The temperature characteristics of a silicon microgyroscope are studied, and the temperature compensation method of the silicon microgyroscope is proposed. First, an open-loop circuit is adopted to test the entire mic...The temperature characteristics of a silicon microgyroscope are studied, and the temperature compensation method of the silicon microgyroscope is proposed. First, an open-loop circuit is adopted to test the entire microgyroscope＇s resonant frequency and quality factor variations over temperature, and the zero bias changing trend over temperature is measured via a closed-loop circuit. Then, in order to alleviate the temperature effects on the performance of the microgyroscope, a kind of temperature compensated method based on the error back propagation（BP）neural network is proposed. By the Matlab simulation, the optimal temperature compensation model based on the BP neural network is well trained after four steps, and the objective error of the microgyroscope＇s zero bias can achieve 0.001 in full temperature range. By the experiment, the real time operation results of the compensation method demonstrate that the maximum zero bias of the microgyroscope can be decreased from 12.43 to 0.75（°）/s after compensation when the ambient temperature varies from -40 to 80℃, which greatly improves the zero bias stability performance of the microgyroscope.展开更多
Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on s...Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on some weapon tracking servo system, a three layer BPNN was used to off line identify the backlash characteristics, then a nonlinear compensator was designed according to the identification results. Results The simulation results show that the method can effectively get rid of the sustained oscillation(limit cycle) of the system caused by the backlash characteristics, and can improve the system accuracy. Conclusion The method is effective on sloving the problems produced by the backlash characteristics in servo systems, and it can be easily accomplished in engineering.展开更多
Due to the characteristics of high efficiency,wide working range,and high flexibility,industrial robots are being increasingly used in the industries of automotive,machining,electrical and electronic,rubber and plasti...Due to the characteristics of high efficiency,wide working range,and high flexibility,industrial robots are being increasingly used in the industries of automotive,machining,electrical and electronic,rubber and plastics,aerospace,food,etc.Whereas the low positioning accuracy,resulted from the serial configuration of industrial robots,has limited their further developments and applications in the field of high requirements for machining accuracy,e.g.,aircraft assembly.In this paper,a neural-network-based approach is proposed to improve the robots’positioning accuracy.Firstly,the neural network,optimized by a genetic particle swarm algorithm,is constructed to model and predict the positioning errors of an industrial robot.Next,the predicted errors are utilized to realize the compensation of the target points at the robot’s workspace.Finally,a series of experiments of the KUKA KR 500–3 industrial robot with no-load and drilling scenarios are implemented to validate the proposed method.The experimental results show that the positioning errors of the robot are reduced from 1.529 mm to 0.344 mm and from 1.879 mm to 0.227 mm for the no-load and drilling conditions,respectively,which means that the position accuracy of the robot is increased by 77.6%and 87.9%for the two experimental conditions,respectively.展开更多
A network time-delay compensation method based on time-delay prediction and implicit proportional-integral-based generalized predictive controller(PIGPC) is proposed. The least squares support vector machine(LSSVM) is...A network time-delay compensation method based on time-delay prediction and implicit proportional-integral-based generalized predictive controller(PIGPC) is proposed. The least squares support vector machine(LSSVM) is used to predict the current time-delay, the parameters of the least squares support vector machine are optimized by particle swarm optimization(PSO) algorithm,and the predicted time-delay is used instead of the actual time-delay as the parameters of the network time-delay compensation controller. In order to improve the compensation effect of implicit generalized predictive controller(GPC), this paper puts forward an implicit generalized predictive control algorithm with proportional-integral-based(PI) structure and designs the controller based on implicit PIGPC. Through the simulation results, the effectiveness of this design in the paper is verified.展开更多
Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpred...Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpredictability and pre-maturing of the results of the genetic algorithm, as well as the slow speed of the training speed of the particle algorithm, a kind of Mind Evolutionary Algorithm optimized BP neural network featuring extremely strong global search capacity was proposed;type KVC850MA/2 five-axis CNC of Changzheng Lathe Factory was used as the research subject, and the Mind Evolutionary Algorithm optimized BP neural network algorithm was used for the establishment of the compensation model between temperature changes and the CNCs’ thermal deformation errors, as well as the realization method on hardware. The simulation results indicated that this method featured extremely high practical value.展开更多
