Non-contact Measurement Research Articles

Thermal radiation imaging technology and sensor assisted calculation of sports training technology simulation

With the development of sports training science and technology, the real-time monitoring of athletes’ physical condition and training effect is becoming more and more important. As a non-contact measurement method, thermal radiation imaging technology can capture the changes of athletes’ body surface temperature efficiently and in real time, and provide relevant information about physiological state and performance indicators. This paper discusses the application of thermal radiation imaging technology in sports training to help measure athletes’ physical state and training effect, so as to provide scientific basis for improving training efficiency and reducing sports injuries. In this paper, the high resolution thermal imaging equipment is used to systematically analyze the thermal radiation imaging of athletes, and the physiological index monitoring and data processing technology are combined to dynamically observe the temperature distribution and change characteristics of athletes under different training states. Through comparative analysis, the correlation between the data provided by thermal radiation imaging technology in the training process and the traditional physiological indicators was evaluated. The results show that thermal radiation imaging technology can effectively reflect the heat distribution and local body temperature changes of athletes during training. Compared to traditional physiological feedback data, the technology has significant advantages in quickly identifying excessive fatigue and potential injuries in athletes, especially during high-intensity training, and can provide early warning of associated risks. The application of sensor-assisted measurement system will further enhance the accuracy and effectiveness of sports scientific research and provide important support for athletes’ scientific training and health management.

Research on capacitance-coupled electrical impedance imaging fusion algorithm based on wavelet transform.

Capacitively Coupled Electrical Impedance Tomography (CCEIT), as a new electrical tomography method, effectively overcomes the drawbacks of traditional electrical resistance tomography that requires direct contact with liquids, so as to achieve non-contact measurement. In order to make full use of the complete electrical impedance information (real and imaginary) obtained by CCEIT and further improve the quality of image reconstruction, an image fusion algorithm based on wavelet transform was proposed. First, the Landweber algorithm is used to image the real part information and the imaginary part information, and the wavelet transform algorithm is used to decompose the two images at multiple scales, in which the low-frequency coefficient is fused by the improved logic filter algorithm based on the adaptive threshold, the high-frequency coefficient is selected based on the absolute value maximum method for fusion, and finally, the fused image is obtained by wavelet reconstruction. Numerical simulations and experiments verify the feasibility of the proposed method, and the results show that the relative error is reduced by up to 17% and the correlation coefficient is increased by up to 10%.

Full-Field Measurement of Lightweight Nonlinear Structures Using Three-Dimensional Scanning Laser Doppler Vibrometry

Modern noncontact measurement techniques such as the three-dimensional scanning laser Doppler vibrometry (3D SLDV) are advantageous in measuring vibrations of lightweight, thin-walled aerospace structures, which were conventionally deemed as difficult or not feasible to apply using attached transducers. Nevertheless, the full-field measurements using 3D SLDV are still limited to extracting modal properties of linear structures, while measurements of complex nonlinear structures are rarely reported. This paper aims to extend the full-field measurement capability of 3D SLDV and combines it with a multiple-input–single-output vibration controller to deal with nonlinear structures. An advanced test strategy is introduced, which is capable of obtaining amplitude-dependent resonant frequencies, modal damping ratios, and full-field, multiharmonic mode shapes of nonlinear normal modes (NNMs). Conflicting parameters such as the frequency resolution and measurement time are optimized by combining phase separation and phase resonance testing techniques in a coherent strategy. The capabilities of the proposed nonlinear modal testing strategy are demonstrated on a realistic, large-scale fan blade that exhibits softening behaviors. Two of its NNMs were investigated at larger vibration amplitudes. Its nonlinear modal parameters were successfully extracted and validated, highlighting the time efficiency and data accuracy of the proposed strategy for measuring industrial-scale, lightweight nonlinear structures.

Modelling and Experimental Validation of the Flame Temperature Profile in Atmospheric Plasma Coating Processes on the Substrate

This work presents a characterisation model for the temperature distribution at different substrate depths during the atmospheric plasma spray (APS) coating process. The torch heat flow in this model is simulated as forced convection defined by a surface, a temperature profile, and a convection coefficient. The simulation model considers three plasma temperature profiles of the Al2O3 coating on a 5 mm thickness flat aluminium substrate. The simple and low-cost experimental procedure, based on a thermocouple, measures the plasma plume temperature distribution of the APS coating system, and their results are used to obtain the parameter values of each of the three proposed plasma temperature profiles. The experimental method for in situ non-contact temperature measurements inside the substrate is based on an infrared pyrometry technique and validates the simulation results. The Gaussian temperature profile shows excellent accuracy with the measured temperatures. The Gaussian approach could be a powerful tool for predicting residual stress through a coupled one-way thermal-mechanical analysis of the APS process.

