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Due to low stiffness of compliant series robot body, the influence of joint flexible deformation caused by the gravity load of the end-effector on the absolute positioning accuracy cannot be ignored. In order to improve the flexible positioning accuracy of the robot end-effector, a calibration method for predicting and compensating the flexible positioning error of the 7-DOF series compliant robot due to the gravity load of the end-effector is proposed. The method identifies and predicts the pose configuration with similar flexible positioning error through the semi-parametric error model based on joint stiffness identification and of BP neural network. The feedforward method is used to compensate the positioning error. In this study, a 7-DOF series compliant robot is selected as the research object. Based on the principle of error similarity, the measurement points and the corresponding pose configurations are selected as the training data for the parameter identification of the semi-parametric error model, and the flexible positioning errors on the continuous trajectory of the end-effector are compensated according to the predicted values of the model. When the mass of the end-effector is 5 kg, by this method, the maximum value, mean value and mean square error of flexible positioning error on multiple straight-line trajectories on the plane are increased from 8.4779, 5.6039, and 1.8496 mm to 0.9720, 0.3079, and 0.2303 mm, respectively. It means the positioning accuracy are increased by 88.53%, 94.50%, and 87.55%, respectively, by which could observe that the precision of this robot's end-effector was significantly improved. [ABSTRACT FROM AUTHOR]

Industrial robots are extensively utilized in handling, assembly, and welding tasks owing to their expansive workspace, scalability, flexibility, and cost-effectiveness. However, their inadequate absolute positioning accuracy significantly impedes their application in precise operational scenarios. To enhance robot positioning accuracy, the hysteresis error induced by gear meshing backlash is considered. Firstly, the impact of joint hysteresis on robot positioning errors is analyzed, the notion of modified joint space is introduced, and the similarity theory of error in modified joint space is analyzed. Secondly, for the problem of parameter overfitting of the universal Kriging model, a method of dynamically determining the basis function set by using the genetic algorithm is proposed. Finally, the target trajectory is corrected by a feed-forward iterative compensation algorithm. An experiment on a tandem industrial robot SMART5 NJ 220-2.7 is conducted to demonstrate the effectiveness of the compensation. The experimental results show that the error caused by joint hysteresis is significant, with joint 1 notably affecting y axis positioning accuracy, while joints 2 and 3 predominantly influence x axis positioning accuracy. Furthermore, cross-validation tests verified the good anti-overfitting effect of optimized Kriging for models with multiple input parameters and the good fitting accuracy of the modified space model for hysteresis errors. Moreover, after employing MJS&GPS+GA error modeling and feed-forward iteration compensation, the average absolute positioning error of the trajectory decreased by 81% to 0.09252 mm, and the maximum absolute positioning error decreased by 59% to 0.27713 mm.

During the manufacturing process of a piece, different factors influence the process so that errors of a systematic and random nature are added, affecting both the dimensions and the final geometry. The strategy presented in this research seeks to identify, monitor, and computationally control the errors arising from the arrangement of the actuators that can be derived from errors in assembly, wear of the components, and thermal deformations. This article proposes an algorithm to prevent and correct geometric and dimensional errors in manufacturing parts as a compensation strategy. The algorithm obtains the references of the manufacturing process and estimates the process’s deviation, calculating the manufactured part’s error concerning the projected piece. Error compensation is performed through reference points that alter the location of the target points in the opposite direction to the resulting error to nullify it kinematically. The validation of the strategy is carried out through the computational implementation of kinematic algorithms of a machine tool with a Cartesian structure of two degrees of freedom on which position and orientation errors are induced. The presented results allow for verifying the proposed algorithm’s effectiveness against tests with significant errors. The compensation strategy presented allows projecting this algorithm as an online software calibration method, reducing the number of stops due to mechanical maintenance of the CNC machine and making closed-loop manufacturing possible with real-time compensation.