In rolling mill, the accuracy and quality of the strip exit thickness are very important factors. To realize high accuracy in the strip exit thickness, the Automatic Gauge Control (AGC) system is used. Because of roll...In rolling mill, the accuracy and quality of the strip exit thickness are very important factors. To realize high accuracy in the strip exit thickness, the Automatic Gauge Control (AGC) system is used. Because of roll eccentricity in backup rolls, the exit thickness deviates periodically. In this paper, we design PI controller in outer loop for the strip exit thickness while PD controller is used in inner loop for the work roll actuator position. Also, in order to reduce the periodic thickness deviation, we propose roll eccentricity compensation by using Fuzzy Neural Network with online tuning. Simulink model for the overall system has been implemented using MATLAB/SIMULINK software. The simulation results show the effectiveness of the proposed control.展开更多
In order to improve the voltage quality of rural power distribution network, the series capacitor in distribution lines is proposed. The principle of series capacitor compensation technology to improve the quality of ...In order to improve the voltage quality of rural power distribution network, the series capacitor in distribution lines is proposed. The principle of series capacitor compensation technology to improve the quality of rural power distribution lines voltage is analyzed. The real rural power distribution network simulation model is established by Power System Power System Analysis Software Package (PSASP). Simulation analysis the effect of series capacitor compensation technology to improve the voltage quality of rural power distribution network, The simulation results show that the series capacitor compensation can effectively improve the voltage quality and reduce network losses and improve the transmission capacity of rural power distribution network.展开更多
The capacitive reactive power reversal in the urban distribution grid is increasingly prominent at the period of light load in the last years.In severe cases,it will endanger the security and stability of power grid.T...The capacitive reactive power reversal in the urban distribution grid is increasingly prominent at the period of light load in the last years.In severe cases,it will endanger the security and stability of power grid.This paper presents an optimal reactive power compensation method of distribution network to prevent reactive power reverse.Firstly,an integrated reactive power planning(RPP)model with power factor constraints is established.Capacitors and reactors are considered to be installed in the distribution system at the same time.The objective function is the cost minimization of compensation and real power loss with transformers and lines during the planning period.Nodal power factor limits and reactor capacity constraints are new constraints.Then,power factor sensitivity with respect to reactive power is derived.An improved genetic algorithm by power factor sensitivity is used to solve the model.The optimal locations and sizes of reactors and capacitors can avoid reactive power reversal and power factor exceeding the limit.Finally,the effectiveness of the model and algorithm is proven by a typical high-voltage distribution network.展开更多
The machining precision not only depends on accurate mechanical structure but also depends on motion compensation method. If manufacturing precision of mechanical structure cannot be improved, the motion compensation ...The machining precision not only depends on accurate mechanical structure but also depends on motion compensation method. If manufacturing precision of mechanical structure cannot be improved, the motion compensation is a reasonable way to improve motion precision. A motion compensation method based on neural network of radial basis function(RBF) was presented in this paper. It utilized the infinite approximation advantage of RBF neural network to fit the motion error curve. The best hidden neural quantity was optimized by training the motion error data and calculating the total sum of squares. The best curve coefficient matrix was got and used to calculate motion compensation values. The experiments showed that the motion errors could be reduced obviously by utilizing the method in this paper.展开更多
A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identi...A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify parameters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed.展开更多
