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The performance evaluation of terahertz measurement technology on complex curved surfaces, such as blades, holds significant importance. Industrial robots can be equipped with probes to realise this technology. However, overcoming the limitation of robot encoder modification and addressing the problem of poor pose accuracy in robot-assisted terahertz detection is one of the main challenges affecting the evaluation results . This paper proposes a compensation method combining model and optimisation-based machine learning for robot-assisted terahertz detection. First, robot trajectory planning that meets the requirements for terahertz measurement of blades is described, and a NURBS surface model of the blade and efficiently plan the probe’s orientation is proposed. Second, a rapid and precise method for optimising trajectory pose accuracy, combining the robot’s D-H model with calibration compensation based on Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM), is proposed. Third, prediction and compensation method is simulated and validated against the trajectory planning results. Finally, precision evaluation is conducted based on the calibration outcomes, and the robot assisted terahertz blade detection experiment is carried out. The results show that compared to the other compensation method, this approach improves both precision and efficiency, laying the foundation for detection devices in terahertz measurement research on curved surfaces. [ABSTRACT FROM AUTHOR]

The quartz flexible accelerometer (QFA) is a critical component in navigation-grade strapdown inertial navigation systems (SINS) due to its bias error, which significantly impacts the overall navigation accuracy of SINS. Temperature variations induce dynamic changes in the bias and scale factor of QFA, leading to a degradation of the navigation accuracy of SINS. To address this issue, this paper proposes a temperature error compensation method based on a non-uniform mutation strategy genetic algorithm (NUMGA) and a polynomial curve model (PCF). Firstly, the temperature bias mechanism of QFA output is analyzed, and a polynomial temperature error model is established. Then, the NUMGA is utilized to identify the model parameters using the −20–40 °C test data, seeking the optimal parameters for the polynomial. Finally, the compensation parameters are used for cold start static test verification. The results demonstrate that the temperature compensation model based on NUMGA-PCF can automatically select the optimal parameters, which enable the model to exhibit a stable decreasing trend on the adaptation curve without multiple fluctuations. Compared to the traditional GA temperature compensation model, the compensation errors in the three axes of QFA in SINS are reduced by 612.24 μg, 60.82 μg, and 875.82 μg, respectively. Before the 20th generation, there are no decrease in convergence speed observed with the in-crease of population diversity. Within the −20–40 °C temperature range, the average values and standard deviations of QFA for the three optimized axes can be maintained below 0.1 μg by using this compensation model. [ABSTRACT FROM AUTHOR]

A characteristic feature of redundancy in the motor system is the ability to compensate for the failure of individual motor elements without affecting task performance. In this study, we examined the pattern and variability in error compensation between motor elements during a virtual task. Participants performed a redundant cursor control task with finger movements. In some conditions, we induced ‘failure’ by using a haptic glove to apply forces to constrain the motion of one of four fingers (index, middle, ring, or little). Our results showed that (i) other fingers increased their range of motion to compensate for the failure of a specific finger, with most of this compensation coming from non-adjacent fingers, and (ii) there was greater trial-to-trial variation in how the task was achieved as indexed by the higher null space variability when the middle and ring fingers were constrained. These results highlight the key role of the interdependence between motor elements in determining error compensation patterns and the variability in coordination patterns. [ABSTRACT FROM AUTHOR]

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Airborne gravity gradient dynamic measurement error compensation is a crucial aspect of data processing in gravity gradient dynamic measurements. This study introduces a deep learning approach based on a residual backpropagation (Res-BP) neural network for post-error compensation in airborne gravity gradient dynamic measurement. The network employs residual connections to facilitate identity mapping, thereby enabling gradient propagation across layers. This strategy preserves the original information while acquiring additional information through nonlinear operations, effectively mitigating the gradient vanishing issue and enhancing the neural network's fitting capability. The method proposed in this paper is applied to both simulation data from a gravity gradiometer and high-altitude dynamic measured data of an airborne gravity gradient. Compared to traditional neural network multilayer perceptron, the Res-BP method significantly improves compensation accuracy through its application in high flight experiment of the southern section of Zhangguangcai Ridge on the western side of Mudanjiang City, Heilongjiang Province. [ABSTRACT FROM AUTHOR]

