The digital twin,as the decision center of the automated drilling system,incorporates physical or data-driven models to predict the system response(rate of penetration,down-hole circulating pressure,drilling torques,e...The digital twin,as the decision center of the automated drilling system,incorporates physical or data-driven models to predict the system response(rate of penetration,down-hole circulating pressure,drilling torques,etc.).Real-time drilling torque prediction aids in drilling parameter optimization,drill string stabilization,and comparing the discrepancy between observed signal and theoretical trend to detect down-hole anomalies.Due to their inability to handle huge amounts of time series data,current machine learning techniques are unsuitable for the online prediction of drilling torque.Therefore,a new way,the just-in-time learning(JITL)framework and local machine learning model,are proposed to solve the problem.The steps in this method are:(1)a specific metric is designed to measure the similarity between time series drilling data and scenarios to be predicted ahead of bit;(2)parts of drilling data are selected to train a local model for a specific prediction scenario separately;(3)the local machine learning model is used to predict drilling torque ahead of bit.Both the model data test results and the field data application results certify the advantages of the method over the traditional sliding window methods.Moreover,the proposed method has been proven to be effective in drilling parameter optimization and pipe sticking trend detection.Finally,we offer suggestions for the selection of local machine learning algorithms and real-time prediction with this approach based on the test results.展开更多
A full-face rock tunnel boring machine(TBM)has two main parameters:cutterhead thrust and cutterhead torque.Cutterhead thrust can be predicted numerically using different methods,such as the rated thrust of the disc cu...A full-face rock tunnel boring machine(TBM)has two main parameters:cutterhead thrust and cutterhead torque.Cutterhead thrust can be predicted numerically using different methods,such as the rated thrust of the disc cutter,the indentation test of the disc cutter,a linear breaking experiment,or applying statistical models based on field data.However,cutterhead torque prediction still lacks the necessary and sufficient theoretical analysis and corresponding research.Consequently,we studied the rock-breaking characteristics of the disc cutter on the cutterhead and found a difference between the lateral friction resistance and the inner and outer rock breaking.We then proposed the cutterhead torque composition of the full-face rock tunnel boring machine,shoveling and transporting ballast torque,rolling resistance torque,lateral friction resistance torque,and disc cutter lateral imbalance torque,and established the corresponding theory.Through practical engineering ver-ification,we found that the relative error between the cutterhead torque value calculated by the theory and the actual value was only 0.51%.If the lateral friction resistance moment of the disc cutter rock breaking and the lateral imbalance moment of the disc cutter are not considered,the relative error between the calculated value of cutterhead torque and the actual value is 7.48%.Therefore,the accuracy of the established cutterhead torque theory is 6.97%higher than that of the disc cutter linear rolling crushing rock test theory.The research results provide an important reference for determining the cutterhead torque of a full-face rock tunnel boring machine.展开更多
Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented...Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for realtime cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.展开更多
The development of vehicle-to-everything and cloud computing has brought new opportunities and challenges to the automobile industry.In this paper,a commuter vehicle demand torque prediction method based on historical...The development of vehicle-to-everything and cloud computing has brought new opportunities and challenges to the automobile industry.In this paper,a commuter vehicle demand torque prediction method based on historical vehicle speed information is proposed,which uses machine learning to predict and analyze vehicle demand torque.Firstly,the big data of vehicle driving is collected,and the driving data is cleaned and features extracted based on road information.Then,the vehicle longitudinal driving dynamics model is established.Next,the vehicle simulation simulator is established based on the longitudinal driving dynamics model of the vehicle,and the driving torque of the vehicle is obtained.Finally,the travel is divided into several accelerationcruise-deceleration road pairs for analysis,and the vehicle demand torque is predicted by BP neural network and Gaussian process regression.展开更多
