Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the s...Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.展开更多
In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to t...In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.展开更多
Finite-control-set model predictive control(FCSMPC)has advantages of multi-objective optimization and easy implementation.To reduce the computational burden and switching frequency,this article proposed a simplified M...Finite-control-set model predictive control(FCSMPC)has advantages of multi-objective optimization and easy implementation.To reduce the computational burden and switching frequency,this article proposed a simplified MPC for dual three-phase permanent magnet synchronous motor(DTPPMSM).The novelty of this method is the decomposition of prediction function and the switching optimization algorithm.Based on the decomposition of prediction function,the current increment vector is obtained,which is employed to select the optimal voltage vector and calculate the duty cycle.Then,the computation burden can be reduced and the current tracking performance can be maintained.Additionally,the switching optimization algorithm was proposed to optimize the voltage vector action sequence,which results in lower switching frequency.Hence,this control strategy can not only reduce the computation burden and switching frequency,but also maintain the steady-state and dynamic performance.The simulation and experimental results are presented to verify the feasibility of the proposed strategy.展开更多
A model predictive current control(MPCC)with adaptive-adjusting method of timescales for permanent magnet synchronous motors(PMSMs)is proposed in this paper to improve the dynamic response and prediction accuracy in t...A model predictive current control(MPCC)with adaptive-adjusting method of timescales for permanent magnet synchronous motors(PMSMs)is proposed in this paper to improve the dynamic response and prediction accuracy in transient-state,while lessening the computational burden and improving the control performance in steady-state.The timescale characteristics of different parts of MPCC,such as signal sampling,prediction calculation,control output,model error correction,are analyzed,and the algorithm architecture of MPCC with multi-timescale is proposed.The difference between reference and actual speed,and the change rate of actual speed are utilized to discriminate the transient state of speed change and load change,respectively.Adaptive-adjusting method of control period and prediction stepsize are illustrated in detail after operation condition discrimination.Experimental results of a PMSM are presented to validate the effectiveness of proposed MPCC.In addition,comparative evaluation of single-step MPCC with fixed timescale and proposed MPCC is conducted,which demonstrates the superiority of proposed control strategy.展开更多
For a permanent magnet synchronous motor(PMSM)model predictive current control(MPCC)system,when the speed loop adopts proportional-integral(PI)control,speed regulation is easily affected by motor parameters,resulting ...For a permanent magnet synchronous motor(PMSM)model predictive current control(MPCC)system,when the speed loop adopts proportional-integral(PI)control,speed regulation is easily affected by motor parameters,resulting in the inability to balance the system robustness and dynamic performance.A PMSM optimal control strategy combining linear active disturbance rejection control(LADRC)and two-vector MPCC(TV-MPCC)is proposed.Firstly,a mathematical model of a PMSM is presented,and the PMSM TV-MPCC model is developed in the synchronous rotation coordinate system.Secondly,a first-order LADRC controller composed of a linear extended state observer and linear state error feedback is designed to reduce the complexity of parameter tuning while linearly simplifying the traditional active disturbance rejection control(ADRC)structure.Finally,the conventional PI speed regulator in the motor speed control system is replaced by the designed LADRC controller.The simulation results show that the speed control system using LADRC can effectively deal with the changes in motor parameters and has better robustness and dynamic performance than PI control and similar methods.The system has a fast motor speed response,small overshoot,strong anti-interference,and no steady-state error,and the total harmonic distortion is reduced.展开更多
The modular multilevel converter(MMC)has become a promising topology for widespread power converter applications.However,an evident circulating current flowing between the phases will increase system losses and compli...The modular multilevel converter(MMC)has become a promising topology for widespread power converter applications.However,an evident circulating current flowing between the phases will increase system losses and complicate the heatsink design.This paper proposes a novel hybrid model predictive control method for MMCs.This method utilizes an indirect structure MPC and a sorting algorithm to implement current tracking and capacitor voltages balancing,considerably resulting in reduced calculation burden.In addition,different from the conventional MPC solutions,we add a simple proportional-integral(PI)controller to suppress circulating current through modifying the submodule(SM)inserted number,which is parallel to the MPC loop.This hybrid control solution combines both advantages of MPC and linear control,evidently resulting in improved performance of circulating current.Finally,the MATLAB/Simulink results of an 11-level MMC system verify the effectiveness of the proposed solution.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