To enhance the synchronization performance of multiple clocks in robotic communication systems, a study was conducted on a distributed clock synchronization compensation scheme. Quantitative compensation and dynamic compensation algorithms for clock errors were designed. The synchronization compensation scheme, based on the IgH EtherCAT communication protocol stack, was implemented, and trajectory tracking and communication performance tests were conducted on a six-axis robotic arm. The results show that the application of this synchronization scheme improves the circular trajectory precision of the robotic arm's end effector compared to non-IgH synchronization schemes. The clock synchronization error is reduced to 54 ns, with the error tolerance within ± 200 ns. It is evident that the compensation scheme enhances clock synchronization performance and meets the application requirements of the robotic arm. [ABSTRACT FROM AUTHOR]

Industrial robotic arms are often subject to significant end-effector pose deviations from the target position due to the combined effects of nonlinear deformations such as link flexibility, joint compliance, and end-effector load. To address this issue, a study was conducted on the analysis and compensation of end-position errors in a six-degree-of-freedom robotic arm. The kinematic model of the robotic arm was established using the Denavit–Hartenberg (DH) parameter method, and a rigid–flexible coupled virtual prototype model was developed using ANSYS and ADAMS. Kinematic simulations were performed on the virtual prototype to analyze the variation in end-effector position errors under rigid–flexible coupling conditions. To achieve error compensation, an approach based on an Enhanced Crayfish Optimization Algorithm (ECOA) optimizing a BP neural network was proposed to compensate for position errors. An experimental platform was constructed for error measurement and validation. The experimental results demonstrated that the positioning accuracy after compensation improves by 75.77%, fully validating the effectiveness and reliability of the proposed method for compensating flexible errors. [ABSTRACT FROM AUTHOR]

Under regional environmental conditions such as open-pit mines and construction sites, there are usually fixed GNSS measurement points. Around these fixed stations, there are also mobile GNSS measurement modules. These mobile measurement modules offer advantages such as low power consumption, low cost, and large data volume. However, due to their low accuracy, these modules can only provide approximate positions as monitoring data, such as for vehicle management in open-pit mines. To extract more information from the existing large volume of low-accuracy data, it is necessary to process these low-accuracy data. Under conditions of the same time and space in a small area, factors affecting measurement accuracy can be comprehensively considered. By analyzing the temporal GNSS data within the same spatiotemporal small region and understanding the variation patterns of measurement errors, a general equation for measurement error variation can be formulated. Using filtering methods, the data quality can be improved. Through the analysis of the experimental data in this study, it was found that the variation patterns of measurement data obtained by devices of the same accuracy during the same time period are generally consistent. After applying filtering methods, the measurement accuracy of each station improved by up to approximately 95.9%, with a minimum improvement of approximately 84.4%. Under the condition of a 95% confidence level, the reliability increased by up to approximately 73.2%, with a minimum improvement of approximately 58.2%. These experimental results fully demonstrate that under regional spatiotemporal conditions, the temporal data obtained by GNSS measurement devices with similar accuracy exhibit similar error distribution patterns. Applying the same filtering method can significantly improve the accuracy and reliability of measurement data. [ABSTRACT FROM AUTHOR]

Coaxial rotor helicopters have many advantages and have a wide range of civilian and military applications; however, there is a risk of blade collision between the upper and lower rotor blades, and the challenge still exists in balancing rotor parameters and flight control. In this paper, a blade tip distance measurement method based on coded ultrasonic ranging and phase triggering is proposed to tackle this measurement environment and expand the application of ultrasonic ranging in high-speed dynamic measurement. The time of flight (Tof) of coded ultrasonic ranging is calculated by the amplitude threshold improvement method and cross-correlation method, and the sound velocity is compensated by a proposed multi-factor compensation method. The static distance error of coded ranging with different codes are all within ±0.5 mm in the range of 10–1000 mm. The measurement error characteristics under different trigger phases and different rotational speeds are studied, and the error model is fitted by the back-propagation neural network method. After compensation, the vertical distance measurement errors are within ±2 mm in the range of 100–1000 mm under the condition that the rotational speed of the blade is up to 1020 RPM. It also provides a potential solution for other high-speed measurement problems. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The framework of the article is a theoretical investigation of the Doppler effect on electromagnetic signals. The target application is relevant to satellite missions with resource-constrained systems based on low-cost hardware platforms and the innovation of the paper is on the error compensation strategy and on the proposal of an approximate polynomial model. In particular, the scenario of interest considers a non-cooperative emitter of electromagnetic waves on the surface of the Earth and a receiver able to perform Angle of Arrival measurements embarked on a satellite in Low Earth Orbit. Assuming the problem of geolocation is solved, and starting from the kinematics of the satellite and its relative motion with respect to the emitter, the correction on the frequency is derived with respect to the orbital parameters, which is a common representation of data for satellite systems. The purpose of the paper is not only to provide a reference model to predict the necessary correction on the frequency of the signal due to the Doppler effect, but also to present a real-time strategy for compensation in the pipeline of signal processing after its geolocation. The main goal of the paper is to give real-time estimation and compensation strategy for the Doppler factor considering only the current instantaneous kinematic information, without the information on the history of the signal over time. Furthermore, data fitted from simulations show that a first degree polynomial model has a maximum error prediction on the order of less than 0.01 percent around Nadir pointing and can be appealing for fast computation of this correction with low-cost hardware based on Field Programmable Gate Array.