In order to compensate for the disturbance of wide variation in rotor demanded torque on power turbine speed and realize the fast response control of turboshaft engine during variable rotor speed,a cascade PID control...In order to compensate for the disturbance of wide variation in rotor demanded torque on power turbine speed and realize the fast response control of turboshaft engine during variable rotor speed,a cascade PID control method based on the acceleration estimator of gas turbine speed(Ngdot)and rotor predicted torque feedforward is proposed.Firstly,a two-speed Dual Clutch Transmission(DCT)model is applied in the integrated rotor/turboshaft engine system to achieve variable rotor speed.Then,an online estimation method of Ngdot based on the Linear Quadratic Gaussian with Loop Transfer Recovery(LQG/LTR)is proposed for power turbine speed cascade control.Finally,according to the cascade PID controller based on Ngdot estimator,a rotor demanded torque predicted method based on the Min-batch Gradient Descent-Neural Network(MGD-NN)is put forward to compromise the influence of rotor torque interference.The simulation results show that compared with cascade PID controller based on Ngdot estimator and the one combined with collective pitch feedforward control,the novel control method proposed can reduce the overshoot of power turbine speed by more than 20%,which possesses faster response,superior dynamic effect and satisfactory robustness performance.The control method proposed can realize the fast response control of turboshaft engine with variable rotor speed better.展开更多
An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated rec...An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.展开更多
Finite control set model predictive torque control(FCS-MPTC)has become increasingly prevalent for induction motors(IM)owing to its simple concept,easy incorporation of constraints and strong flexibility.In traditional...Finite control set model predictive torque control(FCS-MPTC)has become increasingly prevalent for induction motors(IM)owing to its simple concept,easy incorporation of constraints and strong flexibility.In traditional FCS-MPTC speed controller design,a classical proportional integral(PI)controller is typically chosen to generate the torque reference.However,the PI controller is dependent on system parameters and sensitive to the load torque variation,which seriously affects control performance.In this paper,a model predictive torque control using sliding mode control(MPTC+SMC)for IM is proposed to enhance the robust performance of the drive system.First,the influence of the parameter mismatches for FCS-MPTC is analyzed.Second,the shortcomings of traditional PI controller are derived.Then,the proposed MPTC+SMC method is designed,and the MPTC+PI and MPTC+SMC are compared theoretically.Finally,experimental results demonstrate the correctness and effectiveness of the proposed MPTC+SMC.In comparison with MPTC+PI,MPTC+SMC has the better dynamic performance and stronger robust performance against parameter variations and load disturbance.展开更多
A global fast terminal sliding mode(GFTSM)-based model predictive torque control(MPTC)strategy is developed for permanent magnet synchronous motor(PMSM)drive system with only one phase current sensor.Generally two pha...A global fast terminal sliding mode(GFTSM)-based model predictive torque control(MPTC)strategy is developed for permanent magnet synchronous motor(PMSM)drive system with only one phase current sensor.Generally two phase-current sensors are indispensable for MPTC.In response to only one phase current sensor available and the change of stator resistance,a novel adaptive observer for estimating the remaining two phase currents and time-varying stator resistance is proposed to perform MPTC.Moreover,in view of the variation of system parameters and external disturbance,a new GFTSM-based speed regulator is synthesized to enhance the drive system robustness.In this paper,the GFTSM,based on sliding mode theory,employs the fast terminal sliding mode in both the reaching stage and the sliding stage.The resultant GFTSM-based MPTC PMSM drive system with single phase current sensor has excellent dynamical performance which is very close to the GFTSM-based MPTC PMSM drive system with two-phase current sensors.On the other hand,compared with proportional-integral(PI)-based and sliding mode(SM)-based MPTC PMSM drive systems,it possesses better dynamical response and stronger robustness as well as smaller total harmonic distortion(THD)index of three-phase stator currents in the presence of variation of load torque.The simulation results validate the feasibility and effectiveness of the proposed scheme.展开更多