The spraying construction robot is unable to include information about ground leveling in the map when it is created, and when the robot operates according to the built map, the spraying clamping fixture at the working end of the spraying construction robot can not be parallel to the wall due to the lack of ground information. In order to compensate the posture error of the spraying fixture relative to the wall, a multi-sensor fusion method is proposed based on the unscented Kalman filter to compensate the posture of the spraying fixture.The state equation of the fixture posture is constructed from the data measured by the displacement measurement sensor， the equation of fixture posture measurement is constructed from the data measured by the gyroscope, and the optimal estimation of the fixture posture is obtained by using the unscented Kalman filter algorithm and transferring them to the robot, so as to achieve the purpose of compensating the posture error of the spraying fixture. Finally, the experimental platform is built to verify the feasibility of the error compensation system. The experimental results show that the positional error between the spraying fixture and the wall after error compensation is reduced to 0.005°.

Laser emitters are used to generate vertically intersecting structured light, which can provide a reference for pose measurement. Therefore, the accuracy of the pose measurement system is determined by the accuracy of laser emitter instruments. The motion joint errors of the mechanical structure of the laser emitter instrument are analyzed and the transverse and longitudinal error transmission models are established in this paper. On this basis, the dual-axis coupling model is established and the error simulation analysis is performed. A method of using spring traction for error compensation is proposed and compared with the error before compensation. The results show that the error is significantly reduced, and the average laser deflection angle is about 1/14 of the pre compensation value.

Neutral point clamped inverters, such as NPC, ANPC, or T-Type inverters, have emerged as competitive solutions for specific automotive traction applications due to the increase in DC-link voltage levels up to $800 \,\mathrm{V}$. Besides improved harmonic performance, certain 3-level inverter structures can provide a reconfigured failure mode operation in case of a semiconductor failure by applying a permanent neutral point connection in the faulty phase leg. This feature is a vital factor in the design of traction inverters, particularly in the context of fault-tolerant autonomous vehicles. During failure mode operation, none of the neutral point balancing techniques found in current literature are practical. Addressing this issue is crucial since the neutral point stress is high in this mode of operation. Therefore, this paper investigates the impact of stationary and dynamic neutral point voltage deviations on the motor flux in electric machines and offers a compensatory strategy for these errors. Furthermore, a new neutral point control method is presented that balances stationary neutral point voltage deviations by using a phase angle dependent voltage shift in the $\alpha / \beta$-plane. All proposed strategies are first discussed theoretically and then verified by simulation and measurements on an $800 \,\mathrm{V}$ IPMSM machine test bench.

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.

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.

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.

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.

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.

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.

For the problem of geometric error prediction of CNC machine tools, an improved hybrid grey wolf optimization (IHGWO) algorithm is proposed to optimize the geometric error modeling scheme of the support vector regression machine (SVR). The predicted and measured values of the geometric error are combined to construct the fitness function. In IHGWO, principles of particle swarm optimization (PSO) algorithm and dimension learning-based hunting (DLH) search strategies are introduced while retaining the excellent grey wolf position of the basic grey wolf optimization (GWO) algorithm. IHGWO algorithm uses Euclidean distance to construct the neighborhood of individual grey wolves, which enhances the ability to communicate between individual grey wolves and improves the convergence speed and accuracy of the algorithm. Predictive performance of SVR models using sum squared residual to quantify geometric error. Based on the screw theory, space models of geometric errors of CNC machine tools are established and combined with SVR models of geometric errors for compensation. Empirical evidence proves that the proposed method surpasses current error modeling methods in terms of precision and efficiency, as evidenced by a minimum reduction of 9% in circular trajectory error and a reduction to two overruns in S-shaped test pieces after error compensation. This research contributes to the field of CNC machine tool error modeling and has practical implications for manufacturing industries. [ABSTRACT FROM AUTHOR]

