Error Compensation Model Research Articles

The Analysis and Compensation Experiment of Linear Feed Axis Positioning Error Based on Matrix Operation

The positioning accuracy of the linear feed axis is a key factor affecting the machining accuracy of CNC machine tools. The generation process of the positioning error of the linear feed axis is expounded. The Z axis of the vertical linear feed axis of the machining center is the research object, the positioning error analytical matrix is established, and the positioning error calculation of the Z axis of the vertical linear feed axis is completed based on the principle of solving the inhomogeneous linear equation. The positioning error detection of the vertical linear feed axis Z axis of the CNC machining center is carried out. Based on the characteristics of the error detection data, an error compensation model is constructed. , the variance of the positioning error detection point after compensation is reduced to 1.562, which meets the needs of precision machining.

Joint torque estimation method of industrial robot by combining error compensation model based on LSTM and iterative weighted parameter identification

It is essential for human-robot interaction to accurate joint torque value estimation of industrial robots without force/torque (F/T) sensors. The most common method to estimate the value of robot joint torque is based on dynamic model parameter identification. However, no matter how elaborate the modeling methods are used, there are always uncertainty errors when robot’s joint rotation direction changes. In this paper, an identification framework is proposed to estimate optimal model parameters, and a deep learning network is proposed to compensate for the uncertainty errors caused by the unmodeled dynamics part. At first, the dynamic parameters are estimated during the Least Squares (LS) identification process considering the noise influence and data outliers, and the nonlinear friction model is unified into the identification framework. Secondly, based on Long-Short Term Memory (LSTM) network, an error compensation model (ECM) is proposed to establish the mapping relationship between the joint motion and the identification errors. Finally, a 6-DOF robot is used for parameter identification and ECM validation. Experimental results show that the proposed identification method is better than the LS method. Compared with the torque value estimated by the identification method, the proposed ECM can compensate for the uncertainty errors and improve the torque estimation accuracy.

A temperature-sensitive points selection method for machine tool based on rough set and multi-objective adaptive hybrid evolutionary algorithm

Reasonable deployment of temperature sensors is the key to accurately monitoring the temperature field of machine tools and improving the accuracy of thermal error prediction and compensation models. To determine the optimal deployment location of sensors, this paper proposes a temperature-sensitive points selection method tightly coupled with rough set and multi-objective optimization. Firstly, the importance of each temperature measurement point to the thermal error is calculated based on the rough set, and information entropy is introduced to amplify the importance difference among adjacent measurement points at the same heat source. Then, with the temperature measurement points groups as the variables, the number of temperature measurement points in the group, and the information importance of the group as the objectives, a multi-objective attribute reduction model is established, which transforms the temperature-sensitive points selection problem into a discrete multi-objective optimization problem. Finally, a multi-objective adaptive hybrid evolutionary algorithm is proposed, which designs a population initialization method based on mutual information and interval probability, and dynamic adaptive evolutionary parameters to achieve optimal temperature-sensitive points selection. Experiments on the high-speed dry hobbing machine verify the superiority and effectiveness of the proposed method.

Similarities and differences between exosystem-model-based disturbance observer and model error compensator

Similarities and differences between exosystem-model-based disturbance observer and model error compensator

Indoor altitude estimation assisted by inertial compensation and online floor modeling

Abstract Indoor pedestrian altitude information plays a key role in such applications as emergency relief and military reconnaissance. However, height errors of inertial positioning grow without boundary because of the altitude channel divergence of the Strap-down Inertial Navigation System (SINS). This article proposes a new vertical location method based on the foot-mounted Inertial Measurement Unit (IMU) and online floor modeling. First, the number of stairs at each step is calculated after the height of each step is corrected by the error compensation model. Second, the prior height is estimated by utilizing the number of stairs and the stair height. Then, the key point library related to the floor is built online. Finally, the error correction of the vertical displacement is carried out by matching the prior height with the key point library. The gait classification shows that the accuracy based on the error compensation model can reach up to 99%. Moreover, the maximum attitude error is less than 1 m and the accumulated vertical positioning errors are eliminated completely when a pedestrian walks up and down inside a multi-story building, and all these have verified accuracy and robustness of vertical indoor positioning.