Technology of the fish abundance accounting in fish farming cages using hydroacoustic and software tools

The purpose of the work: improvement of methods of fish number accounting in growing cages and reservoirs of fish farms using hydroacoustic tools and information technologies.Methods used: solving the problem of non-contact (non-invasive) measurement of the number of fish in cages using the hydroacoustic echo integration method. Investigation of the reflective properties of fish using a calibrated scientific echo sounder with a split beam. Carrying out measurements with different numbers of fish in cage. Identification and analysis of factors affecting the accuracy of the echo integration estimation of the number of fishes in cage.Novelty: the elements of novelty are the use of a scientific echo sounder with vertically oriented antenna and split beam to estimate the number of fishes in cages, the algorithm for calculating the number of fishes in cages by the echo integration method, the results of the study of reflective properties of Amur carp and identified optimal measurement conditions that allow hydroacoustic estimates of number with maximum accuracy.Results: the dependences of target strength on the linear size of Amur carp are obtained. An algorithmic scheme for calculating the number of fishes in cages by the hydroacoustic echo integration method has been developed. Experiments were carried out with different numbers of fish in the cage. It is established that the main factors affecting the accuracy of the population estimate are the number of individuals in the cage and the daily distribution of fish. Measurements at night and at concentrations above the threshold (50 pcs./m2) can significantly bring the estimated and true numbers of fish in the cage closer and reduce the variability of density estimates.Practical significance: solving the problem of remote measurement of the fish number in growing cages will allow farmers to optimize feed needs, which, in turn, will ensure rapid growth of fish without overfilling it and not polluting the reservoir.

Research Progress on Precision Tool Alignment Technology in Machining

In the field of numerical control machining, tool alignment technology is a key link to ensure machining accuracy and quality. Tool alignment refers to determining the correct position of the tool relative to the workpiece, and its accuracy directly affects the precision of part machining. With the development of precision machining technology, the research and application of cutting technology are increasingly valued. Tool alignment methods are mainly divided into two categories: contact and non-contact. The contact type tool alignment method relies on direct contact between the tool and the workpiece or tool alignment instrument to measure the position. Among them, the trial cutting method is a traditional contact type tool alignment method that determines the tool position through actual cutting, which is intuitive but inefficient. The contact type tool presetter uses specialized equipment to improve the accuracy and efficiency of tool presetting through contact measurement. The non-contact tool alignment method does not rely on physical contact, while the image method uses image recognition technology to determine the tool position, making it suitable for high-precision applications. The laser diffraction method and the laser direct method use laser technology for non-contact measurement. The laser diffraction method determines the position of the tool by analyzing the diffraction mode of the laser beam, while the laser direct method directly measures the distance between the laser and the tool. This article mainly introduces the classification of tool alignment, commonly used knife alignment methods and common tool alignment devices, as well as the development status of international tool alignment instrument products.

Non-contact measurement of conveyor belt speed based on fast point cloud registration of feature block

Abstract Non-contact, real-time measurement of conveyor belt speed is critical for energy-saving speed regulation and efficient development of coal mine conveyor systems. Existing speed measurement technologies for conveyor systems are often limited by the slippage and wear in contact measurement and complex environmental disturbance. This study introduces three-dimensional point cloud technology into coal flow information detection and innovatively presents a non-contact measurement method of conveyor belt speed based on fast point cloud registration of feature blocks. In the proposed method, a three-dimensional camera is used to capture point cloud data of the dynamically running conveyor belt, and the raw point cloud is preprocessed and segmented into blocks. Then, the C-MANV (curvature and mean angle of normal vectors) features of block point clouds are constructed based on the point cloud neighborhood curvature and the mean angle of neighborhood normal vectors. Finally, an improved block point cloud registration method based on C-MANV features is adopted to achieve the accurate measurement of the dynamic running speed of conveyor belt. Experimental results demonstrate that the proposed method achieves an average relative error of less than 1.8% in high-speed conveyor belt operation with an average processing time of less than 35 ms, which fulfils the accuracy and real-time requirements for conveyor belt speed detection in coal mines. This study provides an effective technical solution for the speed monitoring of coal mine conveyor systems.

