRU-based module is trained offline with the input of MMPN and IMU observations. Due to the advantages of time series forecasting, the GRU-based module can accurately predict the position error of IMU/MMPN integration online. The KITTI dataset with different driving scenarios is used to confirm the feasibility of the proposed methodology. The results of the experiments verify that the proposed methodology can achieve precise vehicle pose estimation during GNSS outages.

MOHP-EC: A Multiobjective Hierarchical Prediction Framework for Urban Rail Transit Passenger Flow

For sophisticated management, advertisement placement, and epidemic prevention control of urban rail transit (URT), accurate and real-time predictions of passenger flows at different levels are of great importance. Unlike traditional prediction tasks, hierarchical prediction (HP) requires that the hierarchical constraints be satisfied as much as possible (the sum of the predicted passenger flow of child nodes should nearly equal the parent node) to achieve realistic predictions. This article proposes a multiobjective HP (MOHP) framework with an error compensation (EC) mechanism for predicting URT passenger flow with a hierarchical structure. Three components are included: the initial prediction module, the EC module, and the hierarchical coordination module. In the initial prediction module, the initial passenger flow prediction of each layer is carried out. The EC model is developed based on proportional-integral-derivative control to compensate for the initial predicted value of every layer. As a final step, a trainable HP model is constructed based on deep learning to coordinate the prediction values of each layer. As examples, we construct three scenarios of passenger flow hierarchy based on the URT system in Wuxi, China. The constructed prediction framework is used to conduct experimental analyses. As a result, the MOHP-EC prediction framework could satisfy the hierarchical constraints and use the passenger flow hierarchical information to reduce prediction errors. The mean absolute error was reduced by 35%, and the root-mean-square error was reduced by 39%.

An Ultra-Efficient Approximate Multiplier With Error Compensation for Error-Resilient Applications

Approximate computing is a promising paradigm for trading off accuracy to improve hardware efficiency in error-resilient applications such as neural networks and image processing. This brief presents an ultra-efficient approximate multiplier with error compensation capability. The proposed multiplier considers the least significant half of the product a constant compensation term. The other half is calculated precisely to provide an ultra-efficient hardware-accuracy trade-off. Furthermore, a low-complexity but effective error compensation module (ECM) is presented, significantly improving accuracy. The proposed multiplier is simulated using HSPICE with 7nm tri-gate FinFET technology. The proposed design significantly improves the energy-delay product, on average, by 77% and 54% compared to the exact and existing approximate designs. Moreover, the proposed multiplier’s accuracy and effectiveness in neural networks and image multiplication are evaluated using MATLAB simulations. The results indicate that the proposed multiplier offers high accuracy comparable to the exact multiplier in NNs and provides an average PSNR of more than 51dB in image multiplication. Accordingly, it can be an effective alternative for exact multipliers in practical error-resilient applications.

HE[formula omitted]LM-AD: Hierarchical and efficient attitude determination framework with adaptive error compensation module based on ELM network

Satellite attitude determination, as a crucial pre-processing technology in remote sensing earth observation, is significantly associated with the geometric accuracy of satellite stereo images. To compensate for the observation errors in raw attitude data for achieving accurate results, the conventional Kalman filter-based and filter-smoothing combination frameworks provide insufficient access to statistical features of data, resulting in inferior practical system performance. To conquer these drawbacks, a hierarchical and efficient attitude determination framework is proposed called HE2LM-AD, which consists of a simplified adaptive Kalman filtering module, a neural network-based system error compensation module, and a weighted attitude smoothing module. To be more specific, an adaptive noise covariance matrix update is exploited in the Extended Kalman Filter (EKF) iterations, and a simplification scheme based on Cholesky decomposition is employed to obtain coarse filter outputs efficiently. Following that, a dedicated Extreme Learning Machine (ELM)-based error compensation network is designed to capture the pattern of observation errors, providing robust compensation at lower overhead. To further pursue high-performance attitude determination results, we fused the outcomes of the aforementioned two components according to the weights achieved from adaptive learning. Experiment results on in-orbit satellite attitude simulation datasets demonstrate and validate the feasibility and effectiveness of our framework.

Flexible Nonlinear Error Correction Method Based on Support Vector Regression in Fringe Projection Profilometry

The gamma effect of a projector is the most critical factor affecting three-dimensional shape measurements. A flexible and efficient error correction algorithm based on machine learning is proposed that facilitates correcting the system nonlinear error without any system nonlinear parameter calibration or knowledge of prior system information. The proposed method is a two-stage framework. The first stage is the projector gamma estimation module. First, the probability density function (PDF) of the wrapped phase with different gamma values is generated by simulation. The corresponding PDF sequence and gamma values are combined into a dataset to train a support vector regression (SVR) model. The trained SVR model can estimate the system gamma value according to the PDF sequence of the actual measured wrapped phases. The second stage is the phase error compensation module. In this stage, we establish the objective function according to the mathematical model and the gamma value estimated in the first part, and then use an iterative algorithm to solve the precise phase. Experimental and simulation results confirm the effectiveness of this method in directly identifying the system gamma value from the wrapped phase without complicated system calibration or any prior system information. The proposed algorithm is suitable for time-varying nonlinear systems.