:Machine Learning(ML)algorithms have been widely used for financial time series prediction and trading through bots.In this work,we propose a Predictive Error Compensated Wavelet Neural Network(PEC-WNN)ML model that i...:Machine Learning(ML)algorithms have been widely used for financial time series prediction and trading through bots.In this work,we propose a Predictive Error Compensated Wavelet Neural Network(PEC-WNN)ML model that improves the prediction of next day closing prices.In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs.An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence.The performance of the proposed model is evaluated using six different stock data samples in the New York stock exchange.The results have demonstrated significant improvement in forecasting accuracy in all cases when the second network is used in accordance with the first one by adding the outputs.The RMSE error is 33%improved when the proposed PEC-WNN model is used compared to the Long ShortTerm Memory(LSTM)model.Furthermore,through the analysis of training mechanisms,we found that using the updated training the performance of the proposed model is improved.The contribution of this study is the applicability of simultaneously different time frames as inputs.Cascading the predictive error compensation not only reduces the error rate but also helps in avoiding overfitting problems.展开更多
Synthetic aperture radars(SARs)encounter the azimuth cutoff problem when observing sea waves.Consequently,SARs can only capture the waves with wavelengths larger than the cutoff wavelength and lose the information of ...Synthetic aperture radars(SARs)encounter the azimuth cutoff problem when observing sea waves.Consequently,SARs can only capture the waves with wavelengths larger than the cutoff wavelength and lose the information of waves with smaller wavelengths.To increase the accuracy of SAR wave observations,this paper investigates an azimuth cutoff compensation method based on the simulated multiview SAR wave synchronization data obtained by the collaborative observation via networked satellites.Based on the simulated data and the equivalent multiview measured data from Sentinel-1 virtual networking,the method is verified and the cutoff wavelengths decrease by 16.40%and 14.00%.The biases of the inversion significant wave height with true values decrease by 0.04 m and 0.14 m,and the biases of the mean wave period decrease by 0.17 s and 0.22 s,respectively.These results demonstrate the effectiveness of the azimuth cutoff compensation method.Based on the azimuth cutoff compensation method,the multisatellite SAR networking mode for wave observations are discussed.The highest compensation effect is obtained when the combination of azimuth angle is(95°,115°,135°),the orbital intersection angle is(50°,50°),and three or four satellites are used.The study of the multisatellite networking mode in this paper can provide valuable references for the compensation of azimuth cutoff and the observation of waves by a multisatellite network.展开更多
The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nod...The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nodes without the support of the Global Navigation Satellite System(GNSS)and other prior information remains a formidable challenge to real-time wireless networks design.Therefore,a self-organizing network methodology based on multi-agent negotiation is proposed,which autonomously determines the master node through collaborative negotiation and competitive elections.On this basis,a real-time network protocol design is carried out and a high-precision time synchronization method with motion compensation is proposed.Simulation results demonstrate that the proposed method enables rapid networking with the capability of selfdiscovery,self-organization,and self-healing.For a cluster of 8 satellites,the networking time and the reorganization time are less than 4 s.The time synchronization accuracy exceeds 10-10s with motion compensation,demonstrating excellent real-time performance and stability.The research presented in this paper provides a valuable reference for the design and application of spacebased self-organizing networks for satellite cluster.展开更多
The rise of time-sensitive applications with broad geographical scope drives the development of time-sensitive networking(TSN)from intra-domain to inter-domain to ensure overall end-to-end connectivity requirements in...The rise of time-sensitive applications with broad geographical scope drives the development of time-sensitive networking(TSN)from intra-domain to inter-domain to ensure overall end-to-end connectivity requirements in heterogeneous deployments.When multiple TSN networks interconnect over non-TSN networks,all devices in the network need to be syn-chronized by sharing a uniform time reference.How-ever,most non-TSN networks are best-effort.Path delay asymmetry and random noise accumulation can introduce unpredictable time errors during end-to-end time synchronization.These factors can degrade syn-chronization performance.Therefore,cross-domain time synchronization becomes a challenging issue for multiple TSN networks interconnected by non-TSN networks.This paper presents a cross-domain time synchronization scheme that follows the software-defined TSN(SD-TSN)paradigm.It utilizes a com-bined control plane constructed by a coordinate con-troller and a domain controller for centralized control and management of cross-domain time synchroniza-tion.The general operation flow of the cross-domain time synchronization process is designed.The mecha-nism of cross-domain time synchronization is revealed by introducing a synchronization model and an error compensation method.A TSN cross-domain proto-type testbed is constructed for verification.Results show that the scheme can achieve end-to-end high-precision time synchronization with accuracy and sta-bility.展开更多