Novel hybrid interference and atomic force microscopy

Abstract A novel hybrid microscope which combines an interference microscopic (IM) and an atomic force microscopic (AFM) measurement mode is introduced. It is realised by adding an AFM probe to an IM, where the AFM probe can be mechanically switched in or out of the beam path of the IM. When the AFM cantilever is out of the beam path, the system works in the IM measurement mode for noncontact and fast optical measurements. When the AFM cantilever is in the beam path, it works in the AFM measurement mode, where the AFM tip interacts with sample surfaces for measurements. The deformation of the AFM cantilever induced by the tip-sample interaction force is detected from interference fringes, which are formed by the interference of the light beam reflected from the backside of the AFM cantilever and a reference beam in the IM. This novel design has a high-level synergy of AFM and IM technologies and provides several promising application potentials. For instance, it can bring high-resolution AFM measurements whenever and wherever needed in IM measurements, thus, to complement the lateral resolution limits of IM; The AFM measurement mode can provide measurement results with higher topography fidelity, thus, to offer in-situ reference areal surface metrology for IM measurements. In the paper, design concept, realisation of a prototype instrument, and experimental results illustrating the performance of the prototype instrument are detailed. 

Automated body measurement of beef cattle based on keypoint detection and local point cloud clustering

Abstract Body size parameters of beef cattle are crucial for assessing growth status and breeding value. In actual farming environments, the various postures of beef cattle and complex backgrounds can affect the accuracy and stability of non-contact body measurement methods. Therefore, this paper proposes a novel method called the cattle body measurement method (CBMM), which combines keypoint detection with local point cloud clustering. First, a keypoint detection model based on YOLOv8-SimBiFPN is constructed. This model enhances the feature extraction and fusion capabilities of YOLOv8-pose by introducing SimAM and BiFPN into the backbone and neck networks, respectively, and realizes 2D keypoint detection for beef cattle in various postures. Second, a 3D keypoint-locating algorithm based on Density-based spatial clustering of applications with noise (DBSCAN) is proposed. This algorithm utilizes 2D keypoints, depth maps and camera parameters to generate local point clouds, which are then clustered using DBSCAN to segment cattle body point clouds, thereby relocating the 3D keypoints based on their positional features. Finally, body size parameters are calculated based on the 3D keypoints and distance formulae. In our experiment, the mean average precision (mAP@0.5) of YOLOv8-SimBiFPN reached 99.1% on an Angus beef cattle keypoint detection dataset. The mean absolute percentage errors for measuring beef cattle withers height, hip height, body depth, body length, and oblique body length using the CBMM were 4.37%, 4.96%, 6.47%, 4.84%, and 4.14%, respectively. In summary, our method can achieve non-contact body measurement for beef cattle in a free-moving state with high accuracy and stability.

Investigation of Impedance Determination using Two-Point High Frequency Injection Technique for Non-Contact Voltage Measurement System

Energy management facilities are becoming more necessary due to the growth of smart energy and microgrid technologies. Convenient access to information on energy consumption is an advantage for energy planning and management and secure for the operators. Consequently, these conceive the idea of developing non-contact sensor approach to replace the conventional energy measurement while maintain full accessibility to voltage and current information. This paper proposes a method of non-contact conductive voltage measurement with an automated calibration system. The previous application of non-contact voltage measurement systems in the low-voltage range used a method based on the capacitive coupling principle by covering a metal plate around an insulated power line cable. The important factor of measurement accuracy depends on the determination of impedance occurring between the conductors within the cable and the metal plates attached to the cable surface by insulating as a dielectric. The proposed impedance determination process employs a two-point high-frequency signal injection technique that transmits a reference signal to calculate the established parasitic impedance value. From the high-frequency injection technique, the resulting impedance can be calibrated throughout the reverse calculation to measure the actual voltage of the power line. The experimental results of the proposed voltage measurement system can measure the actual voltage of the power line with the capability to calibrate the impedance due to the variation in the environment and eliminate noise errors from the instability under various conditions.