Vector-controlled AC motor drives utilize pulse width modulation(PWM)to synthesize the desired output voltage of the voltage source inverter(VSI).In space vector PWM(SVPWM)techniques,the average realization of the spa...Vector-controlled AC motor drives utilize pulse width modulation(PWM)to synthesize the desired output voltage of the voltage source inverter(VSI).In space vector PWM(SVPWM)techniques,the average realization of the space vector applying the volt-sec balance principle results in an instantaneous error voltage that generates high frequency torque ripple.It may lead to an increase in motor vibration and acoustic noise.This article presents a high frequency torque ripple prediction model based on stator flux ripple and proposes a targeted designed variable switching frequency PWM(VSFPWM)strategy to diminish high frequency torque ripple.The switching frequency is dynamically adjusted according to the peak value of the predicted stator flux ripple to mitigate high frequency torque ripple.In contrast to existing strategies,the strategy outlined in this article directly suppresses high frequency torque ripple,thus remaining unaffected by inaccurate motor parameters.Additionally,due to the introduction of the power factor angle,the proposed strategy can better adapt to the full speed range operating conditions of the motor.Detailed simulations and experiments are provided to validate the effectiveness of the proposed strategy.展开更多
Employing matrix converter （MC） as driving mode, the strategy of model predictive torque control （MPTC） is proposed for three phase permanent magnet synchronous motor （PMSM） system. MC is applied instead of conv...Employing matrix converter （MC） as driving mode, the strategy of model predictive torque control （MPTC） is proposed for three phase permanent magnet synchronous motor （PMSM） system. MC is applied instead of conventional AC DC AC converter to increase the power factor （PF） of the system input side. MPTC is used to select optimal voltage space vector to enable the system to have satisfactory torque and flux control effect. The resultant MPTC strategy not only makes the MC fed PMSM system operate reliably and have perfect control performance, but also makes the PF of the system input side be 1. Compared with direct torque control （DTC）, the proposed MPTC strategy guarantees that MC fed PMSM has better command following characteristics in the presence of variation of load torque and tracking reference speed. Simulation results verify the feasibility and effectiveness of the proposed strategy.展开更多
Aiming at the torque and flux ripples in the direct torque control and the time-varying parameters for permanent magnet synchronous motor (PMSM), a model predictive direct torque control with online parameter estimati...Aiming at the torque and flux ripples in the direct torque control and the time-varying parameters for permanent magnet synchronous motor (PMSM), a model predictive direct torque control with online parameter estimation based on the extended Kalman filter for PMSM is designed. By predicting the errors of torque and flux based on the model and the current states of the system, the optimal voltage vector is selected to minimize the error of torque and flux. The stator resistance and inductance are estimated online via EKF to reduce the effect of model error and the current estimation can reduce the error caused by measurement noise. The stability of the EKF is proved in theory. The simulation experiment results show the method can estimate the motor parameters, reduce the torque, and flux ripples and improve the performance of direct torque control for permanent magnet synchronous motor (PMSM).展开更多
A novel double extended state observer(DESO)based on model predictive torque control(MPTC)strategy is developed for three-phase permanent magnet synchronous motor(PMSM)drive system without current sensor.In general,to...A novel double extended state observer(DESO)based on model predictive torque control(MPTC)strategy is developed for three-phase permanent magnet synchronous motor(PMSM)drive system without current sensor.In general,to achieve high-precision control,two-phase current sensors are necessary for successful implementation of MPTC.For this purpose,two ESOs are used to estimate q-axis current and stator resistance respectively,and then based on this,d-axis current is estimated.Moreover,to reduce torque and flux ripple and to improve the performance of the torque and speed,MPTC strategy is designed.The simulation results validate the feasibility and effectiveness of the proposed scheme.展开更多
Single voltage vectors applied in the conventional model predictive torque control(MPTC)for multiphase motors do not only suffer from serious torque and stator flux ripples but also cause the large harmonic current.To...Single voltage vectors applied in the conventional model predictive torque control(MPTC)for multiphase motors do not only suffer from serious torque and stator flux ripples but also cause the large harmonic current.To address the aforementioned challenges,an MPTC using a modified dual virtual vector modulation method is proposed to improve the operational performance of a dual three-phase permanent magnet synchronous motor.Virtual voltage vectors are synthesized as the candidate control set to restrain the harmonic current.A transformation method is introduced to consider both the stator flux and torque in the duty cycle modulation.The torque and stator flux ripples are simultaneously reduced by addressing the limitations of nonuniform units.Furthermore,the null voltage vector is then inserted to expand the modulation range and improve the steady-state performance.Moreover,the sawtooth carrier is adopted to address the challenge of the asymmetric switch sequence caused by the modified modulation.Finally,the experimental results are presented to verify the effectiveness and superiority of the proposed MPTC method.展开更多