The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation...The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation,aircraft maneuver interference field,and geomagnetic field.In this paper,appropriate physical features and the modular feedforward neural network(MFNN)with Levenberg-Marquard(LM)back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data.LM algorithm is a kind of least square estimation algorithm of nonlinear parameters.It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method.LM algorithm’s computing efficiency is high and fitting error is very low.The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON(T-L)model with the linear least square(LS)solution by fitting experiments with five different aeromagnetic surveys’data.展开更多
Because of long driving chain and great system load inertia, the serial manipulator has a serious time delay problem which leads to significant real-time tracking control errors and damages the welding quality finally...Because of long driving chain and great system load inertia, the serial manipulator has a serious time delay problem which leads to significant real-time tracking control errors and damages the welding quality finally. In order to solve the time delay problem and enhance the welding quality, an adaptive real-time predictive compensation control（ARTPCC） is presented in this paper. The ARTPCC technique combines offline identification and online compensation. Based on the neural network system identification technique, the ARTPCC technique identifies the dynamic joint model of the 6-DOF serial arc welding manipulator offline. With the identified dynamic joint model, the ARTPCC technique predicts and compensates the tracking error online using the adaptive friction compensation technique. The ARTPCC technique is proposed in detail in this paper and applied in the real-time tracking control experiment of the 6-DOF serial arc welding manipulator. The tracking control experiment results of the end-effector reference point of the manipulator show that the presented control technique reduces the tracking error, enhances the system response and tracking accuracy efficiently. Meanwhile, the welding experiment results show that the welding seam turns more continuous, uniform and smooth after using the ARTPCC technique. With the ARTPCC technique, the welding quality of the 6-DOF serial arc welding manipulator is highly improved.展开更多
A dispersion compensation method is introduced to correct the distorted image passing through an ultrathin metal film.An LCD-CCD system is modeled by the back propagation network and used to evaluate the transmittance...A dispersion compensation method is introduced to correct the distorted image passing through an ultrathin metal film.An LCD-CCD system is modeled by the back propagation network and used to evaluate the transmittance of the ultrathin metal film.Training samples for the network come from 729 images captured by shooting test patches,in which the RGB values are uniformity distributed between 0 and 255.The RGB value of the original image that will be distorted by the dispersion is first transformed by mapping from the LCD to the CCD,multiplied by the inverse matrix of the transmittance matrix,and finally transformed by mapping from the CCD to the LCD,then the corrected image is obtained.In order to verify the effectiveness of the proposed method,ultrathin aluminum films with different thicknesses are evaporated on glass substrates and laid between the CCD and LCD.Experimental results show that the proposed method compensates for the dispersion successfully.展开更多
The process of a γ-irradiation experiment of fibre optical gyroscope （FOG） control circuit was described, in which it is demonstrated that the FOG control circuit, except for D/A converter, could endure the dose of...The process of a γ-irradiation experiment of fibre optical gyroscope （FOG） control circuit was described, in which it is demonstrated that the FOG control circuit, except for D/A converter, could endure the dose of 10krad with the protection of cabin material. The distortion and drift in D/A converter due to radiation, which affect the performance of FOG seriously, was indicated based on the elemental analysis. Finally, a compensation network based on adaptive neuro-fuzzy inference system is proposed and its function is verified by simulation.展开更多
Accuracy is one of the most important key indices to evaluate multi-axis systems’ (MAS’s) characteristics and performances. The accuracy of MAS’s such as machine tools, measuring machines and robots is adversely af...Accuracy is one of the most important key indices to evaluate multi-axis systems’ (MAS’s) characteristics and performances. The accuracy of MAS’s such as machine tools, measuring machines and robots is adversely affected by various error sources, including geometric imperfections, thermal deformations, load effects, and dynamic disturbances. The increasing demand for higher dimensional accuracy in various industrial applications has created the need to develop cost-effective methods for enhancing the overall performance of these mechanisms. Improving the accuracy of a MAS by upgrading the physical structure would lead to an exponential increase in manufacturing costs without totally eliminating geometrical deviations and thermal