The error compensation method is an economical and effective method to improve the machining accuracy of machine tools. Conventional volumetric error modeling of machine tools is based on the rigid body kinematics model using 21 terms of geometric errors and assumes the geometric error of one axis is independent of other axes. However, if the error motion does not satisfy this assumption in serial machine tools, it may significantly influence the overall machine tool accuracy. This paper reveals the existence of mutual crosstalk of angular errors in coupling axes by experiments with equipping the five-degree-of-freedom (5-DOF) embedded sensor module in each axis. A new on-machine measurement method is proposed for detecting crosstalk errors of the coupling axes covering the whole coupling area. It is confirmed that the position-dependent geometric errors (PDGEs) of each axis should be modified by two-dimensional dependence. Therefore, extended from the normal volumetric error model (NVEM) that is commonly used nowadays, a novel enhanced volumetric error model (EVEM) that contains crosstalk's PDGEs is proposed. The real-time error compensation controller (RECC) embedded in CNC system kernel has been developed in collaboration with the vender by writing in the application function. The effectiveness and feasibility of the EVEM and RECC are verified by non-cutting and cutting experiments. Through comparison tests with laser interferometer on body diagonals, the accuracy improving rate of EVEM is about 10 % higher than NVEM. The real-cutting tests on the ISO10791 sample are verified by a CMM and the results show the accuracy improvement of EVEM is better than NVEM by about 20 %. • A new entire coupling plane measurement method is proposed to reveal the crosstalk error phenomenon in any stacked-up XY stages. These PDGEs are not only in relation to their own axis but also to the coupled axes. • Based on the normal volumetric error model (NVEM), the new enhanced volumetric error model (EVEM) that considers crosstalk errors is proposed. • The real-time error compensation controller (RECC) embedded in CNC system kernel for volumetric error compensation has been developed. It is a direct multi-sensor feedback compensation control system without the use of an external computer as other researches. • The effectiveness of EVEM and RECC are verified through a series of experiments. The advantage of EVEM over NVEM is also proven by comparison tests. [ABSTRACT FROM AUTHOR]

To improve stability and accuracy during machining, the structural performance of machine tools must be predicted in advance. This study aims to improve the performance of a large-scale turning-milling complex machine tool by analyzing its structural characteristics and health status. Modal and spatial accuracies of an actual machine tool were analyzed using the finite element method based on the working environment and material properties. Static analysis was used to determine spatial processing position deformation errors and an appropriate compensation value to improve accuracy. The modal analysis determined the characteristic parameters of the main machine structure in a fair frequency range, and the modal analysis software verified the machine frequency using modal shape curve fitting. Modal error percentages between the virtual and machine models determined the validation of the model. The prediction diagnosis performance system monitored the spindle vibration signal to evaluate its health status. However, the use and maintenance of each piece of equipment differed. Abnormal symptoms of the spindle are observed in specific frequency bands or vibration characteristics. The feature modeling can establish a health diagnosis model and use the principal component analysis method to observe vibration characteristics and identify machine health. [ABSTRACT FROM AUTHOR]

In alumina production, the evaporation as the key process uses recyclable resources and reduces environmental pollution. In fact, the quality of export product with offline and delayed, results in low precision of process control and high energy consumption. To ensure green and efficient production, in this paper, a new prediction method integrating process knowledge and data-driven spatial-temporal adaptive model is put forward. First, to preprocessed production data for ensuring modeling accuracy, data reconciliation technology is adopted. Then, based on material and heat transfer mechanism, for equipment and industrial process, the mechanism models are established. Furthermore, with time difference and moving window model, an error compensation method is utilized in terms of double locally weighted kernel PLS for estimation error in hypothesis-based mechanism modeling. Finally, the data-driven spatial-temporal adaptive model and the process knowledge-based mechanism model are integrated. To illustrate the model feasibility, an industrial sodium aluminate solution evaporation is used. It demonstrates that, for the developed model, the prediction accuracy can reach more than 90% within the ± 2% error range, and effectively estimate the actual product quality and ensure the prediction effect. • A new concentration prediction method integrating knowledge and data-driven spatial-temporal adaptive model is proposed. • Mechanism modelling based on data reconciliation and process analysis is achieved to estimate concentration. • A TD-MW-DLWKPLS model considering both sample similarity and variable correlation is adopted for error compensation. • The performances of the developed method are validated on evaporation industrial application. [ABSTRACT FROM AUTHOR]