For the two-level five-phase permanent magnet synchronous motor(FP-PMSM)drive system,an improved finite-control-set model predictive torque control(MPTC)strategy is adopted to reduce torque ripple and improve the cont...For the two-level five-phase permanent magnet synchronous motor(FP-PMSM)drive system,an improved finite-control-set model predictive torque control(MPTC)strategy is adopted to reduce torque ripple and improve the control performance of the system.The mathematical model of model reference adaptive system(MRAS)of FP-PMSM is derived and a method based on fractional order sliding mode(FOSM)is proposed to construct the model reference adaptive system(FOSMMRAS)to improve the motor speed estimation accuracy and eliminate the sliding mode integral saturation effect.The simulation results show that the FP-PMSM speed sensorless FCS-MPTC system based on FOSM-MRAS has strong robustness,good dynamic performance and static performance,and high reliability.展开更多
Aiming at the problem that the traditional control strategy of permanent magnet synchronous motor(PMSM)for electric vehicles has low control performance,a novel adaptive non-singular fast terminal sliding mode control...Aiming at the problem that the traditional control strategy of permanent magnet synchronous motor(PMSM)for electric vehicles has low control performance,a novel adaptive non-singular fast terminal sliding mode control(ANFTSMC)model predictive torque control(MPTC)strategy is proposed.A new adaptive exponential approach rate is designed,and the traditional switching function sgn()is replaced by the hyperbolic tangent function tanh().A new ANFTSMC with extended state observer(ESO)is constructed as the speed regulator of the system,and ESO can observe disturbances.This improved method weakens chattering and improves the robustness of the system.To realize sensorless control of the speed control system,an ESO speed observer based on tanh(Fal)is constructed.Compared with the traditional ESO based on Fal function,the observation error is smaller,and the observation accuracy is higher.Finally,aiming at the model predictive torque control strategy used,a new objective function construction method is proposed,which avoids the design of weight coefficient,and the traditional voltage vector selection method is improved and optimized,which reduces the calculation amount of the algorithm.展开更多
To address the problems of torque limit and controller saturation in the control of robot arm joint,an anti-windup control strategy is proposed for a humanoid robot arm,which is based on the integral state prediction ...To address the problems of torque limit and controller saturation in the control of robot arm joint,an anti-windup control strategy is proposed for a humanoid robot arm,which is based on the integral state prediction under the direct torque control system of brushless DC motor. First,the arm joint of the humanoid robot is modelled. Then the speed controller model and the influence of the initial value of the integral element on the system are analyzed. On the basis of the traditional antiwindup controller,an integral state estimator is set up. Under the condition of different load torques and the given speed,the integral steady-state value is estimated. Therefore the accumulation of the speed error terminates when the integrator reaches saturation. Then the predicted integral steady-state value is used as the initial value of the regulator to enter the linear region to make the system achieve the purpose of anti-windup. The simulation results demonstrate that the control strategy for the humanoid robot arm joint based on integral state prediction can play the role of anti-windup and suppress the overshoot of the system effectively. The system has a good dynamic performance.展开更多
The Insulated-Gate Bipolar Transistor(IGBT)module is the core of the three-level(3L)traction converter.In order to improve the lifetime of the traction converter,a model predictive torque control(MPTC)based on the jun...The Insulated-Gate Bipolar Transistor(IGBT)module is the core of the three-level(3L)traction converter.In order to improve the lifetime of the traction converter,a model predictive torque control(MPTC)based on the junction temperature constraint is proposed.Firstly,the optimization range is reduced by judging the sector where the reference voltage vector locates,as the traditional MPTC needs to be optimized 27 times in each sampling period,which requires a large amount of calculation.Secondly,by simplifying the calculation of IGBT power loss and dynamically constraining it in the cost function,the performance of optimal voltage vector balancing control can be balanced with power loss.Simulation results show that the proposed method reduces the junction temperature of the power semiconductor and prolongs the converter lifetime compared with the traditional strategy.展开更多
基金support from the Natural Science Foundation of China(Grant numbers:U23B6010 and 52122401).