In the use of machine learning methods for error compensation in dead reckoning of an autonomous undersea vehicle(AUV), the neural network algorithm is commonly used. However, neural networks require a large number of training samples to achieve stable training results. To solve this problem, research was conducted on the application of support vector machine(SVM) for error compensation in dead reckoning. By utilizing SVM, an error compensation model was trained to correct the errors in dead reckoning, thereby improving navigational accuracy. The error compensation model takes seven parameters as input: pitch angle, roll angle, course angle, forward, right, and upward velocity of the Doppler velocity log(DVL) relative to the ground, and dead reckoning time of the AUV. The difference in latitude and longitude provided by the global positioning system(GPS) and inertial navigation system(INS) + DVL combination compared with latitude and longitude obtained from dead reckoning serves as the output of the model. The SVM trained model and the neural network trained model show a relative error of 0.28% and 0.93%, respectively, when the amount of data is limited. Through lake tests, it is concluded that the model trained by SVM can control the relative error of dead reckoning within 0.5%.

Aiming at the problem that machine tool thermal error (MTE) and probe pre-travel error (PPE) seriously weaken measuring accuracy, existing researches are conducted under certain conditions that MTE in a constant ambient temperature is considered or only PPE is compensated. A novel comprehensive error MTE-PPE model with consideration of different constant ambient temperatures is developed to enhance measuring accuracy. Independently developed new type of horizontal CNC gear grinder (L300G) regarded as the research object, MTE-PPE is equivalent to probe slip error (PSE) based on involute property and L300G involute gear measuring principle. Mini-batch gradient descent optimization algorithm (MGDOA) is employed to reckon more reasonable PSE compensation values, on the basis of which grinded topological modification tooth surface is fitted according to the least squares method (LSM) tooth surface fitting principle. The theoretical topological modification tooth surface is constructed founded on the L300G involute helical gear topological modification grinding principle, and morphology deviation is derived from matching the constructed and fitted tooth surfaces. Results show that, before and after PSE compensation, L300G tooth lead measuring accuracy is level 4; after PSE compensation, L300G tooth profile measuring accuracy is improved by 2 grades, reaching level 4, which is consistent with Gleason 350 GMS measuring report; fluctuation range of tooth surface morphology deviation is 0–5 μm, and the tooth profile error and tooth lead error extracted from morphology deviation are consistent with Gleason 350GMS measuring report. The model effectively enhances measuring accuracy, truly reflects the 3D morphology of grinded topological modification tooth surface, and fairly eliminates the limitation of incomplete evaluation of a single error measured item. It is expected to be popularized in high-precision measuring and grinding technology of horizontal or vertical gear grinders. [ABSTRACT FROM AUTHOR]

This paper presents a low power wideband dB-linear analog baseband (ABB) circuit for a millimeter-wave (mmW) wireless receiver in 40 nm CMOS technology. The proposed ABB system consists of a multi-stage variable gain amplifier (VGA) and a low-pass filter (LPF). The 5-stage VGA is composed of two variable gain units followed by three fixed gain units with DC offset cancellation (DCOC). The first variable gain unit with a self-compensated transistor pair and compact active inductor load is designed for dB-linear functionality and bandwidth extension, respectively. Moreover, a proposed error compensation method is applied to the second cascaded variable gain unit for further dB-linear gain error correction. A 4th-order Butterworth transconductance-capacitance (Gm-C) LPF with flipped source follower (FSF) as an input transconductance stage for linearity enhancement is designed after the VGA stage. The prototype chip is implemented, and measurement results show a dB-linear gain range from −18 to 26 dB with less than 0.5 dB-linear gain error with a bandwidth of 4 GHz. The VGA and LPF consume 8.3 mW and 3 mW, respectively, under a 1 V power supply, while the entire ABB occupies an area of 0.94 mm2 with an active core area of only 0.045 mm2. [ABSTRACT FROM AUTHOR]