A Novel Strategy of Control Performance Improvement for Six-Phase Permanent Magnet Synchronous Hub Motor Drives of EVs Under New European Driving Cycle

This paper proposes a model predictive current control (MPCC) method of six-phase permanent magnet synchronous hub motor (PMSHM) using auxiliary voltage vectors. First of all, 26 voltage vectors can be obtained from the switching states of six inverters, and 24 auxiliary voltage vectors can be generated by combining them. Second, four virtual voltage vectors can be found through the required locations, and the two vectors with the lowest cost will be selected by cost function. Then, additional current error correction module can better track the current. Finally, the proposed method is compared with the conventional MPCC method and another method with virtual voltage vectors. The innovation of this paper is that the recombination of vectors can better track and predict the current. A current error compensation module is added to reduce the error between the actual current and the predicted current. And the experimental results show that the proposed method has better performance such as smaller torque ripple and current THD both in steady and dynamic states. Finally, the strategy proposed in this paper was applied to the vehicle model through HIL test platform. The possibility of applying the strategy proposed in this paper to pure electric vehicles was verified under New European Driving Cycle (NEDC).

Positioning accuracy control of dual-axis dicing saw for machining semiconductor chip

Dicing of semiconductor chips constitutes an important procedure in the processing of integrated circuits, which poses a high-precision and high-speed stability requirements for dicing saw. In this study, the optimization of motion accuracy and position accuracy of dual-axis dicing saw (DDS) is studied and an overall control scheme of the DDS is designed. A programmable multi-axis controller (PMAC) is selected as the main control module and the servo motor is adopted as the linear axis drive element. The positioning algorithm of servo control system is analyzed. The position deviations are studied under different proportional gains, integral time constants, and speed feedforward gains. The high-precision contact and non-contact positioning method of the machine tool is developed. Positioning error of each linear axis is compensated using positioning error compensation module of PMAC. The dicing test results demonstrate that the improved control system could be used for high-precision and high-speed stability dicing of semiconductor chips.

A Deep Learning Based Hybrid Framework for Day-Ahead Electricity Price Forecasting

With the deregulation of the electric energy industry, accurate electricity price forecasting (EPF) is increasingly significant to market participants' bidding strategies and uncertainty risk control. However, it remains a challenging task owing to the high volatility and complicated nonlinearity of electricity prices. Aimed at this, a novel hybrid deep-learning framework is proposed for day-ahead EPF, which includes four modules: the feature preprocessing module, the deep learning-based point prediction module, the error compensation module, and the probabilistic prediction module. The feature preprocessing module is based on isolation forest (IF), and least absolute shrinkage and selection operator (Lasso), which is used to detect outliers and select the correlated features of electricity price series. The point prediction module combines the deep belief network (DBN), long-short-term memory (LSTM) neural network (RNN), and convolutional neural network (CNN), and is employed to extract complicated nonlinear features. The residual error between forecasting price and actual price can be reduced based on the error compensation module. The probabilistic prediction module based on quantile regression (QR) is used to estimate the uncertainty under various confidence levels. The PJM market data is employed in case studies to evaluate the proposed framework, and the results revealed that it has a competitive advantage compared with all of the considered comparison methods.

Real-time Thermal Error Compensation Strategy for Precision Machine tools

Abstract Present manufacturing trend is towards producing precision components with better accuracy. Machine errors like geometrical, thermal and process errors affect the component accuracy. Among these errors, thermal error contributes more than 50-60% of the total machining error. This paper mainly focuses on the development of a real-time thermal error compensation module for precision machine tools and talks about effective modeling of thermal errors, development of thermal error compensation model using feed-forward backpropagation neural network and also simplified model using regression analysis technique, algorithm development for real-time compensation and implementation of module onto the open architecture CNC controller. The developed module has been successfully tested on a Diamond Turning Machine (DTM) by machining the precision component and also verified the effectiveness of the module

An error compensation scheme for multi-axis machine tool using machining method template

In this paper, a software-based geometric error compensation method using the machining method template is proposed to modify the long and complicated G-code file with multiple machining features. Firstly, an error model is established to evaluate the error distribution. Then, a new segmentation method is proposed to split the long multi-axis line into several parts with the same length to eliminate the nonlinear effect. Furthermore, a compensation scheme according to the machining method is presented to determine which segments and axes should be compensated when facing with a long and complicated G-code file. In addition, a framework is developed to process the G-code file with high efficiency. The G-code file is firstly separated into the motion and non-motion command files, which are then merged together to form a new G-code file after the motion commands are modified by an error compensation module. Finally, a machining experiment with a test workpiece containing multiple features is conducted on a five-axis machining center to validate the effectiveness and correctness of the proposed method.