文摘The digital twin,as the decision center of the automated drilling system,incorporates physical or data-driven models to predict the system response(rate of penetration,down-hole circulating pressure,drilling torques,etc.).Real-time drilling torque prediction aids in drilling parameter optimization,drill string stabilization,and comparing the discrepancy between observed signal and theoretical trend to detect down-hole anomalies.Due to their inability to handle huge amounts of time series data,current machine learning techniques are unsuitable for the online prediction of drilling torque.Therefore,a new way,the just-in-time learning(JITL)framework and local machine learning model,are proposed to solve the problem.The steps in this method are:(1)a specific metric is designed to measure the similarity between time series drilling data and scenarios to be predicted ahead of bit;(2)parts of drilling data are selected to train a local model for a specific prediction scenario separately;(3)the local machine learning model is used to predict drilling torque ahead of bit.Both the model data test results and the field data application results certify the advantages of the method over the traditional sliding window methods.Moreover,the proposed method has been proven to be effective in drilling parameter optimization and pipe sticking trend detection.Finally,we offer suggestions for the selection of local machine learning algorithms and real-time prediction with this approach based on the test results.
基金Supported by National Natural Science Foundation of China(Grant No.51475163)National Hi-tech Research and Development Program of China(Grant No.2012AA041803).
文摘A full-face rock tunnel boring machine(TBM)has two main parameters:cutterhead thrust and cutterhead torque.Cutterhead thrust can be predicted numerically using different methods,such as the rated thrust of the disc cutter,the indentation test of the disc cutter,a linear breaking experiment,or applying statistical models based on field data.However,cutterhead torque prediction still lacks the necessary and sufficient theoretical analysis and corresponding research.Consequently,we studied the rock-breaking characteristics of the disc cutter on the cutterhead and found a difference between the lateral friction resistance and the inner and outer rock breaking.We then proposed the cutterhead torque composition of the full-face rock tunnel boring machine,shoveling and transporting ballast torque,rolling resistance torque,lateral friction resistance torque,and disc cutter lateral imbalance torque,and established the corresponding theory.Through practical engineering ver-ification,we found that the relative error between the cutterhead torque value calculated by the theory and the actual value was only 0.51%.If the lateral friction resistance moment of the disc cutter rock breaking and the lateral imbalance moment of the disc cutter are not considered,the relative error between the calculated value of cutterhead torque and the actual value is 7.48%.Therefore,the accuracy of the established cutterhead torque theory is 6.97%higher than that of the disc cutter linear rolling crushing rock test theory.The research results provide an important reference for determining the cutterhead torque of a full-face rock tunnel boring machine.
基金financially supported by the National Natural Science Foundation of China (Grant Nos. 52074258, 41941018, and U21A20153)
文摘Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for realtime cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.
基金supported in part by National Natural Science Foundation(NNSF)of China(Nos.61803079,61890924,61991404)in part by Fundamental Research Funds for the Central Universities(No.N2108006)in part by Liaoning Revitalization Talents Program(No.XLYC1907087)。
文摘The development of vehicle-to-everything and cloud computing has brought new opportunities and challenges to the automobile industry.In this paper,a commuter vehicle demand torque prediction method based on historical vehicle speed information is proposed,which uses machine learning to predict and analyze vehicle demand torque.Firstly,the big data of vehicle driving is collected,and the driving data is cleaned and features extracted based on road information.Then,the vehicle longitudinal driving dynamics model is established.Next,the vehicle simulation simulator is established based on the longitudinal driving dynamics model of the vehicle,and the driving torque of the vehicle is obtained.Finally,the travel is divided into several accelerationcruise-deceleration road pairs for analysis,and the vehicle demand torque is predicted by BP neural network and Gaussian process regression.