The primary factors affecting the geometric accuracy of abrasive water jet (AWJ) machining of ultra‐thick carbon fiber‐reinforced polymer (CFRP) laminates are the cutting front drag and kerf width deviation caused by energy dissipation. Firstly, this study comprehensively analyzes the influence of the coupling relationship between cutting front drag and kerf width deviation on the geometric accuracy of hole machining. Secondly, a gradient distribution theory for jet traverse speed with cutting depth is proposed for taper hole machining characteristics. Finally, a general geometric error model applicable to both straight and taper hole machining of ultra‐thick CFRP laminates using AWJ is established, along with a geometric error compensation method. A series of experiments validate the accuracy of the proposed geometric error model and compensation method. The novelty of this research lies in unifying the geometric error models for straight and taper hole machining of ultra‐thick CFRP laminates by considering the gradient distribution of jet traverse speed. The proposed error compensation method reduces the high degree of freedom required for machine tools. This study provides a general geometric error model and compensation strategy for AWJ hole machining of ultra‐thick CFRP laminates, which has significant engineering value for achieving 3D controllable AWJ machining of complex ultra‐thick CFRP components. Highlights: The high geometric precision taper holes machining technique is a necessary prerequisite for achieving precise machining of complex trajectories in ultra‐thick CFRP with abrasive water jet.The influence of the coupling relationship between cutting front drag and kerf taper on the geometric accuracy of hole processing is comprehensively analyzed.A model considering the gradient distribution of traverse speed during taper holes machining was established, and a geometric error model considering this gradient distribution of traverse speed was developed.A taper holes machining geometric error compensation method with high engineering applicability was proposed.Geometric error prediction experiments and error compensation experiments were conducted, and the results proved the accuracy of the geometric error model and error compensation method. [ABSTRACT FROM AUTHOR]

Aiming at the widespread geometric and dimensional accuracy defects of additive manufacturing products, a modeling and compensation strategy for the position error of additive manufacturing machines was proposed based on the measured data of calibration sample augmentation point and the least square fitting method. Firstly, based on the widely used fuse forming equipment, an additive manufacturing test bench was built. Based on the test bench, a step pyramid calibration sample cluster was manufactured to establish the position error of the additive manufacturing equipment. The position of the center point (augmented point) and the distance to the vertical plane of each step were measured by a coordinate measuring machine. Secondly, based on the measured data and the kinematic chain-error transfer relationship, the equipment position error was characterized by low-order polynomial, and the parameters of the error model were estimated by least square fitting. At the same time, the initial error model was optimized according to the significance level of the estimated parameters. Finally, the position error compensation of the additive manufacturing equipment was carried out by using the optimized model combined with the modification of the stereo lithography file of the formed parts, and a brief size compensation method was proposed at the same time. The validity of the proposed error model and compensation method was verified by the forming processes of a new calibration sample cluster and real-world mechanical parts. The research results show that, comparing with the calibration sample cluster and the real-world mechanical parts manufactured without using error compensation, the maximum position error of the formed products after using the equipment position error compensation can be controlled below 300 μm, and the maximum relative dimensional error of the key features of the formed real-world mechanical parts is - 2. 4% after the comprehensive use of the equipment position and size error compensation. It meets the actual engineering needs, therefore the proposed error model and compensation method are proved to be effective. [ABSTRACT FROM AUTHOR]

With the continuous development of power system and the growth of load demand, accurate short-term load forecasting (SLTF) provides reliable guidance for power system operation and scheduling. Therefore, this paper proposes a two-stage short-term load forecasting method. In the first stage, the original load is processed by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The time series features of the load are extracted by temporal convolutional network (TCN), which is used as an input to realize the initial load prediction based on gated recurrent unit (GRU). At the same time, in order to overcome the problem that the prediction model established by the original subsequence has insufficient adaptability in the newly decomposed subsequence, the real-time decomposition strategy is adopted to improve the generalization ability of the model. To further improve the prediction accuracy, an error compensation strategy is constructed in the second stage. The strategy uses adaptive variational mode decomposition (AVMD) to reduce the unpredictability of the error sequence and corrects the initial prediction results based on the temporal convolutional network-gated recurrent unit (TCN-GRU) error compensator. The proposed two-stage forecasting method was evaluated using load data from Queensland, Australia. The analysis results show that the proposed method can better capture the nonlinearity and non-stationarity in the load data. The mean absolute percentage error of its prediction is 0.819%, which are lower than the other compared models, indicating its high applicability in SLTF. [ABSTRACT FROM AUTHOR]