Based on the fractional order theory and sliding mode control theory,a model prediction current control(MPCC)strategy based on fractional observer is proposed for the permanent magnet synchronous motor(PMSM)driven by ...Based on the fractional order theory and sliding mode control theory,a model prediction current control(MPCC)strategy based on fractional observer is proposed for the permanent magnet synchronous motor(PMSM)driven by three-level inverter.Compared with the traditional sliding mode speed observer,the observer is very simple and eases to implement.Moreover,the observer reduces the ripple of the motor speed in high frequency range in an efficient way.To reduce the stator current ripple and improve the control performance of the torque and speed,the MPCC strategy is put forward,which can make PMSM MPCC system have better control performance,stronger robustness and good dynamic performance.The simulation results validate the feasibility and effectiveness of the proposed scheme.展开更多
This paper presents an improved finite control set model predictive current control(FCS-MPCC)of a five-phase permanent magnet synchronous motor(PMSM).First,to avoid including all the 32 voltage vectors provided by a t...This paper presents an improved finite control set model predictive current control(FCS-MPCC)of a five-phase permanent magnet synchronous motor(PMSM).First,to avoid including all the 32 voltage vectors provided by a two-level five-phase inverter into the control set,virtual voltage vectors are adopted.As the third current harmonics can be much reduced by virtual voltage vectors automatically,the harmonic items in the cost function of conventional FCS-MPCC are not considered.Furthermore,an adaptive control set is proposed based on voltage prediction.Best control set with proper voltage vector amplitude corresponding to different rotor speed can be achieved by this method.Consequently,current ripples can be largely reduced and the system performs much better.At last,simulations are established to verify the steady and transient performance of the proposed FCS-MPCC,and experiments based on a 2 kW five-phase motor are carried out.The results have validated the performance improvement of the proposed control strategy.展开更多
The recent studies on Artificial Intelligence(AI)accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing...The recent studies on Artificial Intelligence(AI)accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adap-tiveness and fast responding features.The Model Predictive Control(MPC)tech-nique is a widely used,safe and reliable control method based on constraints.On the other hand,the Eddy Current dynamometers are highly nonlinear braking sys-tems whose performance parameters are related to many processes related vari-ables.This study is based on an adaptive model predictive control that utilizes selected AI methods.The presented approach presents an updated the mathema-tical model of an Eddy Current Dynamometer based on experimentally obtained system operational data.Finally,the comparison of AI methods and related learn-ing performances based on the assessment technique of mean absolute percentage error(MAPE)issues are discussed.The results indicate that Single Hidden Layer Neural Network(SHLNN),General Regression Neural Network(GRNN),Radial Basis Network(RBNN),Neuro Fuzzy Network(ANFIS)coupled MPC have quite satisfying performances.The presented results indicate that,amongst them,GRNN appears to provide the best performance.展开更多
Compared with the traditional three-phase star connection winding,the open-end winding permanent magnet synchronous motor(OW-PMSM)system with a common direct current(DC)bus has a zero-sequence circuit,which makes the ...Compared with the traditional three-phase star connection winding,the open-end winding permanent magnet synchronous motor(OW-PMSM)system with a common direct current(DC)bus has a zero-sequence circuit,which makes the common-mode voltage and the back electromotive force(EMF)harmonic generated by the inverters produce the zero-sequence current in the zero-sequence circuit,and the zero-sequence current has great influence on the operation efficiency and stability of the motor control system.A zero-sequence current suppression strategy is presented based on model predictive current control for OW-PMSM.Through the mathematical model of OW-PMSM to establish the predictive model and the zero-sequence circuit model,the common-mode voltage under different voltage vector combinations is fully considered during vector selection and action time calculation.Then zero-sequence loop constraints are established,so as to suppress the zero-sequence current.In the end,the control strategy proposed in this paper is verified by simulation experiments.展开更多