deformations of MAS components. Hence, the idea of reducing MAS’s error by a software-based alternative approach to provide real-time prediction and correction of geometric and thermally induced errors is considered a strategic step toward achieving the full potential of the MAS. This paper presents a structured approach designed to improve the accuracy of Cartesian MAS’s through software error compensation. Four steps are required to develop and implement this approach: (i) measurement of error components using a multidimensional laser interferometer system, (ii) tridimensional volumetric error mapping using rigid body kinematics, (iii) volumetric error prediction via an artificial neural network model, and finally (iv) implementation of the on-line error compensation. An illustrative example using a bridge type coordinate measuring machine is presented.展开更多
基金The National High Technology Research and Development Program of China (863 Program)(No.2002AA812038)the NationalNatural Science Foundation of China (No.60974116)
文摘The temperature characteristics of a silicon microgyroscope are studied, and the temperature compensation method of the silicon microgyroscope is proposed. First, an open-loop circuit is adopted to test the entire microgyroscope＇s resonant frequency and quality factor variations over temperature, and the zero bias changing trend over temperature is measured via a closed-loop circuit. Then, in order to alleviate the temperature effects on the performance of the microgyroscope, a kind of temperature compensated method based on the error back propagation（BP）neural network is proposed. By the Matlab simulation, the optimal temperature compensation model based on the BP neural network is well trained after four steps, and the objective error of the microgyroscope＇s zero bias can achieve 0.001 in full temperature range. By the experiment, the real time operation results of the compensation method demonstrate that the maximum zero bias of the microgyroscope can be decreased from 12.43 to 0.75（°）/s after compensation when the ambient temperature varies from -40 to 80℃, which greatly improves the zero bias stability performance of the microgyroscope.
文摘Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on some weapon tracking servo system, a three layer BPNN was used to off line identify the backlash characteristics, then a nonlinear compensator was designed according to the identification results. Results The simulation results show that the method can effectively get rid of the sustained oscillation(limit cycle) of the system caused by the backlash characteristics, and can improve the system accuracy. Conclusion The method is effective on sloving the problems produced by the backlash characteristics in servo systems, and it can be easily accomplished in engineering.
基金co-supported by the Natural Science Foundation of Jiangsu Province(No.BK20190417)the National Natural Science Foundation of China(No.52005254)the National Key R&D Program of China(No.2018YFB1306800)。
文摘Due to the characteristics of high efficiency,wide working range,and high flexibility,industrial robots are being increasingly used in the industries of automotive,machining,electrical and electronic,rubber and plastics,aerospace,food,etc.Whereas the low positioning accuracy,resulted from the serial configuration of industrial robots,has limited their further developments and applications in the field of high requirements for machining accuracy,e.g.,aircraft assembly.In this paper,a neural-network-based approach is proposed to improve the robots’positioning accuracy.Firstly,the neural network,optimized by a genetic particle swarm algorithm,is constructed to model and predict the positioning errors of an industrial robot.Next,the predicted errors are utilized to realize the compensation of the target points at the robot’s workspace.Finally,a series of experiments of the KUKA KR 500–3 industrial robot with no-load and drilling scenarios are implemented to validate the proposed method.The experimental results show that the positioning errors of the robot are reduced from 1.529 mm to 0.344 mm and from 1.879 mm to 0.227 mm for the no-load and drilling conditions,respectively,which means that the position accuracy of the robot is increased by 77.6%and 87.9%for the two experimental conditions,respectively.
基金supported by National Natural Science Foundation of China(No.61034005)Liaoning Province Doctor Startup Fund(No.20141070)
文摘A network time-delay compensation method based on time-delay prediction and implicit proportional-integral-based generalized predictive controller(PIGPC) is proposed. The least squares support vector machine(LSSVM) is used to predict the current time-delay, the parameters of the least squares support vector machine are optimized by particle swarm optimization(PSO) algorithm,and the predicted time-delay is used instead of the actual time-delay as the parameters of the network time-delay compensation controller. In order to improve the compensation effect of implicit generalized predictive controller(GPC), this paper puts forward an implicit generalized predictive control algorithm with proportional-integral-based(PI) structure and designs the controller based on implicit PIGPC. Through the simulation results, the effectiveness of this design in the paper is verified.