Theories, Techniques and Materials for Sealing Coalbed Methane Extraction Boreholes in Underground Mines: A Review

To further enhance the intelligent technology, platformisation, and systematisation of coalbed methane extraction sealing technology, this paper analyses the research progress of theories, technologies, and sealing materials related to coalbed methane extraction sealing and systematically summarises the latest achievements of the basic theories, key technologies, and sealing materials of coalbed methane extraction. Considering the increasing mining depth, advancements in intelligent technology, and the evolving landscape of coalbed methane development, it is particularly important to establish a more comprehensive coalbed methane extraction borehole sealing system. Based on this, future development trends and research prospects are proposed: In terms of coalbed-methane-extraction-related theories, there should be a stronger focus on fundamental research such as on gas flow within the coal matrix. For coalbed methane extraction borehole sealing technologies and devices, efforts should be made to enhance research on intelligent, platform-based, and systematic approaches, while adapting to the application of directional long borehole sealing processes. In terms of coalbed methane extraction borehole leakage detection, non-contact measurement and non-destructive monitoring methods should be employed to achieve dynamic monitoring and early warning of methane leaks, integrating these technologies into coalbed methane extraction system platforms. For coalbed methane extraction borehole sealing materials, further development is needed for liquid sealing materials that address borehole creep and the development of fractures in surrounding rock, as well as solid sealing materials with Poisson’s ratios similar to that of the surrounding rock mass.

Combined ResNet Attention Multi-Head Net (CRAMNet): A Novel Approach to Fault Diagnosis of Rolling Bearings Using Acoustic Radiation Signals and Advanced Deep Learning Techniques

The fault diagnosis of rolling bearing acoustic radiation signals holds significant importance in industrial equipment maintenance. It effectively prevents equipment failures and downtime, ensuring the smooth operation of the production process. Compared with traditional vibration signals, acoustic radiation signals have the advantage of non-contact measurement. They can diagnose faults in special conditions where sensors cannot be installed and provide more comprehensive equipment status information. Therefore, to extract the fault characteristic information of rolling bearings from complex acoustic signals, this paper proposes an advanced deep learning model combining Gramian Angular Field (GAF), ResNet1D, ResNet2D, and multi-head attention mechanism, named CRAMNet (Combined ResNet Attention Multi-Head Net), to diagnose the faults of rolling bearing acoustic radiation signals. Firstly, this method includes converting one-dimensional signals into GAF images and performing data standardization and segmentation. Then, the method utilizes ResNet1D to extract features from one-dimensional signals and ResNet2D to extract features from GAF images. Further, it combines the multi-head attention mechanism to enhance feature representation and capture dependencies between different channels. Finally, this paper compares the proposed method with several traditional models (including CNN, LSTM, DenseNet, and CNN-Transformers). Experimental results show that the proposed method performs outstandingly in terms of accuracy and robustness. The combination of residual networks and multi-head attention mechanism in the model significantly enhances its ability to accurately diagnose rolling bearing faults, proving the superiority of the algorithm.

Intelligent dielectric method for evaluating some qualitative characteristics of date fruit

This study aimed to develop an intelligent capacitive system to measure the moisture content of date fruit and to recognize fruit characteristics, such as variety, size, and ripeness. A cost-effective and fast non-contact measurement solution using the capacitive method was employed to create a platform with a variable oscillator to measure the dielectric properties of date fruit after harvest. Different date varieties, namely Zahedi, Ghasb, Mazafati and Medjool, representing dry, semi-dry and wet date fruit, respectively, were selected to model and calibrate the proposed system. Samples of date fruit of each variety were selected at three different ripening stages (Khalal, Rutab and Tamr), ranging from high to low moisture content. Additionally, five distinct moisture contents were determined using the oven method. The moisture content of the date fruit samples ranged from 8.6 % to 86.9 % owing to the selection of four varieties, three ripening stages and five stepwise thermal treatments. After acquiring electronic information, 80 % of the dataset was allocated for training purposes, while the remaining 20 % was reserved for evaluating the final regression model. The results showed that of all the trained machine learning models, Support Vector Regression (SVR) had the highest potential for predicting moisture content at the specified frequencies. The SVR model was fine-tuned by fitting 1824 combinations of hyperparameters over 6 folds. The tuned model's prediction for 20 % of the assigned test data resulted in a coefficient of determination of 88 % compared to the actual moisture content, with a Root Mean Square Error (RMSE) of 9.4 %. Furthermore, the dielectric-based system classified the ripening stages using a Multilayer Perceptron (MLP) model, achieving F1 scores of 87 %, 60 % and 68 % for the Khalal, Rutab and Tamr stages, respectively. The MLP regression model also predicted the geometric mean of the date fruit with a coefficient of determination of 0.82 and an RMSE of 3.05 mm.