To improve the dynamic performance of conventional deadbeat predictive current control(DPCC)under parameter mismatch,especially eliminate the current overshoot and oscillation during torque mutation,it is necessary to...To improve the dynamic performance of conventional deadbeat predictive current control(DPCC)under parameter mismatch,especially eliminate the current overshoot and oscillation during torque mutation,it is necessary to enhance the robustness of DPCC against various working conditions.However,the disturbance from parameter mismatch can deteriorate the dynamic performance.To deal with the above problem,firstly,traditional DPCC and the parameter sensitivity of DPCC are introduced and analyzed.Secondly,an extended state observer(ESO)combined with DPCC method is proposed,which can observe and suppress the disturbance due to various parameter mismatch.Thirdly,to improve the accuracy and stability of ESO,an adaptive extended state observer(AESO)using fuzzy controller based on ESO,is presented,and combined with DPCC method.The improved DPCC-AESO can switch the value of gain coefficients with fuzzy control,accelerating the current response speed and avoid the overshoot and oscillation,which improves the robustness and stability performance of SPMSM.Finally,the three methods,as well as conventional DPCC method,DPCC-ESO method,DPCC-AESO method,are comparatively analyzed in this paper.The effectiveness of the proposed two methods are verified by simulation and experimental results.展开更多
The widely used cascade speed and torque controllers have a limited control performance in most high power applications due to the low switching frequency of power electronic converters and the convenience to avoid sp...The widely used cascade speed and torque controllers have a limited control performance in most high power applications due to the low switching frequency of power electronic converters and the convenience to avoid speed overshoots and oscillations for lifetime considerations. Model Predictive Direct Current Control （MPDCC） leads to an increase of torque control performance taking into account the discrete nature of inverters but temporary offsets and poor responses to load torque variations are still issues in speed control. A load torque estimator is proposed in this paper in order to further improve dynamic behavior. It compensates the load torque influence on the speed control setting a feed forward torque reference value. The benefits are twice; the speed controller reaches the speed reference value without offsets which would need to be compensated by an integrator and a better response to load torque variations is obtained since they are detected and compensated leading to small speed variations. Moreover, the influence of pararneter errors and disturbances has been analyzed and limited so that they play a minor role in operation.展开更多
In this paper, a new predictive control strategy for current source matrix converter (CSMC) is presented. Proposed predictive control strategy allows for creating output voltages with boost type voltage transfer ratio...In this paper, a new predictive control strategy for current source matrix converter (CSMC) is presented. Proposed predictive control strategy allows for creating output voltages with boost type voltage transfer ratio and desired frequency. The description of predictive control circuit of the CSMC is presented. Furthermore the simulation test results to confirm functionality of the proposed control strategy and converter properties under this strategy are shown.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
In this paper, model-predictive control(MPC) is proposed for controlling power source of accelerators. The system state equation is employed as the predictive model. With MPC, the difference between possible output an...In this paper, model-predictive control(MPC) is proposed for controlling power source of accelerators. The system state equation is employed as the predictive model. With MPC, the difference between possible output and the ideal output is forecasted and decreased, so that the system can trace the ideal trail as closely and quickly as possible. The results of simulations and experiments show that this method can reduce influence of low frequency noise.展开更多
For a type of high⁃order discrete⁃time nonlinear systems(HDNS)whose system models are undefined,a model⁃free predictive control(MFPC)algorithm is proposed in this paper.At first,an estimation model is given by the imp...For a type of high⁃order discrete⁃time nonlinear systems(HDNS)whose system models are undefined,a model⁃free predictive control(MFPC)algorithm is proposed in this paper.At first,an estimation model is given by the improved projection algorithm to approach the controlled nonlinear system.Then,on the basis of the estimation model,a predictive controller is designed by solving the finite time domain rolling optimization quadratic function,and the controller’s explicit analytic solution is also obtained.Furthermore,the closed⁃loop system's stability can be ensured.Finally,the results of simulation reveal that the presented control strategy has a faster convergence speed as well as more stable dynamic property compared with the model⁃free sliding mode control(MFSC).展开更多