文摘Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpredictability and pre-maturing of the results of the genetic algorithm, as well as the slow speed of the training speed of the particle algorithm, a kind of Mind Evolutionary Algorithm optimized BP neural network featuring extremely strong global search capacity was proposed;type KVC850MA/2 five-axis CNC of Changzheng Lathe Factory was used as the research subject, and the Mind Evolutionary Algorithm optimized BP neural network algorithm was used for the establishment of the compensation model between temperature changes and the CNCs’ thermal deformation errors, as well as the realization method on hardware. The simulation results indicated that this method featured extremely high practical value.
文摘In rolling mill, the accuracy and quality of the strip exit thickness are very important factors. To realize high accuracy in the strip exit thickness, the Automatic Gauge Control (AGC) system is used. Because of roll eccentricity in backup rolls, the exit thickness deviates periodically. In this paper, we design PI controller in outer loop for the strip exit thickness while PD controller is used in inner loop for the work roll actuator position. Also, in order to reduce the periodic thickness deviation, we propose roll eccentricity compensation by using Fuzzy Neural Network with online tuning. Simulink model for the overall system has been implemented using MATLAB/SIMULINK software. The simulation results show the effectiveness of the proposed control.
文摘In order to improve the voltage quality of rural power distribution network, the series capacitor in distribution lines is proposed. The principle of series capacitor compensation technology to improve the quality of rural power distribution lines voltage is analyzed. The real rural power distribution network simulation model is established by Power System Power System Analysis Software Package (PSASP). Simulation analysis the effect of series capacitor compensation technology to improve the voltage quality of rural power distribution network, The simulation results show that the series capacitor compensation can effectively improve the voltage quality and reduce network losses and improve the transmission capacity of rural power distribution network.
文摘The capacitive reactive power reversal in the urban distribution grid is increasingly prominent at the period of light load in the last years.In severe cases,it will endanger the security and stability of power grid.This paper presents an optimal reactive power compensation method of distribution network to prevent reactive power reverse.Firstly,an integrated reactive power planning(RPP)model with power factor constraints is established.Capacitors and reactors are considered to be installed in the distribution system at the same time.The objective function is the cost minimization of compensation and real power loss with transformers and lines during the planning period.Nodal power factor limits and reactor capacity constraints are new constraints.Then,power factor sensitivity with respect to reactive power is derived.An improved genetic algorithm by power factor sensitivity is used to solve the model.The optimal locations and sizes of reactors and capacitors can avoid reactive power reversal and power factor exceeding the limit.Finally,the effectiveness of the model and algorithm is proven by a typical high-voltage distribution network.
基金supported by the Project of National Natural Science Foundation of China(51275052)the Project of Science and Technique Development Plan of Beijing Municipal Commission of Education(KM201311232022)
文摘The machining precision not only depends on accurate mechanical structure but also depends on motion compensation method. If manufacturing precision of mechanical structure cannot be improved, the motion compensation is a reasonable way to improve motion precision. A motion compensation method based on neural network of radial basis function(RBF) was presented in this paper. It utilized the infinite approximation advantage of RBF neural network to fit the motion error curve. The best hidden neural quantity was optimized by training the motion error data and calculating the total sum of squares. The best curve coefficient matrix was got and used to calculate motion compensation values. The experiments showed that the motion errors could be reduced obviously by utilizing the method in this paper.
文摘A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify parameters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed.
基金This study is based on the research project“Development of Cyberdroid based on Cognitive Intelligent system applications”(2019–2020)funded by Crypttech company(https://www.crypttech.com/en/)within the contract by ITUNOVA,Istanbul Technical University Technology Transfer Office.
文摘:Machine Learning(ML)algorithms have been widely used for financial time series prediction and trading through bots.In this work,we propose a Predictive Error Compensated Wavelet Neural Network(PEC-WNN)ML model that improves the prediction of next day closing prices.In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs.An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence.The performance of the proposed model is evaluated using six different stock data samples in the New York stock exchange.The results have demonstrated significant improvement in forecasting accuracy in all cases when the second network is used in accordance with the first one by adding the outputs.The RMSE error is 33%improved when the proposed PEC-WNN model is used compared to the Long ShortTerm Memory(LSTM)model.Furthermore,through the analysis of training mechanisms,we found that using the updated training the performance of the proposed model is improved.The contribution of this study is the applicability of simultaneously different time frames as inputs.Cascading the predictive error compensation not only reduces the error rate but also helps in avoiding overfitting problems.