On-machine inspection and compensation for thin-walled parts with sculptured surface considering cutting vibration and probe posture

Abstract On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dynamic cutting conditions, theoretical modeling of profile inspection usually leads to insufficient adaptation, which causes inaccuracy problems. To address the above issues, a novel coupled model for profile inspection is proposed by combining the theoretical model and the data-driven model. The key process is to first realize local feature extraction based on the acquired vibration signals. The hybrid sampling model, which fuses geometric feature terms and vibration feature terms, is modeled by the lever principle. Then, the weight of each feature term is adaptively assigned by a multi-objective multi-verse optimizer. Finally, an inspection error compensation model based on the attention mechanism considering different probe postures is proposed to reduce the impact of pre-travel and radius errors on inspection accuracy. The anisotropy of the probe system error and its influence mechanism on the inspection accuracy are analyzed quantitatively and qualitatively. Compared with the previous models, the proposed hybrid profile inspection model can significantly improve the accuracy and efficiency of on-machine sampling. The proposed compensation model is able to correct the inspection errors with better accuracy. Simulations and experiments demonstrate the feasibility and validity of the proposed methods. The proposed model and corresponding new findings contribute to high-precision and efficient on-machine inspection, and help to understand the coupling mechanism of inspection errors.

Machine learning for aspherical lens form accuracy improvement in precision molding of infrared chalcogenide glass

Precision glass molding (PGM) is an effective approach to manufacturing infrared chalcogenide glass (ChG) aspherical lens with complex shapes. However, infrared ChG aspherical lens often experiences form error in the designed profile and the final profile obtained by PGM. To reduce the form error of infrared ChG aspherical lens in the PGM process, a form error compensation model based on the random forest (RF) algorithm is proposed. The infrared ChG aspherical lens profile was first machined on an electroless nickel-phosphorus (Ni–P) plating to serve as the mold for PGM. After molding, the profile data of the lens was extracted, and a compensation model based on RF was constructed to optimize the model parameters using the evaluation parameters such as root mean square error (RMSE), coefficient of determination (R2), and out-of-bag (OOB). Finally, the generated compensated profile based on the compensation model was used for the compensation machining of the mold. Through this compensation approach, we have demonstrated a substantial 63.5 % reduction in the form error of the fabricated infrared ChG aspherical lens, decreasing the Peak-to-Valley (PV) value from 1.04 μm to 0.38 μm.

A design approach of fractional model error compensator for fractional dynamical systems with polytopic uncertainty and disturbance

A design approach of fractional model error compensator for fractional dynamical systems with polytopic uncertainty and disturbance

A Design of Position Control System with Model Error Compensator

A Design of Position Control System with Model Error Compensator

Modeling spatiotemporal temperature dynamics of large-format power batteries: A multi-source information fusion approach

Uneven temperature distributions can significantly impact battery performance and cycle life. Industrial applications, in particular, where unknown disturbances and limited sensors exist, pose significant challenges for accurately estimating the battery temperature field. This work proposes a multi-source information fusion framework for capturing the spatiotemporal temperature dynamics of pouch-type Lithium-ion batteries. First, we develop a system model of the battery thermal process based on physical insights, considering unknown parameter deviations and unmodeled dynamics. Next, we use the Galerkin-spectral method to expand the spatiotemporal variable and reduce the original infinite-dimensional system to a low-order model containing the most important system modes. The unknown component of the model is then entirely stripped and integrated into a nonlinear term. To close the reality gap of the physics-based model, we subsequently develop an error compensation model that learns the dynamic behavior from sparse observations. Ultimately, our framework design yields a fusion-driven model for reliable temperature field prediction. Simulations and experimental data validate the superior performance and generalization capability of the proposed method.