Amidst the accelerating pace of automation in sheet metal bending, the need for small-batch, multi-varietal, efficient, and adaptable production modalities has become increasingly pronounced. To address this need and to enhance the efficacy of the bending process, this study presents the design and development of an embedded soft PLC (Programmable Logic Controller) rooted in the Codesys development platform and leveraging the ARM Cortex-A55 architecture. This controller employs the EtherCAT communication protocol to facilitate seamless and efficient interactions with fully electric servo-driven CNC (Computerized Numerical Control) bending machinery. To mitigate the challenge of bending springback errors, a finite element simulation model is constructed and refined through the application of ALE (Arbitrary Lagrangian-Eulerian) adaptive grid technology, thereby bolstering simulation precision. Subsequently, an enhanced WOA-BP (Whale Optimization Algorithm—Backpropagation) model, integrating Latin hypercube sampling and neural network techniques, is deployed to anticipate and counteract these springback errors. Experimental outcomes demonstrate that the proposed methodology effectively constrains the final forming angle deviation to within 0.3°, significantly enhancing the reliability and precision of the bending system. This achievement not only underscores the technical feasibility but also contributes to advancing the frontier of sheet metal bending automation. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the advantages of low cost, high flexibility, and large workspace, industrial robot has been considered to be the most promising plan to replace traditional CNC machine tool. However, the low absolute positioning accuracy of robot is a key factor that restricts further application in high-precision metal-cutting scenarios. In order to improve the absolute positioning accuracy of robot, a positioning error compensation method based on the stacked ensemble learning is proposed. Firstly, the sources of positioning errors and compensation strategies are clarified by analyzing the kinematic model and structural composition of industrial robot. Then, based on the stacked ensemble learning algorithm, robot positioning error prediction model containing multi-layer learners is constructed. And a discrete grid optimization method is presented for model hyper-parameters optimization calculation. Next, predicted positioning errors are adopted to realize the positioning compensation by offline compensation method. Finally, by setting up a robotic milling platform based on MOTOMAN ES165D robot, a series of error compensation experiments have been implemented to verify the proposed method. After compensation, the maximum absolute position error and average position error have decreased by 83% and 89%, respectively, in the compensation experiments of a single point. Moreover, the error compensation of the end milling experiments has also brought significant accuracy improvement, which proved the effectiveness of the proposed method in robotic machining. [ABSTRACT FROM AUTHOR]

A novel binocular vision error compensation method is proposed to solve the major error compensation problem caused by nonlinear distortion of camera and projector on underwater target detection. By optimizing the visual structure and integrating the projector with two cameras, the method enhances the adaptability and robustness of the system in the complex underwater environment. Through fine signal preprocessing such as multi-scale decomposition and feature extraction, the three-dimensional information of the target is captured more effectively and the data noise is effectively removed. The characteristics of underwater target detection error compensation data are extracted, and the characteristic parameters reflecting the significant difference between them are obtained. In order to ensure the accuracy of compensation results, a multi-scale attention mechanism is introduced into the improved binocular vision, and feature parameters are taken as the input of binocular vision model. After continuous learning and training, high-precision error compensation for underwater targets is realized. Experimental results show that different underwater targets can be detected when the average error value compensated by this method is less than 0.1 mm, and the maximum error value is always less than 1.5 mm. The structural similarity index measure of the proposed method is above 0.91, and the fluctuation is small. This method has significant advantages in reducing errors, adapting to different terrain targets, and providing stable and high-precision output, which shows its application potential and superiority in the field of underwater target detection, and provides a more effective and reliable technical solution for underwater target detection. [ABSTRACT FROM AUTHOR]

Metal mirrors will be widely used in the coming decades. Therefore, as one of the enabling technologies for metal optical freeform surface manufacturing, ultra-precision (UP) diamond turning error compensation has become a research hotspot. However, for the tool offset error and tool radius error, which are the main errors in UP diamond turning, no precise and efficient calculation method has been found in the literature. In this study, a more precise and efficient algorithm was developed and validated in three ways using on-machine measurement data and profilometer measurement data. After one compensation, the tool offset error can be reduced to below 0.1 μm, and the tool radius error can be reduced to below 1 micrometer, which will significantly improve the UP turning accuracy and efficiency of optical parts. [ABSTRACT FROM AUTHOR]

