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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.

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.

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.

With the construction of the modern power system, power load forecasting is significant to keep the electric Internet of Things in operation. However, it usually needs to collect massive power load data on the server and may face the problem of privacy leakage of raw data. Federated learning can enhance the privacy of the raw power load data of clients by frequently transmitting model updates. Concerning the increasing communication burden of resource-heterogeneous clients resulting from frequent communication with the server, a communication-efficient federated learning algorithm based on Compressed Model Updates and Lazy uploAd (CMULA-FL) was proposed to reduce the communication cost. CMULA-FL also integrates the error compensation strategy to improve the model utility. First, the compression operator is used to compress the transmitted model updates, of which large norms are uploaded to reduce the communication cost of each epoch and transmission frequency. Second, by measuring the error of compression and lazy upload, the error is accumulated to the next epoch to improve the model utility. Finally, based on simulation experiments on the benchmark power load data, the results show that the communication cost decreases at least 60% with controlled loss of model prediction compared with baseline.

The aiming accuracy of the Unmanned Aerial Vehicles (UAV) Steadicam head can be affected by many factors, such as the state of the UAV during the actual flight and the installation error of the system related hardware. In order to eliminate the influence of objective factors on the UAV Steadicam, a Kalman filter aiming algorithm based on the coordinate transformation method is proposed to eliminate the attitude error of the UAV Steadicam and improve the accuracy of the system. The algorithm uses coordinate transformation to eliminate mounting errors and combines coordinate transformation and Kalman filtering methods to eliminate objective errors of the UAV in flight. The experimental simulation results show that our method can accurately give the amount of azimuth and pitch angle error compensation during the flight of the UAV, improving the accuracy of the UAV Steadicam head. Ultimately, the method is applied to the development of a real product.

The application of robots in high-precision automated machining is constrained by their limited multi-directional repeatability. In contrast, robots demonstrate superior levels of unidirectional repeatability, implying the potential for enhancing their precision. To further improve the positioning accuracy of robots, backlash error induced by rotating direction is considered, the concept of robot joint extended space is proposed, and the robot kinematic model is used to analyze the spatial similarity of robot error in the extended joint space. The dynamic Kriging method based on the optimization of basis functions is proposed to avoid overfitting of the surrogate model, and a model for estimating the robot’s positioning error in the joint extended space is constructed. Based on the estimated positioning error, the proposed calibration method is finally experimentally validated by error feedforward compensation. The results indicate that after Kriging interpolation in the robot joint space and feedforward compensation, the maximum/average positioning error of the robot is improved from 1.5157 mm and 0.8562 mm before compensation to 0.3471 mm and 0.1856 mm after compensation, and then further improved to 0.1848 mm and 0.1197 mm after adopting joint expansion space and dynamic Kriging interpolation, which decreases by 46.7% and 33.5%, respectively. This method effectively compensates the multi-directional repeatability error introduced by the joint backlash and improves the robot’s positioning accuracy.

This article proposes a compensation method for gain and offset errors in the current sensing of three-phase AC machine drives with three current sensor measurements during exact or close to standstill conditions. Errors are treated sequentially based on the elliptical trajectory method, a mathematical manipulation by demodulating faulted time-varying $d,q$ currents using the machine position. As three current sensors are used, a correction factor through measured neutral current is proposed such that the impact of machine resistance deviation and inverter non-idealities are minimized. The method does not incorporate any additional hardware, making it low-cost and straightforward in its implementation. It is proposed as a tool for initial commissioning before the machine runs. It is robust to rotor position perturbations and does not need a mechanical lock for compensation in standstill conditions. The effectiveness of the suggested approach is shown by analysis, modelling, and experimental findings acquired from a hardware prototype by mimicking the actual use of high-precision mechatronics.

The ultrasonic anemometer is widely used in the field of wind speed measurement in the coal mine. To compensate for the error of wind speed caused by the shadow effect, we propose a method based on kernel extreme learning machine combined with particle swarm optimization (PSO-KELM). Firstly, we established and analyzed the simulation experiment by computational fluid dynamics (CFD). Secondly, we used the PSO to optimize the parameters of the KELM. The evaluation indexes of the PSO-KELM on the test set are better than other regression models. Finally, the accuracy of the PSO-KELM was verified by experimental data obtained by the ultrasonic anemometer in the wind tunnel. The results show that the compensation effect of the PSO-KELM is better than other regression models. The PSO-KELM model proposed in the paper can effectively compensate for the error of wind speed, and make the measurement results of the ultrasonic anemometer more accurate.

As a fundamental component of heatmap-based human pose estimation methods, heatmap decoding is to transform heatmaps into joint coordinates. We found that previous heatmap decoding methods generally ignored the effect of systematic errors introduced by the resolution increaseing operations in the network decoder. This work fills the gap by taking the systematic errors in heatmap decoding into consideration. We proposed a fast method to reduces the systematic and random errors in one shot by error compensation. The proposed method outperforms the previous best method on the COCO and the MPII datasets while being over 2 times faster. Extensive experiments with different networks, resolutions, metrics and datasets have proved the rationality of the proposed idea.