The virtual factory model of manufacturing industry can reduce cost and increase efficiency, optimize service quality, so it is good choice to cope with the fierce global competition. In order to continuously improve product quality in the mode, the accuracy enhancement of CNC equipment related to the production process was mainly focused on, which were the key techniques of error compensation, including error source analysis, error modeling, error measurement and error data processing. Therefore, the five-axis machine tool was taken as the representative of CNC equipment, and 45 geometric errors which affected its accuracy were found out; through error modeling, the relationship between these 45 errors and the total system error of the five-axis machine tool was established; then, according to the error characteristics of the five-axis machine tool, the measuring system was constructed and its effectiveness was verified; finally, data handling was carried out for the measurement data, which provided non-measurement node data and greatly reduced the amount of data information, so it laid a data foundation for rapid and effective error compensation. [ABSTRACT FROM AUTHOR]

In response to the issues of a wide variety of equipment, large batches, heavy payloads, limited space, complex assembly paths, and high assembly risks inside the cabin, a heavy-duty positioning and assembly robot was designed. Based on the study of the robot kinematics, an error model was established. With the radius of minimum bounding sphere as the constraint condition, the identification results of error parameters by genetic algorithm were compensated into the robot control system. Taking cabinet assembly as an example, a working path was planned based on spatial constraint conditions. By a dynamic constraint energy consumption function model, multi-objective optimal trajectories were obtained with time, impact, and energy consumption as optimization objectives. Prototype testing verified the effectiveness of the error parameter identification, which reduces the absolute positioning errors of the robot. Moreover, the multi-objective optimal trajectory has a small total joint impact and smooth motion, achieving efficient, smooth, and reliable installation of cabinet-type equipment. [ABSTRACT FROM AUTHOR]

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Based on the need to improve the detection accuracy, efficiency and traceability of low temperature co-fired ceramics (LTCC), a set of LTCC laser thickness measuring system was designed. For the problem of measuring accuracy was difficult to meet, the existing error was analyzed, the use of adjusting fixture for thickness measurement eliminated coaxial error and linearity error, optimized the design structure to reduce inclination error, eliminated repeatability errors through cyclic sensor calibration, reduced mechanical vibration error through data optimization and filtering processing, improved the system measurement accuracy. Compared with the actual product thickness data, the final accuracy error ≤5 μm, which meets the application requirements of the product and has a broad application market. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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 article presents two multipliers equipped with error compensation techniques, utilizing approximate 4:2 compressors to minimize energy consumption. Compared to previous approximate multipliers with error compensation circuit, the proposed designs named, "M00" and "M01", strike a good balance between accuracy and energy consumption. Although the reduction in the number of transistors has significantly decreased the energy consumption of M00, the accuracy of this design is still more than adequate for real-world applications such as image processing and neural networks. On the other hand, M01 attains high precision while consuming less energy than other multipliers. This article compares the proposed designs with existing multipliers using HSPICE tool in 7 nm FinFET technology. Moreover, the accuracy and quality of the proposed multipliers are evaluated using MATLAB. The results reveal that both proposed designs are very efficient in image processing. According to the results, M00 has the lowest energy consumption among the considered designs. M00 is about 7% better than its counterpart in terms of the power-delay-product parameter (5% Pdynamic and 48% Pstatic). M01 presents favorable results in the sharpening, smoothing, and Discrete Cosine Transform processes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To address the strong nonlinearity, uncertainty, and mutual coupling in the cobalt removal process of zinc hydrometallurgy, an algorithm based on an improved genetic algorithm (GA) backpropagation (BP) neural network combined with distributed nonlinear model predictive control (NMPC) is proposed. This method was applied to improve the quality of the purification solution and reduce the consumption of zinc powder, overcoming the challenges faced by the current cobalt removal process. First, a synergistic continuously stirred tank reactor (SCSTR) model was constructed for the dynamic cobalt removal process. Second, aiming at the problem that a single SCSTR model has difficulty describing the process accurately, based on the highly nonlinear mapping ability of data‐driven models, a method that organically integrates the SCSTR model and an error compensation model based on the GA‐BP neural network was proposed (GA‐BP‐SCSTR) to provide a more accurate online prediction of the process indicators. Then, a distributed NMPC architecture was developed using the GA‐BP‐SCSTR model, control vector parameterization (CVP) technique, and sequential quadratic programming (SQP) algorithm to achieve the coordinated control of the cobalt removal process. Finally, simulation results of an actual site showed that the prediction accuracy of the GA‐BP‐SCSTR model was higher than those of other models. The proposed predictive control method can maintain the outlet cobalt ion concentrations at the set values while achieving accurate control of the zinc powder addition. This approach can provide guidance for on‐site production and eliminate the blindness of manual experience control. [ABSTRACT FROM AUTHOR]