A generalized data assimilation architecture of digital twin for complex process industrial systems

As one of the critical cores of digital twin (DT), data assimilation (DA) can maintain consistency and synchronization between DT and physical system. Kalman filtering is a common DA method, but its estimation performance is deteriorated by factors such as model inaccuracy and time-varying noise covariance in practical applications. The errors caused by these multiple uncertainties are all coupled to the measurements, which augments the difficulty for DT to obtain physical system information. In order to tackle the DA problem with multiple uncertainties, this paper proposes a generalized DA architecture for DT in sophisticated process industry. First, by combining Stein variational gradient descent and nonlinear Bayesian filtering paradigm, a recursive estimation framework is established, which has higher accuracy in estimating the noise covariance compared to traditional methods. Second, to effectively deal with model inaccuracy by using filtering residuals containing time-varying noise, we propose a neural network and modified wavelet-based model error compensation (NNMW-MEC) block. Based on the modified wavelet technique, the filtering residual denoising built in NNMW-MEC can better cope with time-varying noise compared to existing wavelets, and extract the low-frequency signal involving model error information from noisy residual smoothly. In addition, because of the neural network-based state-compensation subblock, NNMW-MEC has more outstanding ability in compensating the state deviations with large changing range. Finally, we take the boiler system in a coal-fired power plant as an example to verify the effectiveness of our architecture. Experimental results show that the DA architecture proposed in this paper can improve the estimation performance of DT under inaccurate models and uncertain noise statistics.

Error analysis and modeling of the racetrack magnetohydrodynamic linear motion sensor

Micro-vibration measurement methods for spacecraft structures mainly include the use of an accelerometer, laser, and magnetohydrodynamic (MHD) measurement methods. The micro-vibration measurement sensor developed based on the MHD measurement method has no mechanical wear between internal components, a fast high-frequency vibration response, and strong anti-interference properties. To reduce the measurement error of the racetrack MHD linear motion sensor developed in the laboratory, this paper investigated the sensor error, analysed the error source, combined the BP neural network optimized by particle swarm optimization (PSO) with variational mode decomposition (VMD), used the VMD-PSO-BP neural network to establish the error compensation model of the racetrack MHD linear motion sensor, and combined the PSO-BP neural network with wavelet threshold de-noising (WTD). The WTD-PSO-BP and RBF neural networks were used to develop the error compensation model of the racetrack MHD linear motion sensor. Comparing the three models, the experimental results show that the VMD-PSO-BP model has the best compensation effect. The mean absolute error of the output signal of the racetrack MHD linear motion sensor compensated by the VMD-PSO-BP neural network model was 1–2 times lower than that before compensation, the signal-to-noise ratio was 10 times higher on average, and the correlation coefficient was more than 0.95.

Elastodynamic modeling and structural error compensation for a 5-DOF parallel mechanism with subclosed loops

In order to reduce the adverse effects of structural errors on a novel 5-degrees of freedom (DOF) parallel kinematic mechanism (PKM) with subclosed loops, studies on the elastodynamic modeling and structural error compensation are conducted in this paper. First, considering that introduction of subclosed loops significantly increased the complexity and difficulty of elastodynamic modeling, an equivalent unit modeling method for the branch chain with subclosed loop is proposed based on force analysis and deformation compatibility condition. Then, elastodynamic model of the whole 5-DOF PKM is established with the matrix structural analysis based method. Second, by defining a six-dimensional error (three translational errors and three rotational errors) for each node, structural error model of the PKM is constructed based on closed-loop vector method. To consider the coupling effects of static and dynamic structural errors, the dynamic structural errors derived by above elastodynamic model would be substituted into the structural error model along with given static structural errors. Finally, structural error compensation optimization model is established and structural errors are compensated by adjusting the driven parameters based on the improved particle swarm optimization (PSO) algorithm. Numerical simulations are conducted to verify the proposed elastodynamic modeling and structural error compensation methods. After compensation, the position errors along x, y, and z directions are respectively decreased by 68.036%, 81.409%, and 90.625%, and the angle errors around x and y directions are respectively decreased by 91.426% and 62.042%.