In order to solve the problem of nonlinear error for a dual rotary table five-axis CNC machine tool due to the linkage of rotary and translational axes, the simulation of motion nonlinear error compensation for a multi-axis linkage CNC machine tool is proposed. The adjacent points in the tool position file are selected as the tool position points for building the model, and then the nonlinear error model resolved by the harmonic function is established according to the error distribution in the classical post-processing. The nonlinear error between the two tool position points is quickly predicted by the analytical expression of this model, and the real-time error compensation of the intermediate interpolation points is realized. Finally, MALTLAB simulation analysis is performed on the tool position file of an impeller part machining to verify the effectiveness of the proposed algorithm. The experimental results show that it can be seen from the distribution curve of the nonlinear error that it is about 10% after compensation as before compensation, thus verifying the effectiveness of the nonlinear error compensation mechanism. The correctness of the nonlinear error analysis and compensation method and the effectiveness of post-processing are verified. [ABSTRACT FROM AUTHOR]

This paper is aimed to address the issue of decreased accuracy in the ship block docking caused by the structural errors of posture adjustment mechanism. First, inverse kinematic analysis is performed to investigate the sources of static errors in the mechanism. Subsequently, based on the closed-loop vector method, a pose error model for the moving platform is established, which includes eight categories of error terms. The impact of various structural errors on the pose accuracy of the moving platform is then compared and analyzed under both single-limb and multi-limb configurations. Therefore, a compensation method based on the whale optimization algorithm optimized radial basis function neural network is proposed. By transforming pose errors into actuator length errors, it establishes a predictive model between the theoretical pose of the dynamic platform and actuator length errors. After optimizing the network parameters, it yields the actuator length compensation to correct the actual pose of the dynamic platform. Simulation and experimental results validate the effectiveness of this method in enhancing the motion accuracy of the parallel mechanism. The mean pose accuracy of the moving platform is improved by 85.07%, demonstrating a significant compensation effect. [ABSTRACT FROM AUTHOR]

Featured Application: Using a calibration sphere with a known diameter allows for calibration of a robot that includes both geometric errors and end-effector deformation errors. A self-calibration method utilizing spherical constraints is proposed for calibration of robot end-effectors. The method establishes a mathematical model to account for both the geometric errors of the robot and the deformation errors of the end-effector. A nonlinear least-squares parameter identification technique based on spherical constraints is employed to achieve autonomous calibration of the end-effector. Contrasted with methodologies relying on point plane or distance constraints, this novel technique delivers superior positioning accuracy, streamlined operational procedures and enhanced efficiency. Both simulation and experimental validation confirm that the self-calibration method using spherical constraints improves the positioning accuracy of the robot end-effector from 3 mm to 0.3 mm, showing the effectiveness of the method. [ABSTRACT FROM AUTHOR]

In vector-controlled Permanent Magnet Synchronous Motors (PMSMs), measuring the motor flux angle, particularly the rotor position, is essential. If a pulse error occurs during motor control using an encoder, it becomes impossible to accurately estimate the rotor position, leading to incorrect position angle information being used in the coordinate transformation. This mismatch causes discrepancies between the commanded three-phase stator current and the actual current applied to the motor. As a result, the motor and inverter output and efficiency decrease, and the control performance deteriorates. Therefore, research is necessary to compensate for such errors. This paper proposes an algorithm that detects pulse errors occurring in incremental encoders and automatically switches to the Hall sensor control mode to compensate for the encoder pulse errors. The proposed algorithm, based on Hall sensors, has the advantage of not significantly affecting control delays, which could be problematic in high-speed operating ranges; thus, effective control is maintained even in such situations. [ABSTRACT FROM AUTHOR]

Aspherical surfaces, with their varying curvature, minimize aberrations and enhance clarity, making them essential in optics, aerospace, medical devices, and telecommunications. However, manufacturing these surfaces is challenging because of systematic errors in CNC equipment, tool wear, measurement inaccuracies, and environmental disturbances. These issues necessitate precise error compensation to achieve the desired surface shape. Traditional methods for spherical optics are inadequate for aspherical components, making accurate surface shape error detection and compensation crucial. This study integrates advanced metrology with optimized material removal functions in the grinding and polishing processes. By combining numerical control technology, computer technology, and data analysis, we developed CAM software (version 1) tailored for aspherical surfaces. This software uses a compensation correction algorithm to process error data and generate NC programs for machining. Our approach automates and digitizes the grinding and polishing process, improving efficiency and surface accuracy. This advancement enables high-precision mass production of rotationally symmetrical aspherical optical components, addressing existing manufacturing challenges and enhancing optical system performance. [ABSTRACT FROM AUTHOR]