A Software Method for Online Thermal Error Inspection and Compensation

In this paper, a new online method is proposed to measure and identify the spindle thermal error of the machining center. The method is firstly established by a comprehensive error identification model including geometric and thermal error. Then the multi-viewpoint modeling method based on stereo vision is adopted to obtain the solution of thermal deformation error without the influence of geometric error. A software is developed to online inspect and compensate the thermal error of machining center. The software is consisted by four modules, measurement module, error identification module, error compensation module and analysis module. It can not only be able to quickly identify the thermal error detection, but also can accurately predict thermal error, and effectively improve the machining accuracy. Finally, the experiment results show that the thermal error values of the machining center can be measured accurately and identified online quickly by the proposed software method.

Real-time Thermal Error Compensation Module for Intelligent Ultra Precision Turning Machine (iUPTM)

Accuracy & precision are 1he main requirements for ultra precision machine tools. Many factors affect 1he performance of 1he system 1hat in turns affect 1he product quality. Among all sources of errors, the thermo mechanical deformation errors are the main contributor for 1he overall geometrical errors. This paper mainly aims at establislunent of methodology to compensate thermal deformation errors in real-time for ultra precision machine tools. The real-time thermal error compensation module has been developed and integrated to intelligent Ultra Precision Turning machine. The module includes temperatures as inputs, neural network algorithm for computing the thermal deformations errors, ‘C’ programming for real-time calculations and integration with open architecture CNC controller. The module runs in silent mode which avoids human intervention for correction of thermal deformation errors.

Thermally induced volumetric error modeling based on thermal drift and its compensation in Z-axis

Traditional thermally induced volumetric error modeling requires 21 geometric error components at different temperatures. Taking thermal drift errors into account, 30 geometric errors are described to model volumetric error in this paper. The main sources of thermally induced volumetric error are positioning errors of each axis and thermal drift errors. An experiment on milling and boring machine is carried out, which shows that volumetric error in Z-axis is affected significantly. To compensate volumetric error of Z-axis, a model of positioning error is proposed based on nut temperature. A finite elements analysis of headstock thermal characteristic is carried out, and error chain is established, which shows the main source of thermal drift of Z-axis is ram expansion. Thermal drift compensation system of Z-axis is developed based on Invar metal and thermal error compensation module of Siemens CNC system. Therefore, the positioning error and thermal drift of Z-axis are compensated. The thermally induced volumetric error in Z-axis is reduced by 80 % after compensation.

Modelling and compensation of fixture locators’ error in CNC milling

Fixture errors are one of the error sources in machining operations. The fixture errors consist of positioning inaccuracies and errors due to workpiece and fixture deformations. Fixture locators’ height error causes incorrect positioning of the workpiece in the fixture and inaccuracy in machined surfaces which can be much more than the locators’ height error. For machining precise workpieces, it is necessary to eliminate these errors. This paper presents a method for modelling and compensating the fixture locators’ height error effect on workpiece machined surfaces. In this method, the planes of workpiece actual coordinate system (ACS) are mathematically modelled in the workpiece theoretical coordinate system (TCS). Using the model, required homogenous transformation matrix for coinciding ACS with TCS and modifying machining toolpath for compensating the errors is generated. The presented model is used to develop a post-processing fixture locator error compensation module, which can modify CNC machining codes to eliminate the effect of fixture locators’ height error on the workpiece machined surfaces. For verifying the presented method, machining simulations and cutting experiments have been performed in this work. The results show that the method can eliminate the effect of fixture locators’ height error on the workpiece machined surfaces considerably.

Compensation of reference surface errors in the machining of free form features

One of the major sources of error in machining operations is the inaccurate positioning of the workpiece in the fixture. Generally, the surface of the workpiece that is in contact with the fixture locators is used as a workpiece reference surface (WRS). Errors in the WRS can lead to improper placing of the workpiece in the fixture and, in turn, to inaccurate machining operations. In this paper, a method is introduced that eliminates the effect of WRS errors on machined features produced in a milling operation. In the proposed method the positioning of the workpiece with consideration of WRS errors is mathematically modelled using homogenous transformation matrices. This model is then used to compensate the effect of WRS errors by modifying the machining toolpath. A post-processing workpiece error compensation module is developed that modifies the initial NC-codes that are generated from a CAM system. The presented method is verified using the simulation software NX and VERICUT and experimental studies. The results confirm that this method can be used to successfully compensate these kinds of errors.