Robust Cooperative Transport System with Model Error Compensator Using Multiple Robots with Suction Cups

In cooperative transport systems, multiple robots work together to transport objects that are difficult to transport with a single robot. In recent years, multi-robot systems that cooperate to transport objects have been researched. However, during the transfer of objects, misalignment occurs between the ideal and actual grasp positions. In an automatic transport system, a grasping error can cause an error in the trajectory of the object, significantly reducing the transport efficiency. In this paper, a control system that allows robust cooperative transport control using a model error compensator is proposed for a leader–follower system in which the transported object is the virtual leader and the followers are ideally arranged. This system adds robustness to the operation of a conventional cooperative transport system by using the ideal formation of robots. The effectiveness of the proposed method was evaluated through cooperative transport experiments using two ideal formations for passing through a narrow entrance. The cooperative transport system could not pass through the narrow entrance using the conventional method; however, the system using the compensator passed through the narrow entrance smoothly.

Hybridized gated recurrent unit with variational mode decomposition and an error compensation mechanism for multi-step-ahead monthly rainfall forecasting.

Highly accurate monthly rainfall predictions can provide early warnings for rain-related disasters, such as floods and droughts, and allow governments to make timely decisions. This paper proposes a two-phase error compensation model based on a gated recurrent unit (GRU), variational mode decomposition (VMD), and error compensation mechanism (ECM) (GRU-VMD-ECM) for accurate multi-step-ahead monthly rainfall forecasts. In the first phase, the GRU model is used to make an initial monthly rainfall prediction, and the error series is extracted. In the second phase, the error series is decomposed into eight subseries using the VMD method. Each subseries is then input into the GRU model to build different forecasting models. These predicted error sequences are added to the initial prediction results to obtain the final forecast. The model's performance is tested using six evaluation indicators based on Beijing's monthly rainfall data from 1951 to 2018. The results show that the error compensation mechanism significantly improved the prediction accuracy, particularly in the Nash-Sutcliffe efficiency (NSE) of single-step-ahead prediction which recorded a substantial increase of 281.16% from 0.259981 to 0.990944, as well as a decrease in root mean square error (RMSE) from 2.257580 to 0.249746. Furthermore, the GRU-VMD-ECM model outperforms the RF, GRU-CNN, and VMD-GRU models in terms of precision across all forecasting horizons. These findings highlight the potential of the GRU-VMD-ECM model in providing highly accurate monthly rainfall predictions for early warnings and informed decision-making by governments.

A bi-vector calibrated algorithm of fiber optic gyroscopes aided magnetometers based on inclinometer

Inclinometers based on magnetometers are widely applied in oil and gas well measurements and have poor anti-interference ability. Affected by the drilling tools and stratum, the magnetized inclinometer will cause the output deviation of three-axis magnetometer. Therefore, periodic calibration before engineering applications is a necessary operation process. Fiber optic gyroscopes, with their characteristics of high precision and anti-interference, complement the advantages of magnetometers, which have improved the precision and reliability of the borehole attitude. In addition, a navigation grade three-axis fiber optic gyroscope provides convenience for periodic calibration of magnetometer. Consequently, a bi-vector field correction method based on a fiber-optic gyroscope and magnetometer inclinometer is presented in this study. First, the geometric relation between the seven elements of the geomagnetic field and Earth’s angular velocity was deduced with reference to the right-hand criterion. Second, a bi-vector field error compensation model was established. Finally, the simulation and experimental results demonstrated the effectiveness of the proposed calibration method.

Plug-and-play positioning error compensation model for ripple suppressing in industrial robot polishing.