An error back propagation （BP） neural network prediction model was established for the shunt current compensation in series resistance spot welding. The input variables for the neural network consist of the resistiv...An error back propagation （BP） neural network prediction model was established for the shunt current compensation in series resistance spot welding. The input variables for the neural network consist of the resistivity of the material, the thickness of workpiece and the spot spacing, and the shunt rate is outputted. A simplified calculation for the shunt rate was presented based on the feature of the constant-current resistance spot welding and the variation of the resistance in resistance spot welding process, and then the data generated by simplified calculation were used to train and adjust the neural network model. The neural network model proposed was used to predict the shunt rate in the spot welding of 20# mlid steel （in Chinese classification） （in 2. 0 mm thickness） and 10# mild steel （in 1.5 mm and 1.0 mm thickness）. The maximum relative prediction errors are, respectively, 2. 83%, 1.77% and 3.67%. Shunt current compensation experiments were peoCormed based on the neural network prediction model proposed to check the diameter difference of nuggets. Experimental results show that maximum nugget diameter deviation is less than 4% for both 10# and 20# mlid steels with spot spacing of 30 mm and 50 mm.展开更多
Performance of a three-phase shunt active power filter(SAPF)relies on the capability of the controller to track the reference current.Therefore,designing an accurate current controller is crucial to guarantee satisfac...Performance of a three-phase shunt active power filter(SAPF)relies on the capability of the controller to track the reference current.Therefore,designing an accurate current controller is crucial to guarantee satisfactory SAPF operation.This paper presents a model predictive current controller(MPCC)for a low-cost,four-switch,shunt active power filter for power quality improvement.A four-switch,B4,converter topology is adopted as an SAPF,hence offering a simple,robust,and low-cost solution.In addition,to further reduce overall cost,only two interfacing filter inductors,instead of three,are used to eliminate switching current ripple.The proposed SAPF model MPCC is detailed for implementation,where simulation and experimental results validate effectiveness of the proposed control algorithm showing a 20%improvement in total harmonic distortion compared with a conventional hysteresis band current controller.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 52077002。
文摘Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.
基金supported in part by the National Natural Science Foundation of China(62403396,62433018,62373113)the Guangdong Basic and Applied Basic Research Foundation(2023A1515011527,2023B1515120010)the Postdoctoral Fellowship Program of CPSF(GZB20240621)
文摘In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.
基金supported by the National Natural Science Foundation of China under Grant 5227705。
文摘Finite-control-set model predictive control(FCSMPC)has advantages of multi-objective optimization and easy implementation.To reduce the computational burden and switching frequency,this article proposed a simplified MPC for dual three-phase permanent magnet synchronous motor(DTPPMSM).The novelty of this method is the decomposition of prediction function and the switching optimization algorithm.Based on the decomposition of prediction function,the current increment vector is obtained,which is employed to select the optimal voltage vector and calculate the duty cycle.Then,the computation burden can be reduced and the current tracking performance can be maintained.Additionally,the switching optimization algorithm was proposed to optimize the voltage vector action sequence,which results in lower switching frequency.Hence,this control strategy can not only reduce the computation burden and switching frequency,but also maintain the steady-state and dynamic performance.The simulation and experimental results are presented to verify the feasibility of the proposed strategy.
基金supported in part by the National Natural Science Foundation of China under Grant 52077054in part by the Natural Science Foundation of Hebei Province under Grant E2019202092+2 种基金in part by the China Postdoctoral Science Foundation under Grant 2021T140077 and 2020M681446in part by the State Key Laboratory of Reliability and Intelligence of Electrical Equipment under Grant EERI_PI2020002in part by the Funds for Creative Research Groups of Hebei Province under Grant E2020202142.