Several academic and budgetary applications in robotics use low-cost encoders which usually present errors inherent to the fabrication of their components or the surrounding environment. However, the data gathered from these sensors could be used successfully if the estimations based on the data are be compensated. This note presents an efficient method to compensate the error of estimation for the distance traveled by robotic four-wheeled vehicles with a speed control based on pulse width modulation. The current approach uses a methodology based on artificial neural networks which compensate the estimation error and enables a way to model the error for a further method of position estimation. Precisely, the model proposed in this work is based on a neural network with one hidden layer and five nodes. Our approach uses the information from the pulse width modulation value to control the speed of the vehicle as well as the values for the tick counting of the encoders in the four wheels. The back-propagation algorithm was used to calculate the weights of the nodes in the neural network. This method showed an improvement in the results with an error comparable to the intrinsic error due to the precision of the sensor, and gives an error model based on a zero-mean normal distribution. [ABSTRACT FROM AUTHOR]

To improve the machining accuracy of free-form surface parts, a coarse-to-fine free-form surface machining error decomposition and compensation method is proposed in this paper. First, the machining error was coarsely decomposed using variational mode decomposition (VMD), and the correlation coefficients between the intrinsic mode function (IMF) and the machining error were obtained to filter out the IMF components that were larger than the thresholding value of the correlation coefficients, which was the coarse systematic error. Second, the coarse systematic errors were finely decomposed using empirical mode decomposition (EMD), which still filters out the IMF components that are larger than the thresholding value of the set correlation coefficient based on the correlation coefficient. Then, the wavelet thresholding method was utilized to finely decompose all the IMF components whose correlation coefficients in the first two decomposition processes were smaller than the threshold value of the correlation coefficient set. The decomposed residual systematic errors were reconstructed with the IMF components screened in the EMD fine decomposition, which gave the fine systematic error. Finally, the machining surface was reconstructed according to the fine systematic error, and its corresponding toolpath was generated to compensate for the machining error without moving the part. The simulation and analysis results of the design show that the method has a more ideal processing error decomposition ability and can decompose the systematic error contained in the processing error in a more complete way. The results of actual machining experiments show that, after using the method proposed in this paper to compensate for the machining error, the maximum absolute machining error decreased from 0.0580 mm to 0.0159 mm, which was a 72.5% reduction, and the average absolute machining error decreased from 0.0472 mm to 0.0059 mm, which was an 87.5% reduction. It was shown that the method was effective and feasible for free-form surface part machining error compensation. [ABSTRACT FROM AUTHOR]

In response to the problems of cumulative errors and inaccurate correction results in the attitude calculation method of hydraulic supports based on inertial measurement units, a fully mechanized working face hydraulic support attitude monitoring method based on particle swarm optimization (PSO) - extreme learning machine (ELM) is proposed. Using the pitch angle of the hydraulic support top beam as the monitoring object, a tilt sensor and gyroscope are used to collect real-time information on the support attitude of the hydraulic support top beam. The collected data is preprocessed and input into the PSO-ELM error compensation model to obtain the predicted solution error. At the same time, the hydraulic support attitude is calculated through Kalman filtering fusion to obtain the calculated value. Then the method uses the error prediction value to compensate for the error in the calculated value, in order to obtain more accurate data on the top beam support attitude. This method only considers the relationship between acceleration and angular velocity data and solution errors, without relying on specific physical models. It can effectively reduce the cumulative error of attitude solution. The experimental results show that the average absolute error of the pitch angle of the top beam of the hydraulic support has been reduced from 1.420 8° before compensation to 0.058 0°. The error curve has good convergence, verifying that the proposed method can sustainably and stably monitor the support attitude of the hydraulic support.