基金co-supported by the National Natural Science Foundation of China,China(Nos.51576096 and 51906102)Qing Lan and 333 Project,the Fundamental Research Funds for the Central Universities,China(No.NT2019004)+3 种基金National Science and Technology Major Project China(No.2017-V-0004-0054)Research on the Basic Problem of Intelligent Aero-engine,China(No.2017-JCJQ-ZD-04721)China Postdoctoral Science Foundation Funded Project,China(No.2019M661835)Aeronautics Power Foundation,China(No.6141B09050385)。
文摘In order to compensate for the disturbance of wide variation in rotor demanded torque on power turbine speed and realize the fast response control of turboshaft engine during variable rotor speed,a cascade PID control method based on the acceleration estimator of gas turbine speed(Ngdot)and rotor predicted torque feedforward is proposed.Firstly,a two-speed Dual Clutch Transmission(DCT)model is applied in the integrated rotor/turboshaft engine system to achieve variable rotor speed.Then,an online estimation method of Ngdot based on the Linear Quadratic Gaussian with Loop Transfer Recovery(LQG/LTR)is proposed for power turbine speed cascade control.Finally,according to the cascade PID controller based on Ngdot estimator,a rotor demanded torque predicted method based on the Min-batch Gradient Descent-Neural Network(MGD-NN)is put forward to compromise the influence of rotor torque interference.The simulation results show that compared with cascade PID controller based on Ngdot estimator and the one combined with collective pitch feedforward control,the novel control method proposed can reduce the overshoot of power turbine speed by more than 20%,which possesses faster response,superior dynamic effect and satisfactory robustness performance.The control method proposed can realize the fast response control of turboshaft engine with variable rotor speed better.
基金funded by“The Pearl River Talent Recruitment Program”of Guangdong Province in 2019(Grant No.2019CX01G338)the Research Funding of Shantou University for New Faculty Member(Grant No.NTF19024-2019).
文摘An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.
基金supported in part by the National Natural Science Funds of China under Grants 5217071282 and 5210071275in part by China Postdoctoral Science Foundation under Grant 2020M683524+7 种基金in part by Nature Science Basic Research Plan in Shaanxi Province under Grant 2020JQ-631 and 2021JQ-477in part by State Key Laboratory of Electrical Insulation and Power Equipment under Grant EIPE20201in part by State Key Laboratory of Large Electric Drive System and Equipment Technology under Grant SKLLDJ012016006in part by Key Research and Development Project of ShaanXi Province under Grant 2019GY-060in part by Key Laboratory of Industrial Automation in ShaanXi Province under Grant SLGPT2019KF01-12in part by the Key R&D plan of Shaanxi Province under Grant 2021GY-282in part by Shaanxi Outstanding Youth Fund under Grant 2020JC-40in part by Key Laboratory of Power Electronic Devices and High Efficiency Power Conversion in Xi’an under Grant 2019219814SYS013CG035。
文摘Finite control set model predictive torque control(FCS-MPTC)has become increasingly prevalent for induction motors(IM)owing to its simple concept,easy incorporation of constraints and strong flexibility.In traditional FCS-MPTC speed controller design,a classical proportional integral(PI)controller is typically chosen to generate the torque reference.However,the PI controller is dependent on system parameters and sensitive to the load torque variation,which seriously affects control performance.In this paper,a model predictive torque control using sliding mode control(MPTC+SMC)for IM is proposed to enhance the robust performance of the drive system.First,the influence of the parameter mismatches for FCS-MPTC is analyzed.Second,the shortcomings of traditional PI controller are derived.Then,the proposed MPTC+SMC method is designed,and the MPTC+PI and MPTC+SMC are compared theoretically.Finally,experimental results demonstrate the correctness and effectiveness of the proposed MPTC+SMC.In comparison with MPTC+PI,MPTC+SMC has the better dynamic performance and stronger robust performance against parameter variations and load disturbance.
基金supported by the National Natural Science Foundation of China(61463025).
文摘A global fast terminal sliding mode(GFTSM)-based model predictive torque control(MPTC)strategy is developed for permanent magnet synchronous motor(PMSM)drive system with only one phase current sensor.Generally two phase-current sensors are indispensable for MPTC.In response to only one phase current sensor available and the change of stator resistance,a novel adaptive observer for estimating the remaining two phase currents and time-varying stator resistance is proposed to perform MPTC.Moreover,in view of the variation of system parameters and external disturbance,a new GFTSM-based speed regulator is synthesized to enhance the drive system robustness.In this paper,the GFTSM,based on sliding mode theory,employs the fast terminal sliding mode in both the reaching stage and the sliding stage.The resultant GFTSM-based MPTC PMSM drive system with single phase current sensor has excellent dynamical performance which is very close to the GFTSM-based MPTC PMSM drive system with two-phase current sensors.On the other hand,compared with proportional-integral(PI)-based and sliding mode(SM)-based MPTC PMSM drive systems,it possesses better dynamical response and stronger robustness as well as smaller total harmonic distortion(THD)index of three-phase stator currents in the presence of variation of load torque.The simulation results validate the feasibility and effectiveness of the proposed scheme.