Temperature distribution inversion in infrared multispectral imaging based on ensemble network.

Temperature distribution can be acquired through non-contact temperature measurement using multispectral imaging. However, the challenge lies in radiometric temperature inversion owing to the unknown emissivity. Despite the promising results demonstrated by traditional algorithms and neural networks, enhancing the precision and reliability of temperature inversion remains a challenge. To tackle these challenges, in this work, we propose the use of ensemble learning for temperature distribution inversion in infrared multispectral imaging. The network comprises a base-learner and a meta-learner, trained to establish the nonlinear relationship between temperature and multispectral distribution measurements. Moreover, the network architecture exhibits high robustness against noise arising in the testing environment. Simulations and real experiments on multispectral imaging measurements illustrate that ensemble learning can be a potent tool for multispectral imaging radiation temperature distribution measurement, achieving superior inversion performance compared to other neural networks. The reproducible code will be available at https://github.com/shuowenyang/Temperature-Inversion.

An impulsive noise filter for rail vibration measurements using a laser Doppler vibrometer on a moving platform

This paper proposes a new impulsive noise (i.e., IN) filter for non-contact rail vibration measurements performed by a laser Doppler vibrometer (i.e., LDV) placed on a moving rail car. The development of the filter is motivated by the fact that such measurements can be contaminated by IN, which is caused by a high level of speckle noise due to the movement of the LDV. Consequently, some portions of the LDV signals can be distorted, leading to missing measurements from some rail segments. The proposed filter consists of two main steps: (i) detecting the signal segments containing the IN (i.e., detection step), and (ii) replacing the detected segments to estimate their IN-free values (i.e., estimation step). The performance of the filter was evaluated on a synthetic signal, which was created by combining a rail vibration signal in the vertical direction (i.e., vibration component) with a speckle noise signal of an LDV placed on a moving platform (i.e., noise component). The reason to use a synthetic signal was to know the vibration and noise components separately. This allowed for the examination of IN’s behavior, which showed that IN manifests itself as artificial peaks as well as high-frequency noise in the proximity of artificial peaks (i.e., artificial fluctuations). Accordingly, the proposed filter was designed to detect and replace both artificial peaks and fluctuations. Qualitative and quantitative evaluations of the filtered synthetic signal showed that the proposed filter exhibited good performance in mitigating the IN.

Numerical Investigation on the Influence of Turbine Rotor Parameters on the Eddy Current Sensor for the Dynamic Blade Tip Clearance Measurement.

Eddy current sensors are increasingly being used to measure the dynamic blade tip clearance in turbines due to their robust anti-interference capabilities and non-contact measurement advantages. However, the current research primarily focuses on enhancing the performance of eddy current sensors themselves, with few studies investigating the influence of turbine rotor parameters on the measurements taken by these sensors for dynamic blade tip clearance. Hence, this paper addresses this gap by using COMSOL Multiphysics 6.2 software to establish a finite model with circuit interfaces. Additionally, the model's validity was verified through experiments. This model is used to simulate the voltage output of the sensor and the measurement of dynamic blade tip clearance under various rotor parameters. The results indicate that the length and number of blades, as well as the hub radius, significantly affect the sensor voltage output in comparison to rotation speed. Furthermore, we show that traditional static calibration methods are inadequate for measuring dynamic blade tip clearance using eddy current sensors. Instead, it is demonstrated that incorporating rotor parameters into the calibration of eddy current sensors can enhance the accuracy of dynamic blade tip clearance measurements.

Potential of Non-Contact Dynamic Response Measurements for Predicting Small Size or Hidden Damages in Highly Damped Structures.

Vibration-based structural health monitoring (SHM) is essential for evaluating structural integrity. Traditional methods using contact vibration sensors like accelerometers have limitations in accessibility, coverage, and impact on structural dynamics. Recent digital advancements offer new solutions through high-speed camera-based measurements. This study explores how camera settings (speed and resolution) influence the accuracy of dynamic response measurements for detecting small cracks in damped cantilever beams. Different beam thicknesses affect damping, altering dynamic response parameters such as frequency and amplitude, which are crucial for damage quantification. Experiments were conducted on 3D-printed Acrylonitrile Butadiene Styrene (ABS) cantilever beams with varying crack depth ratios from 0% to 60% of the beam thickness. The study utilised the Canny edge detection technique and Fast Fourier Transform to analyse vibration behaviour captured by cameras at different settings. The results show an optimal set of camera resolutions and frame rates for accurately capturing dynamic responses. Empirical models based on four image resolutions were validated against experimental data, achieving over 98% accuracy for predicting the natural frequency and around 90% for resonance amplitude. The optimal frame rate for measuring natural frequency and amplitude was found to be 2.4 times the beam's natural frequency. The findings provide a method for damage assessment by establishing a relationship between crack depth, beam thickness, and damping ratio.