基金the support of the National Natural Science Foundation of China(No.61931025)the National Key R&D Program of China(No.2017YFC1405600)。
文摘Synthetic aperture radars(SARs)encounter the azimuth cutoff problem when observing sea waves.Consequently,SARs can only capture the waves with wavelengths larger than the cutoff wavelength and lose the information of waves with smaller wavelengths.To increase the accuracy of SAR wave observations,this paper investigates an azimuth cutoff compensation method based on the simulated multiview SAR wave synchronization data obtained by the collaborative observation via networked satellites.Based on the simulated data and the equivalent multiview measured data from Sentinel-1 virtual networking,the method is verified and the cutoff wavelengths decrease by 16.40%and 14.00%.The biases of the inversion significant wave height with true values decrease by 0.04 m and 0.14 m,and the biases of the mean wave period decrease by 0.17 s and 0.22 s,respectively.These results demonstrate the effectiveness of the azimuth cutoff compensation method.Based on the azimuth cutoff compensation method,the multisatellite SAR networking mode for wave observations are discussed.The highest compensation effect is obtained when the combination of azimuth angle is(95°,115°,135°),the orbital intersection angle is(50°,50°),and three or four satellites are used.The study of the multisatellite networking mode in this paper can provide valuable references for the compensation of azimuth cutoff and the observation of waves by a multisatellite network.
基金supported by the National Natural Science Foundation of China(No.62401597)the Natural Science Foundation of Hunan Province,China(No.2024JJ6469)the Scientific Research Project of National University of Defense Technology,China(No.ZK22-02)。
文摘The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nodes without the support of the Global Navigation Satellite System(GNSS)and other prior information remains a formidable challenge to real-time wireless networks design.Therefore,a self-organizing network methodology based on multi-agent negotiation is proposed,which autonomously determines the master node through collaborative negotiation and competitive elections.On this basis,a real-time network protocol design is carried out and a high-precision time synchronization method with motion compensation is proposed.Simulation results demonstrate that the proposed method enables rapid networking with the capability of selfdiscovery,self-organization,and self-healing.For a cluster of 8 satellites,the networking time and the reorganization time are less than 4 s.The time synchronization accuracy exceeds 10-10s with motion compensation,demonstrating excellent real-time performance and stability.The research presented in this paper provides a valuable reference for the design and application of spacebased self-organizing networks for satellite cluster.
基金supported in part by National Key R&D Program of China(Grant No.2022YFC3803700)in part by the National Natural Science Foundation of China(Grant No.92067102)in part by the project of Beijing Laboratory of Advanced Information Networks.
文摘The rise of time-sensitive applications with broad geographical scope drives the development of time-sensitive networking(TSN)from intra-domain to inter-domain to ensure overall end-to-end connectivity requirements in heterogeneous deployments.When multiple TSN networks interconnect over non-TSN networks,all devices in the network need to be syn-chronized by sharing a uniform time reference.How-ever,most non-TSN networks are best-effort.Path delay asymmetry and random noise accumulation can introduce unpredictable time errors during end-to-end time synchronization.These factors can degrade syn-chronization performance.Therefore,cross-domain time synchronization becomes a challenging issue for multiple TSN networks interconnected by non-TSN networks.This paper presents a cross-domain time synchronization scheme that follows the software-defined TSN(SD-TSN)paradigm.It utilizes a com-bined control plane constructed by a coordinate con-troller and a domain controller for centralized control and management of cross-domain time synchroniza-tion.The general operation flow of the cross-domain time synchronization process is designed.The mecha-nism of cross-domain time synchronization is revealed by introducing a synchronization model and an error compensation method.A TSN cross-domain proto-type testbed is constructed for verification.Results show that the scheme can achieve end-to-end high-precision time synchronization with accuracy and sta-bility.