文摘A model predictive current control(MPCC)with adaptive-adjusting method of timescales for permanent magnet synchronous motors(PMSMs)is proposed in this paper to improve the dynamic response and prediction accuracy in transient-state,while lessening the computational burden and improving the control performance in steady-state.The timescale characteristics of different parts of MPCC,such as signal sampling,prediction calculation,control output,model error correction,are analyzed,and the algorithm architecture of MPCC with multi-timescale is proposed.The difference between reference and actual speed,and the change rate of actual speed are utilized to discriminate the transient state of speed change and load change,respectively.Adaptive-adjusting method of control period and prediction stepsize are illustrated in detail after operation condition discrimination.Experimental results of a PMSM are presented to validate the effectiveness of proposed MPCC.In addition,comparative evaluation of single-step MPCC with fixed timescale and proposed MPCC is conducted,which demonstrates the superiority of proposed control strategy.
文摘For a permanent magnet synchronous motor(PMSM)model predictive current control(MPCC)system,when the speed loop adopts proportional-integral(PI)control,speed regulation is easily affected by motor parameters,resulting in the inability to balance the system robustness and dynamic performance.A PMSM optimal control strategy combining linear active disturbance rejection control(LADRC)and two-vector MPCC(TV-MPCC)is proposed.Firstly,a mathematical model of a PMSM is presented,and the PMSM TV-MPCC model is developed in the synchronous rotation coordinate system.Secondly,a first-order LADRC controller composed of a linear extended state observer and linear state error feedback is designed to reduce the complexity of parameter tuning while linearly simplifying the traditional active disturbance rejection control(ADRC)structure.Finally,the conventional PI speed regulator in the motor speed control system is replaced by the designed LADRC controller.The simulation results show that the speed control system using LADRC can effectively deal with the changes in motor parameters and has better robustness and dynamic performance than PI control and similar methods.The system has a fast motor speed response,small overshoot,strong anti-interference,and no steady-state error,and the total harmonic distortion is reduced.
基金This work was partially supported by the National Natural Science Foundation of China(11847104)General Program of National Natural Science Foundation of China(51977124)+2 种基金Shandong Natural Science Foundation(ZR2019QEE001)Natural Science Foundation of Jiangsu Province(BK20190204)National Distinguished Expert(Youth Talent)Program of China(31390089963058)。
文摘The modular multilevel converter(MMC)has become a promising topology for widespread power converter applications.However,an evident circulating current flowing between the phases will increase system losses and complicate the heatsink design.This paper proposes a novel hybrid model predictive control method for MMCs.This method utilizes an indirect structure MPC and a sorting algorithm to implement current tracking and capacitor voltages balancing,considerably resulting in reduced calculation burden.In addition,different from the conventional MPC solutions,we add a simple proportional-integral(PI)controller to suppress circulating current through modifying the submodule(SM)inserted number,which is parallel to the MPC loop.This hybrid control solution combines both advantages of MPC and linear control,evidently resulting in improved performance of circulating current.Finally,the MATLAB/Simulink results of an 11-level MMC system verify the effectiveness of the proposed solution.
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Based on the fractional order theory and sliding mode control theory,a model prediction current control(MPCC)strategy based on fractional observer is proposed for the permanent magnet synchronous motor(PMSM)driven by three-level inverter.Compared with the traditional sliding mode speed observer,the observer is very simple and eases to implement.Moreover,the observer reduces the ripple of the motor speed in high frequency range in an efficient way.To reduce the stator current ripple and improve the control performance of the torque and speed,the MPCC strategy is put forward,which can make PMSM MPCC system have better control performance,stronger robustness and good dynamic performance.The simulation results validate the feasibility and effectiveness of the proposed scheme.