基金supported in part by the National Key Laboratory of Electromagnetic Energy Foundation under Grant 614221722050501 and 61422172220503。
文摘Vector-controlled AC motor drives utilize pulse width modulation(PWM)to synthesize the desired output voltage of the voltage source inverter(VSI).In space vector PWM(SVPWM)techniques,the average realization of the space vector applying the volt-sec balance principle results in an instantaneous error voltage that generates high frequency torque ripple.It may lead to an increase in motor vibration and acoustic noise.This article presents a high frequency torque ripple prediction model based on stator flux ripple and proposes a targeted designed variable switching frequency PWM(VSFPWM)strategy to diminish high frequency torque ripple.The switching frequency is dynamically adjusted according to the peak value of the predicted stator flux ripple to mitigate high frequency torque ripple.In contrast to existing strategies,the strategy outlined in this article directly suppresses high frequency torque ripple,thus remaining unaffected by inaccurate motor parameters.Additionally,due to the introduction of the power factor angle,the proposed strategy can better adapt to the full speed range operating conditions of the motor.Detailed simulations and experiments are provided to validate the effectiveness of the proposed strategy.
基金National Natural Science Foundation of China(No.61463025)Program for Excellent Team of Scientific Research in Lanzhou Jiaotong University(No.201701)
文摘Employing matrix converter （MC） as driving mode, the strategy of model predictive torque control （MPTC） is proposed for three phase permanent magnet synchronous motor （PMSM） system. MC is applied instead of conventional AC DC AC converter to increase the power factor （PF） of the system input side. MPTC is used to select optimal voltage space vector to enable the system to have satisfactory torque and flux control effect. The resultant MPTC strategy not only makes the MC fed PMSM system operate reliably and have perfect control performance, but also makes the PF of the system input side be 1. Compared with direct torque control （DTC）, the proposed MPTC strategy guarantees that MC fed PMSM has better command following characteristics in the presence of variation of load torque and tracking reference speed. Simulation results verify the feasibility and effectiveness of the proposed strategy.
文摘Aiming at the torque and flux ripples in the direct torque control and the time-varying parameters for permanent magnet synchronous motor (PMSM), a model predictive direct torque control with online parameter estimation based on the extended Kalman filter for PMSM is designed. By predicting the errors of torque and flux based on the model and the current states of the system, the optimal voltage vector is selected to minimize the error of torque and flux. The stator resistance and inductance are estimated online via EKF to reduce the effect of model error and the current estimation can reduce the error caused by measurement noise. The stability of the EKF is proved in theory. The simulation experiment results show the method can estimate the motor parameters, reduce the torque, and flux ripples and improve the performance of direct torque control for permanent magnet synchronous motor (PMSM).
基金National Natural Science Foundation of China(No.61463025)Opening Foundation of Key Laboratory of Opto-technology and Intelligent Control(Lanzhou Jiaotong University),Ministry of Education(No.KFKT2018-8)
文摘A novel double extended state observer(DESO)based on model predictive torque control(MPTC)strategy is developed for three-phase permanent magnet synchronous motor(PMSM)drive system without current sensor.In general,to achieve high-precision control,two-phase current sensors are necessary for successful implementation of MPTC.For this purpose,two ESOs are used to estimate q-axis current and stator resistance respectively,and then based on this,d-axis current is estimated.Moreover,to reduce torque and flux ripple and to improve the performance of the torque and speed,MPTC strategy is designed.The simulation results validate the feasibility and effectiveness of the proposed scheme.
基金Supported by the National Natural Science Foundation of China under Grant 51977099the Natural Science Foundation of Jiangsu Province under Grant BK20191225the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Single voltage vectors applied in the conventional model predictive torque control(MPTC)for multiphase motors do not only suffer from serious torque and stator flux ripples but also cause the large harmonic current.To address the aforementioned challenges,an MPTC using a modified dual virtual vector modulation method is proposed to improve the operational performance of a dual three-phase permanent magnet synchronous motor.Virtual voltage vectors are synthesized as the candidate control set to restrain the harmonic current.A transformation method is introduced to consider both the stator flux and torque in the duty cycle modulation.The torque and stator flux ripples are simultaneously reduced by addressing the limitations of nonuniform units.Furthermore,the null voltage vector is then inserted to expand the modulation range and improve the steady-state performance.Moreover,the sawtooth carrier is adopted to address the challenge of the asymmetric switch sequence caused by the modified modulation.Finally,the experimental results are presented to verify the effectiveness and superiority of the proposed MPTC method.