Remote center of motion (RCM) mechanisms, which are widely used in surgical robots, use mechanical constraints to realize movement around the RCM point. Errors in processing and assembly cause deviation of the RCM point and position error of the end-effector, these deviations cause the robot to exert excessive force on the incision during movement, leading to adverse effects that reduce the safety and accuracy of surgical robots based on the RCM. Based on kinematic calibration, this paper proposes a solution of forward and inverse kinematics considering the RCM point deviation, and this solution can simultaneously reduce the positioning error of the end-effector and the deviation of the RCM point. First, a calibration method based on the combination of the model and neural network is used to improve the kinematic accuracy of the robot. Second, a forward and inverse kinematics solution applied to RCM mechanisms is proposed, and this solution reduces the RCM point deviation during surgery. Finally, experiments were performed with an established surgery robot, verifying that the proposed method can simultaneously reduce the deviation of the RCM point and the position error of the end-effector. The proposed method is found to be superior to other methods and can improve the safety and accuracy of surgical robots.

Till now, most calibration methods only compensate geometric error caused by inaccurate kinematic parameters, while the desired accuracy may still not be achieved when the robot is performing long-stroke, heavy-duty loading and unloading tasks. In this paper, a generalized semi-analytical beam deformation model is firstly proposed for the Cartesian robot to compensate the non-geometric error due to structural deformation under both distributed and concentrated loads. The adjustment factors are introduced in this model to deal with the over-constrained boundary conditions for both intermediate and side modules of the long-stroke Cartesian robot. This improves the fitness of the coming geometric error model, which assumes the beam to be rigid and straight. In addition, as the major error components which do not conform to the Gaussian distribution have been extracted in earlier steps, the Gaussian process regression model is imported to predict the residual error more accurately. In this way, comprehensive geometric and non-geometric error modeling and compensation procedures are formed for the multi-module, long-stroke Cartesian robot. Simulations and real-time experiments are conducted to validate the effectiveness of the proposed method.

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

The manufacturing process of planar microsprings by the UV-LIGA process was introduced; the mechanism of fabrication error was analyzed from three process stages of exposure, SU-8 swelling, and corrosion; and the corresponding improvement measures were proposed. The line width of two groups of microsprings after the exposure, SU-8 swelling, and debonding was measured, and the elastic coefficients in three coordinate directions were accurately measured. The results show that the factors that affect the line width error are stable. The effects of the exposure diffraction, swelling of SU-8 glue, and debonding corrosion process on the line width size follow the order: corrosion error > swelling error > exposure error. When the line width is small, the aspect ratio of the microspring is large, and the direction of machining error is generally negative. The relative error of the elastic coefficient of microsprings in three coordinate directions is larger. The influence rule of the line width error on the elastic coefficient follows the order: kerrory > kerrorx > kerrorz.

This paper proposes an improved backstepping control approach which uses error-compensation and the sliding mode variable structure control to achieve stability for a single machine infinite bus system (SMIBS) with a static synchronous compensator (STATCOM). An adaptive sliding mode control was also utilised to obtain strong response performance in order to avoid excitation and the unknown parameters generated by the recursive steps of traditional backstepping control. Increasing the error-compensation term was used to generate a new virtual control to guarantee that the system states are more rapid stable and eliminate system chattering effectively generated by traditional sliding mode control. The boundedness and convergence of the closed-loop system were then obtained based on Lyapunov stability theory. Finally, a simulation is demonstrated to illustrate the effectiveness and the practicality of the proposed control method.

In order to ensure the electrical performance of the reflector antenna, the surface accuracy constraint need to be satisfied during the structure design. Different calculating methods can get different accuracy values, then influence the difficulty of antenna structure design. Therefore, the evaluation method for antenna surface accuracy is of vital importance. In this paper, the traditional evaluation procedure is first reviewed. This method has been successfully used in structure design of many large antennas, but based on our analysis, this method is sub-optimal. For this reason, a new accuracy evaluation method is introduced, which can get the optimal worst-case surface accuracy. Both quantitative analysis and numerical example of a 110 m radio telescope show that this method can improve the worst-case accuracy effectively. The structural optimization of an 8 m antenna, as a test problem, is also discussed and the results are given.

The strapdown inertial navigation system (SINS) can be installed on a shearer and used for monitoring its position. However, under the complex environment of the mechanized mining face, the strong vibration of the shearer may cause large calculation error. First, the dynamic model of a double-drum shearer is built with a force analysis, and the spectrum characteristics of linear vibration and angular vibration for the fuselage are then obtained. Second, the coning error and sculling error compensation models of SINS for the shearer are derived based on vibration characteristics. Meanwhile, according to the factor of the uncompensated model, multi-sample compensation model, and different coal and rock traits and different vibration frequencies of the fuselage, the shearer SINS error compensation property under multiple parameters is researched and analyzed in simulation. Finally, simulations indicate that the SINS error compensation model with the three-sample algorithm and four-sample algorithm can improve the calculating accuracy of the shearer SINS. The coning and sculling errors can be compensated effectively by the shearer error compensation model under many vibration conditions, such as different coal and rock traits and different frequencies of the fuselage.