Image super-resolution reconstruction of vast-receptive-field pixel attention for precision measurement

Abstract Compared with traditional contact precision measurement, vision-based non-contact precision measurement has the features of low cost and flexible multi-point information extraction, but how to ensure the measurement accuracy of vision-based non-contact precision measurement is an urgent problem. Traditional thinking often focuses on hardware upgrades to improve image resolution, but this brings high costs and is limited by the physical characteristics of the hardware itself. In this paper, we start from the software aspect to improve the image resolution by using the super-resolution reconstruction algorithm and propose an image super-resolution reconstruction algorithm—Swin Transformer with a Vast-receptive-field Pixel Attention, which combines the vast-receptive-field pixel attention mechanism with the Swin Transformer self-attention mechanism, focuses on the learning of the high-frequency information features of the image. Experiments are conducted both in public datasets and real measurement images. Extensive experimental validation shows that the model can obtain more edge and high-frequency detail features in public datasets, and the objective evaluation index on Set5, Set14, B100, Urban100, and Manga109 datasets is improved by 0.06 dB on average compared with the existing algorithms. In actual measurements, the algorithm in this paper for USAF1951 resolution tablet, image super-resolution reconstruction image in the horizontal and vertical direction of the measurement accuracy increased by an average of 6.97%, the horizontal and vertical direction of the relative measurement accuracy of an average of 30.20% improvement. This study provides a potential development direction for vision-based non-contact precision measurement.

Investigation of a Camera-Based Contactless Pulse Oximeter with Time-Division Multiplex Illumination Applied on Piglets for Neonatological Applications.

(1) Objective: This study aims to lay a foundation for noncontact intensive care monitoring of premature babies. (2) Methods: Arterial oxygen saturation and heart rate were measured using a monochrome camera and time-division multiplex controlled lighting at three different wavelengths (660 nm, 810 nm and 940 nm) on a piglet model. (3) Results: Using this camera system and our newly designed algorithm for further analysis, the detection of a heartbeat and the calculation of oxygen saturation were evaluated. In motionless individuals, heartbeat and respiration were separated clearly during light breathing and with only minor intervention. In this case, the mean difference between noncontact and contact saturation measurements was 0.7% (RMSE = 3.8%, MAE = 2.93%). (4) Conclusions: The new sensor was proven effective under ideal animal experimental conditions. The results allow a systematic improvement for the further development of contactless vital sign monitoring systems. The results presented here are a major step towards the development of an incubator with noncontact sensor systems for use in the neonatal intensive care unit.

Position Servo Control of Electromotive Valve Driven by Centralized Winding LATM Using a Kalman Filter Based Load Observer

The exhaust gas recirculation (EGR) valve plays an important role in improving engine fuel economy and reducing emissions. In order to improve the positioning accuracy and robustness of the EGR valve under uncertain dynamics and external disturbances, this paper proposes a positioning servo system design for an electromotive (EM) EGR valve based on the Kalman filter. Taking a novel valve driven by a central winding limited angle torque motor (LATM) as the object, we have fully considered the influence of the motor rotor position and load current, as well as the magnetic field saturation and cogging effect, improved the existing LTAM model, and derived accurate torque expression. The parameter uncertainty of the above internal model and the external stochastic disturbance were unified as “total disturbance”, and a Kalman filter-based observer was designed for disturbance estimations and real-time feed-forward compensation. Furthermore, using non-contact magnetic angle measurements to obtain accurate valve position information, a position control model with real-time response and high accuracy was established. Numerous simulated and experimental data show that in the presence of ± 25% plant model parameter fluctuations and random shock-type disturbances, the servo system scheme proposed in this paper achieves a maximum position deviation of 0.3 mm, a repeatability of positioning accuracy after disturbances of 0.01 mm, and a disturbance recovery time of not more than 250 ms. In addition, the above performance is insensitive to the duration of the disturbance, which demonstrates the strong robustness, high accuracy, and excellent dynamic response capability of the proposed design.