基金National Natural Science Foundation of China(No.51867012)。
文摘For the two-level five-phase permanent magnet synchronous motor(FP-PMSM)drive system,an improved finite-control-set model predictive torque control(MPTC)strategy is adopted to reduce torque ripple and improve the control performance of the system.The mathematical model of model reference adaptive system(MRAS)of FP-PMSM is derived and a method based on fractional order sliding mode(FOSM)is proposed to construct the model reference adaptive system(FOSMMRAS)to improve the motor speed estimation accuracy and eliminate the sliding mode integral saturation effect.The simulation results show that the FP-PMSM speed sensorless FCS-MPTC system based on FOSM-MRAS has strong robustness,good dynamic performance and static performance,and high reliability.
基金Project of National Natural Science Foundation of China(No.61863023)。
文摘Aiming at the problem that the traditional control strategy of permanent magnet synchronous motor(PMSM)for electric vehicles has low control performance,a novel adaptive non-singular fast terminal sliding mode control(ANFTSMC)model predictive torque control(MPTC)strategy is proposed.A new adaptive exponential approach rate is designed,and the traditional switching function sgn()is replaced by the hyperbolic tangent function tanh().A new ANFTSMC with extended state observer(ESO)is constructed as the speed regulator of the system,and ESO can observe disturbances.This improved method weakens chattering and improves the robustness of the system.To realize sensorless control of the speed control system,an ESO speed observer based on tanh(Fal)is constructed.Compared with the traditional ESO based on Fal function,the observation error is smaller,and the observation accuracy is higher.Finally,aiming at the model predictive torque control strategy used,a new objective function construction method is proposed,which avoids the design of weight coefficient,and the traditional voltage vector selection method is improved and optimized,which reduces the calculation amount of the algorithm.
基金Supported by the National Natural Science Foundation of China(61175090,61703249)Shandong Provincial Natural Science Foundation,China(ZR2017MF045)
文摘To address the problems of torque limit and controller saturation in the control of robot arm joint,an anti-windup control strategy is proposed for a humanoid robot arm,which is based on the integral state prediction under the direct torque control system of brushless DC motor. First,the arm joint of the humanoid robot is modelled. Then the speed controller model and the influence of the initial value of the integral element on the system are analyzed. On the basis of the traditional antiwindup controller,an integral state estimator is set up. Under the condition of different load torques and the given speed,the integral steady-state value is estimated. Therefore the accumulation of the speed error terminates when the integrator reaches saturation. Then the predicted integral steady-state value is used as the initial value of the regulator to enter the linear region to make the system achieve the purpose of anti-windup. The simulation results demonstrate that the control strategy for the humanoid robot arm joint based on integral state prediction can play the role of anti-windup and suppress the overshoot of the system effectively. The system has a good dynamic performance.
基金supported by the following funding:Hunan Provincial Department of Education Outstanding Youth Project(Grant No.24B0025)Regional Joint Fund Project of Hunan Province(Grant No.2025JJ70036)National Natural Science Foundation of China(Grant No.52002409).
文摘The Insulated-Gate Bipolar Transistor(IGBT)module is the core of the three-level(3L)traction converter.In order to improve the lifetime of the traction converter,a model predictive torque control(MPTC)based on the junction temperature constraint is proposed.Firstly,the optimization range is reduced by judging the sector where the reference voltage vector locates,as the traditional MPTC needs to be optimized 27 times in each sampling period,which requires a large amount of calculation.Secondly,by simplifying the calculation of IGBT power loss and dynamically constraining it in the cost function,the performance of optimal voltage vector balancing control can be balanced with power loss.Simulation results show that the proposed method reduces the junction temperature of the power semiconductor and prolongs the converter lifetime compared with the traditional strategy.