Aiming at the problems of error compensation after robot error modeling, an improved algorithm was proposed based on pseudo target iteration. This algorithm generated a new pseudo target in each iteration to correct the joint angles, thereby continuously reducing the actual errors of the robot. The pseudo target iterative algorithmic process was improved to mature the algorithm flow, 5 new and different false target generation methods were proposed, and the characteristics and applicable scenarios of each method were analyzed. An ensemble algorithm was proposed combining multiple false target generation methods to further improve the accuracy of error compensation. Using the HSR-JR612 robot to carry out simulation experiments, the simulation results show that the algorithm takes milliseconds and the compensation effectiveness is good, and the integrated algorithm may further improve the pose compensation effect. Finally, the UR10 robot and the laser tracker were used for experiments. The experimental results show that after compensation, the position errors of the or bot end may be reduced to less than 0.06 mm, and the attitude errors may be reduced to less than 0.025°. [ABSTRACT FROM AUTHOR]

A torsional stiffness test bench for vertical cylindrical couplings is developed and a compensation model for the test bench is investigated. The study illustrates the superiority of the vertical structure by analyzing the causes of errors between horizontal and vertical test benches. In this experiment, the steel bar is mounted on a torsional stiffness test bench for error analysis and compensation. The vertical torsional stiffness test bench is found to have the characteristics of high stiffness and easy compensation through experiment, and a time-domain compensation model is established based on these characteristics. The torque and angle are linearized at the same moment using the misalignment of the time-domain signal, and the torque and angular displacement data are fitted by the least squares method. It is experimentally verified that by compensating the test data through the time-domain compensation model, the test bench can achieve high testing accuracy and meet the requirements of coupling torsional stiffness testing.

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.

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.

Dead-beat predictive current control (DPCC) has great potential in the field of motor control due to its fast response. However, it suffers from low robustness due to its sensitivity to parameter variations. In this paper, a DPCC of permanent magnet synchronous motors (PMSM) combined with parameter adaptation is proposed to enhance the robustness of the dead-beat control under parameter misalignment. Firstly, the basic principles of dead-beat current control are introduced. Then, the influence of parameter errors on the control loop of dead-beat current control under imprecise motor parameters is analyzed. To address the issue of imprecise motor parameters, a predictive current control method combined with a parameter-adaptive algorithm is proposed. The motor parameters are identified online, and the parameters of the predictive current controller are modified in real-time to achieve parameter correction. Finally, comparative simulations and performance evaluations are conducted to validate the effectiveness of the proposed method. The results show that the robustness of the current control system is significantly improved, resulting in better control performance when applying the method proposed in this paper.

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Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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.)

In the field of artificial intelligence, pattern recognition is widely used to extract the abstract information in those high dimensional inputs of image, voice, or video. However, the interpretability of pattern recognition still remains understudied. The incomplete features extracted from system input still limit the recognition performance. To reject the disturbance of feature incompleteness, an error compensation is realized into the pattern recognition model under a quantum computation framework. The quantum-based recognition system fulfills the information transmission from input to output with the transformation of quantum states. Then, a compensation for the quantum state is used to reject those intermediate errors in the pattern recognition task. The experiment results in this paper indicate an effectiveness of the proposed method, with which the compensated Quantum Neural Network obtains a better performance. The proposed method brings a more robust recognition system under unknown disturbances. [ABSTRACT FROM AUTHOR]

Deadbeat predictive current control (DPCC) has excellent dynamics and can achieve current control with less computational effort. However, its control performance relies on the precision of the parameters of the motor. Current static error will be generated and control performance will be decreased when the predictive model parameters do not correspond to the practical parameters of the motor. In this article, a weakened integral sliding mode compensation method is proposed which converts the current error into a voltage compensation term and adds it to the prediction control output to effectively compensate for the steady-state error. In addition, a boundary layer is introduced to weaken the integral so as to solve the problems of integral saturation and overshoot caused by the introduction of the integral term when the error is large. Moreover, weight factors are introduced to optimize the feedback current, which enhances the robustness of the system. In the end, the effectiveness of this method is verified via simulation and experimental results. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute 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.)