基金National key special projects for major scientific instruments and equipment development(2017YFF0107400)。
文摘The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation,aircraft maneuver interference field,and geomagnetic field.In this paper,appropriate physical features and the modular feedforward neural network(MFNN)with Levenberg-Marquard(LM)back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data.LM algorithm is a kind of least square estimation algorithm of nonlinear parameters.It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method.LM algorithm’s computing efficiency is high and fitting error is very low.The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON(T-L)model with the linear least square(LS)solution by fitting experiments with five different aeromagnetic surveys’data.
文摘Because of long driving chain and great system load inertia, the serial manipulator has a serious time delay problem which leads to significant real-time tracking control errors and damages the welding quality finally. In order to solve the time delay problem and enhance the welding quality, an adaptive real-time predictive compensation control（ARTPCC） is presented in this paper. The ARTPCC technique combines offline identification and online compensation. Based on the neural network system identification technique, the ARTPCC technique identifies the dynamic joint model of the 6-DOF serial arc welding manipulator offline. With the identified dynamic joint model, the ARTPCC technique predicts and compensates the tracking error online using the adaptive friction compensation technique. The ARTPCC technique is proposed in detail in this paper and applied in the real-time tracking control experiment of the 6-DOF serial arc welding manipulator. The tracking control experiment results of the end-effector reference point of the manipulator show that the presented control technique reduces the tracking error, enhances the system response and tracking accuracy efficiently. Meanwhile, the welding experiment results show that the welding seam turns more continuous, uniform and smooth after using the ARTPCC technique. With the ARTPCC technique, the welding quality of the 6-DOF serial arc welding manipulator is highly improved.
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2009AA044001)the Open Funds of the State Key Laboratory of Robotics and Systems (HIT),China (Grant No. SKLRS-2010-MS-01)the Fundamental Research Funds for the Central Universities,China
文摘A dispersion compensation method is introduced to correct the distorted image passing through an ultrathin metal film.An LCD-CCD system is modeled by the back propagation network and used to evaluate the transmittance of the ultrathin metal film.Training samples for the network come from 729 images captured by shooting test patches,in which the RGB values are uniformity distributed between 0 and 255.The RGB value of the original image that will be distorted by the dispersion is first transformed by mapping from the LCD to the CCD,multiplied by the inverse matrix of the transmittance matrix,and finally transformed by mapping from the CCD to the LCD,then the corrected image is obtained.In order to verify the effectiveness of the proposed method,ultrathin aluminum films with different thicknesses are evaporated on glass substrates and laid between the CCD and LCD.Experimental results show that the proposed method compensates for the dispersion successfully.
文摘The process of a γ-irradiation experiment of fibre optical gyroscope （FOG） control circuit was described, in which it is demonstrated that the FOG control circuit, except for D/A converter, could endure the dose of 10krad with the protection of cabin material. The distortion and drift in D/A converter due to radiation, which affect the performance of FOG seriously, was indicated based on the elemental analysis. Finally, a compensation network based on adaptive neuro-fuzzy inference system is proposed and its function is verified by simulation.
文摘Accuracy is one of the most important key indices to evaluate multi-axis systems’ (MAS’s) characteristics and performances. The accuracy of MAS’s such as machine tools, measuring machines and robots is adversely affected by various error sources, including geometric imperfections, thermal deformations, load effects, and dynamic disturbances. The increasing demand for higher dimensional accuracy in various industrial applications has created the need to develop cost-effective methods for enhancing the overall performance of these mechanisms. Improving the accuracy of a MAS by upgrading the physical structure would lead to an exponential increase in manufacturing costs without totally eliminating geometrical deviations and thermal deformations of MAS components. Hence, the idea of reducing MAS’s error by a software-based alternative approach to provide real-time prediction and correction of geometric and thermally induced errors is considered a strategic step toward achieving the full potential of the MAS. This paper presents a structured approach designed to improve the accuracy of Cartesian MAS’s through software error compensation. Four steps are required to develop and implement this approach: (i) measurement of error components using a multidimensional laser interferometer system, (ii) tridimensional volumetric error mapping using rigid body kinematics, (iii) volumetric error prediction via an artificial neural network model, and finally (iv) implementation of the on-line error compensation. An illustrative example using a bridge type coordinate measuring machine is presented.