基金This work was supported in part by the National Natural Science Foundation of China under 61374125。
文摘This paper presents an improved finite control set model predictive current control(FCS-MPCC)of a five-phase permanent magnet synchronous motor(PMSM).First,to avoid including all the 32 voltage vectors provided by a two-level five-phase inverter into the control set,virtual voltage vectors are adopted.As the third current harmonics can be much reduced by virtual voltage vectors automatically,the harmonic items in the cost function of conventional FCS-MPCC are not considered.Furthermore,an adaptive control set is proposed based on voltage prediction.Best control set with proper voltage vector amplitude corresponding to different rotor speed can be achieved by this method.Consequently,current ripples can be largely reduced and the system performs much better.At last,simulations are established to verify the steady and transient performance of the proposed FCS-MPCC,and experiments based on a 2 kW five-phase motor are carried out.The results have validated the performance improvement of the proposed control strategy.
文摘The recent studies on Artificial Intelligence(AI)accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adap-tiveness and fast responding features.The Model Predictive Control(MPC)tech-nique is a widely used,safe and reliable control method based on constraints.On the other hand,the Eddy Current dynamometers are highly nonlinear braking sys-tems whose performance parameters are related to many processes related vari-ables.This study is based on an adaptive model predictive control that utilizes selected AI methods.The presented approach presents an updated the mathema-tical model of an Eddy Current Dynamometer based on experimentally obtained system operational data.Finally,the comparison of AI methods and related learn-ing performances based on the assessment technique of mean absolute percentage error(MAPE)issues are discussed.The results indicate that Single Hidden Layer Neural Network(SHLNN),General Regression Neural Network(GRNN),Radial Basis Network(RBNN),Neuro Fuzzy Network(ANFIS)coupled MPC have quite satisfying performances.The presented results indicate that,amongst them,GRNN appears to provide the best performance.
基金Fundamental Research Funds for the Central Universities,China(No.2232019D3-53)Initial Research Funds for Young Teachers of Donghua University,China(104070053029)Shanghai Rising-Star Program,China(No.19QA1400400)。
文摘Compared with the traditional three-phase star connection winding,the open-end winding permanent magnet synchronous motor(OW-PMSM)system with a common direct current(DC)bus has a zero-sequence circuit,which makes the common-mode voltage and the back electromotive force(EMF)harmonic generated by the inverters produce the zero-sequence current in the zero-sequence circuit,and the zero-sequence current has great influence on the operation efficiency and stability of the motor control system.A zero-sequence current suppression strategy is presented based on model predictive current control for OW-PMSM.Through the mathematical model of OW-PMSM to establish the predictive model and the zero-sequence circuit model,the common-mode voltage under different voltage vector combinations is fully considered during vector selection and action time calculation.Then zero-sequence loop constraints are established,so as to suppress the zero-sequence current.In the end,the control strategy proposed in this paper is verified by simulation experiments.
基金supported by the National Natural Science Foundation of China(No.52005037).
文摘To improve the dynamic performance of conventional deadbeat predictive current control(DPCC)under parameter mismatch,especially eliminate the current overshoot and oscillation during torque mutation,it is necessary to enhance the robustness of DPCC against various working conditions.However,the disturbance from parameter mismatch can deteriorate the dynamic performance.To deal with the above problem,firstly,traditional DPCC and the parameter sensitivity of DPCC are introduced and analyzed.Secondly,an extended state observer(ESO)combined with DPCC method is proposed,which can observe and suppress the disturbance due to various parameter mismatch.Thirdly,to improve the accuracy and stability of ESO,an adaptive extended state observer(AESO)using fuzzy controller based on ESO,is presented,and combined with DPCC method.The improved DPCC-AESO can switch the value of gain coefficients with fuzzy control,accelerating the current response speed and avoid the overshoot and oscillation,which improves the robustness and stability performance of SPMSM.Finally,the three methods,as well as conventional DPCC method,DPCC-ESO method,DPCC-AESO method,are comparatively analyzed in this paper.The effectiveness of the proposed two methods are verified by simulation and experimental results.
文摘The widely used cascade speed and torque controllers have a limited control performance in most high power applications due to the low switching frequency of power electronic converters and the convenience to avoid speed overshoots and oscillations for lifetime considerations. Model Predictive Direct Current Control （MPDCC） leads to an increase of torque control performance taking into account the discrete nature of inverters but temporary offsets and poor responses to load torque variations are still issues in speed control. A load torque estimator is proposed in this paper in order to further improve dynamic behavior. It compensates the load torque influence on the speed control setting a feed forward torque reference value. The benefits are twice; the speed controller reaches the speed reference value without offsets which would need to be compensated by an integrator and a better response to load torque variations is obtained since they are detected and compensated leading to small speed variations. Moreover, the influence of pararneter errors and disturbances has been analyzed and limited so that they play a minor role in operation.