Single-point diamond turning technology has been widely used in processing microstructures. The accuracy of ultraprecision cutting machine tools affects the performance of the microstructure. By improving the structure of the machine tool itself, the machining accuracy of ultraprecision diamond lathes has almost become optimized. Therefore, this research identifies and analyzes the linear axis tracking error through the dynamic modeling of a macro/micro cutting system. Furthermore, the impact of the tracking error on machining accuracy is obtained. Based on the macro/micro cutting system, a servo tracking error compensation method is proposed, and the effectiveness of this error compensation strategy is verified by simulation. The proposed experimental approach includes cutting experiments of tracking error compensation for a hyperbolic sine wave surface structure, verifying the surface profile accuracy of the workpiece with and without tracking error compensation. Additionally, this study proposes a profile evaluation method for microstructure. Experimental results show that the proposed tracking error compensation strategy effectively reduces the tracking error of ultraprecision cutting machine tools. Additionally, the proposed approach significantly improves the microstructure machining profile accuracy and can be used for ultraprecision lathes with high precision. [ABSTRACT FROM AUTHOR]

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This paper proposes a parasitic time-grating angular displacement sensor for detecting the position of the turntable bearing. The sensor is mainly composed of probe, rotor, and supporting signal circuit. The probe consists of two separated measuring units on the left and right, and each measuring unit is composed of four sensing units with iron cores, excitation windings, and induction windings arranged according to specific rules. The rotor is made of sinusoidal isometric grooves based on the bearing end cover of the original turntable bearing. The signal processing circuit is used to filter, amplify, and compare the induction signal; meantime, it can generate the excitation signal. The high-frequency clock pulse signal is used as the measurement benchmark to realize the analysis of the position information of the induction signal. The structure and working principles of the sensor are presented in detail. Through the finite element method simulation, the feasibility of sensor scheme is verified, and the theoretical resolution of the prototype sensor is estimated to be about 1.33e−5. A sensor prototype is designed for testing, and the experimental results show that its accuracy can reach −0.003611° to 0.002583° in the range of [0°, 360°] after compensating the error point-by-point through the look-up table method. [ABSTRACT FROM AUTHOR]

With the continuous improvement of train speed, the automatic driving of trains instead of driver driving has become the development direction of rail transit in order to realize traffic automation. The application of single modeling methods for speed control in the automatic operation of high-speed trains lacks exploration of the com-bination of train operation data information and physical model, resulting in low system modeling accuracy, which impacts the effectiveness of speed control and the operation of high-speed trains. To further increase the dynamic modeling accuracy of high-speed train operation and the high-speed train's speed control effect, a high-speed train speed control method based on hybrid modeling of mechanism and data drive is put forward. Firstly, a model of the high-speed train's mechanism was created by analyzing the train's dynamics. Secondly, the improved kernel-principal component regression algorithm was used to create a data-driven model using the actual opera-tion data of the CRH3 (China Railway High-speed 3) high-speed train from Huashan North Railway Station to Xi'an North Railway Station of "Zhengxi High-speed Railway," completing the mechanism model compensation and the error correction of the speed of the actual operation process of the high-speed train, and realizing the hybrid modeling of mechanism and data-driven. Finally, the prediction Fuzzy PID control algorithm was devel-oped based on the natural line and train characteristics to complete the train speed control simulation under the hybrid model and the mechanism model, respectively. In addition, analysis and comparison analysis were conduct-ed. The results indicate that, compared to the high-speed train speed control based on the mechanism model, the high-speed train speed control based on hybrid modeling is more accurate, with an average speed control error reduced by 69.42%. This can effectively reduce the speed control error, improve the speed control effect and oper-ation efficiency, and demonstrate the efficacy of the hybrid modeling and algorithm. The research results can provide a new ideal of multi-model fusion modeling for the dynamic modeling of high-speed train operation, further improve control objectives such as safety, comfort, and efficiency of high-speed train operation, and pro-vide a reference for automatic driving and intelligent driving of high-speed trains.