Aiming at the issue of low accuracy in crop mapping and growth monitoring caused by imprecise calibration of radar time-series data, this paper proposed a Synthetic Aperture Radar (SAR) spatio-temporal error compensation method. By constructing a compensation reference image using the buildings with stable backscattering coefficients in the study area, the error correction of time-series SAR data was realized based on the compensation model. The compensated SAR data were used to establish the Sugarcane Growth Index (SGI) according to the growth curve of sugarcane. The sugarcane area was extracted after combining the compensated SGI and optical image, and the accuracy of F1 on sugarcane distribution extraction was 92.12%. There was an improvement of 4.11% compared with no compensation method. On this basis, the newly planted and ratoon sugarcane were further classified by the expectation maximization algorithm based on the compensated SAR image at the seedling stage. The newly planted sugarcane and the ratoon sugarcane could be accurately distinguished with area extraction accuracy of 82.06% and 80.59%, respectively. Moreover, the compensated SAR data of time series during the tillering to maturity stages were used to estimate the sugarcane height, yielding an R2 value of 0.717 using a quadratic polynomial model. The Spatio-temporal error compensation method for distributed targets proposed in this study can reduce the loss of SAR data across various times and regions, and achieve accurate results of sugarcane distribution extraction and plant height estimation.

The traditional filter weighing method has a cumbersome operation process, a long detection cycle, and a low degree of automation. Although it has high accuracy, it cannot meet the needs of real-time detection of dust concentration. An automatic dust mass concentration detection device was designed, and the temperature and humidity compensation model was established to replace the steps of filter membrane drying after sampling in the manual weighing method, so as to further reduce the dust concentration detection time and the volume of the dust concentration detection device. The prototype device was built and the experiment was conducted. The test results showed that the standard deviation of the CCZ-20A dust sampler commonly used in coal mines and the converted concentration of dust detected by this device was within 5%, and the fitting correlation of the experimental data under the univariate linear regression fitting analysis was good. In order to further improve the detection accuracy of the device, an error compensation method based on the combination of Fourier series, linear fitting and periodic fitting was studied, and the concentration calculation process of the device was established and brought into the original data, and the detection concentration error of the device was concentrated from the original (−7.20%, −1.26%) to (−3.64%, 3.65%). After the introduction of the device concentration calculation process, several comparative experiments were carried out, and the experimental results showed that the detection errors of the device concentration were within this range, which verified the reliability of the device concentration calculation process. The device shortens the time required for dust concentration detection, and at the same time controls the detection error within a reasonable range, which provides a reference for the research of filter membrane weighing method in the online monitoring of dust concentration.

In order to solve the problem of the low end positioning accuracy of large hydraulic rock drilling robotic arms due to machining error and the working environment, this paper proposes an end positioning error compensation method based on an Improved Secretary Bird Optimization Algorithm (ISBOA) optimized Back Propagation (BP) neural network. Firstly, the good point set strategy is used to initialize the secretary bird population position to make the initial population distribution more uniform and accelerate the convergence speed of the algorithm. Then, the ISBOA is used to optimize the initial weights and biases of the BP neural network, which effectively overcomes the defect of the BP neural network falling into a local optimum. Finally, by establishing the mapping relationship between the joint value of the robot arm and the end positioning error, the error compensation is realized to improve the positioning accuracy of the rock drilling robot arm. The experimental results show that the average positioning error of the rock drilling robotic arm is reduced from 187.972 mm to 28.317 mm, and the positioning accuracy is improved by 84.94%, which meets the engineering requirements. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Considering the detrimental impact of thermal phenomena on the geometric precision of machine tools, a machine tool ball screw's omni-directional error model is created using the LSTM neural network algorithm. Subsequently, the machine tool ball screw's omni-directional error compensation module is devised by combining the core functions of the Huazhong numerical control system with the visual programming environment of QT and the numerical computation capability of Matlab. To enhance the practicality and accuracy of the compensation model, this study has employed the Whale Optimization Algorithm (WOA) to optimize the parameters of the LSTM model. This has resulted in an improvement in the model's generalization ability and prediction performance, making it more effective. During the experimental validation phase, the Z-axis error of the machine tool was practically operated and analyzed using the compensation method. Results manifestly show that, after employing the compensation method, the peak amplitude of the Z-axis error fluctuations have been notably curtailed to ±0.006 mm -- a considerable reduction compared to the initial error bandwidth of ±0.0145 mm. These empirical findings substantiate the efficacy of the proposed compensation strategy in substantially boosting the machining precision of products, thus furnishing a substantial and instructive benchmark for future inquiries into CNC machine tool error compensation technologies. [ABSTRACT FROM AUTHOR]

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