文摘In this paper, a new predictive control strategy for current source matrix converter (CSMC) is presented. Proposed predictive control strategy allows for creating output voltages with boost type voltage transfer ratio and desired frequency. The description of predictive control circuit of the CSMC is presented. Furthermore the simulation test results to confirm functionality of the proposed control strategy and converter properties under this strategy are shown.
基金supported by National Natural Science Foundation of China(61533013,61273144)Scientific Technology Research and Development Plan Project of Tangshan(13130298B)Scientific Technology Research and Development Plan Project of Hebei(z2014070)
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 National Natural Science Foundation of China(No.11027508)
文摘In this paper, model-predictive control(MPC) is proposed for controlling power source of accelerators. The system state equation is employed as the predictive model. With MPC, the difference between possible output and the ideal output is forecasted and decreased, so that the system can trace the ideal trail as closely and quickly as possible. The results of simulations and experiments show that this method can reduce influence of low frequency noise.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61803224)the Natural Science Foundation of Shandong Province(Grant No.ZR2019QF005).
文摘For a type of high⁃order discrete⁃time nonlinear systems(HDNS)whose system models are undefined,a model⁃free predictive control(MFPC)algorithm is proposed in this paper.At first,an estimation model is given by the improved projection algorithm to approach the controlled nonlinear system.Then,on the basis of the estimation model,a predictive controller is designed by solving the finite time domain rolling optimization quadratic function,and the controller’s explicit analytic solution is also obtained.Furthermore,the closed⁃loop system's stability can be ensured.Finally,the results of simulation reveal that the presented control strategy has a faster convergence speed as well as more stable dynamic property compared with the model⁃free sliding mode control(MFSC).
基金Acknowledgements The authors would like to thank for the financial support from the National Natural Science Foundation of China through document 51275418. The authors would also like to acknowledge professor Yang Siqian for providing discussion of the results for this study.
文摘An error back propagation （BP） neural network prediction model was established for the shunt current compensation in series resistance spot welding. The input variables for the neural network consist of the resistivity of the material, the thickness of workpiece and the spot spacing, and the shunt rate is outputted. A simplified calculation for the shunt rate was presented based on the feature of the constant-current resistance spot welding and the variation of the resistance in resistance spot welding process, and then the data generated by simplified calculation were used to train and adjust the neural network model. The neural network model proposed was used to predict the shunt rate in the spot welding of 20# mlid steel （in Chinese classification） （in 2. 0 mm thickness） and 10# mild steel （in 1.5 mm and 1.0 mm thickness）. The maximum relative prediction errors are, respectively, 2. 83%, 1.77% and 3.67%. Shunt current compensation experiments were peoCormed based on the neural network prediction model proposed to check the diameter difference of nuggets. Experimental results show that maximum nugget diameter deviation is less than 4% for both 10# and 20# mlid steels with spot spacing of 30 mm and 50 mm.
文摘Performance of a three-phase shunt active power filter(SAPF)relies on the capability of the controller to track the reference current.Therefore,designing an accurate current controller is crucial to guarantee satisfactory SAPF operation.This paper presents a model predictive current controller(MPCC)for a low-cost,four-switch,shunt active power filter for power quality improvement.A four-switch,B4,converter topology is adopted as an SAPF,hence offering a simple,robust,and low-cost solution.In addition,to further reduce overall cost,only two interfacing filter inductors,instead of three,are used to eliminate switching current ripple.The proposed SAPF model MPCC is detailed for implementation,where simulation and experimental results validate effectiveness of the proposed control algorithm showing a 20%improvement in total harmonic distortion compared with a conventional hysteresis band current controller.