The industrial robot-based polisher has wide applications in the field of optical manufacturing due to the advantages of low cost, high degrees of freedom, and high dynamic performance. However, the large positioning error of the industrial robot can lead to surface ripple and seriously restrict the system performance, but this error can only be inefficiently compensated for by measurement before each processing at present. To address this problem, we discovered the period-phase evolution law of the positioning error and established a double sine function compensation model. In the self-developed robotic polishing platform, the results show that the Z-axis error in the whole workspace after compensation can be reduced to ±0.06m m, which reaches the robot repetitive positioning error level; the Spearman correlation coefficients between the measurement and modeling errors are all above 0.88. In the practical polishing experiments, for both figuring and uniform polishing, the ripple error introduced by the positioning error is significantly suppressed by the proposed model under different conditions. Besides, the power spectral density (PSD) analysis has shown a significant suppression in the corresponding frequency error. This model gives an efficient plug-and-play compensation model for the robotic polisher, which provides possibilities for further improving robotic processing accuracy and efficiency.

A portable microfluidic photometric detection method based on enzyme linked immunoscatter enhancement

Currently, the combination of smart phones and microfluidic chips is a commonly used device for point-of-care testing (POCT) detection. Enzyme linked immunosorbent assay (ELISA) is an effective way to detect specific proteins in disease. Because the detection accuracy of smartphone cameras is difficult to directly replace high-precision spectral devices, the combination of smartphones and ELISA has not been widely used. Therefore, this paper proposes a microfluidic photometric detection method based on ELISA scattering enhancement. Firstly, the scattering characteristics of IMB are mined, and the optimal value of absorbance error compensation parameter is obtained. Secondly, the absorbance error compensation model based on scattering enhancement characteristics is established to improve the image acquisition accuracy of smart phones. Finally, the microfluidic photometric detection chip is developed, and the optical path system, optical path adjustment system and POCT detection App of smart phone are designed. The optimal compensation parameters of IMB were obtained based on simulated samples, and the linearity of absorbance and concentration increased by 22.6% after compensation. In the IL-6 sample experiment, the detection results of the platform in this paper had a good linear correlation with IL-6 sample concentration, and the linear correlation coefficient was above 0.95459. At the same time, the detection limit and accuracy meet the detection requirements. Therefore, with the participation of smart phones and microfluidic chips, problems such as difficult carrying and complex operation in traditional ELISA daily detection have been solved, laying a foundation for the future promotion and application of ELISA based POCT platform.

Intensity response model and measurement error compensation method for chromatic confocal probe considering the incident angle

Chromatic confocal measurement technology is extensively applied in precision surface profiling within the industrial domain. However, due to limited measurement space and the complex shape of surfaces, it becomes challenging to ensure that the optical axis is aligned with the normal direction of the surface, particularly in regions with steep slopes. Although the reflected light can be captured when the probe is tilted within the numerical aperture angle, it introduces measurement uncertainty. In this paper, we investigate the reflective characteristics of incident light and develop a chromatic confocal light intensity response model that accounts for the incident angle. This model is based on the Rayleigh-Sommerfeld diffraction and dispersion theory. Simulation results demonstrate that the tilt angle results in a shift in the peak wavelength of the reflected spectrum. To compensate for the measurement error caused by this wavelength shift, we establish a calibration curve representing the angle and wavelength variation for the chromatic confocal measurement system. Experimental findings reveal that after compensation, the measurement error attributable to the incident angle is reduced by approximately 72 %.

Target Localization and Grasping of NAO Robot Based on YOLOv8 Network and Monocular Ranging

As a typical visual positioning system, monocular ranging is widely used in various fields. However, when the distance increases, there is a greater error. YOLOv8 network has the advantages of fast recognition speed and high accuracy. This paper proposes a method by combining YOLOv8 network recognition with a monocular ranging method to achieve target localization and grasping for the NAO robots. By establishing a visual distance error compensation model and applying it to correct the estimation results of the monocular distance measurement model, the accuracy of the NAO robot’s long-distance monocular visual positioning is improved. Additionally, a grasping control strategy based on pose interpolation is proposed. Throughout, the proposed method’s advantage in measurement accuracy was confirmed via experiments, and the grasping strategy has been implemented to accurately grasp the target object.