Simplified Model Predictive Current Control for Dual Three-phase PMSM with Low Computation Burden and Switching Frequency

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.

Model Predictive Current Control with Adaptive-adjusting Timescales for PMSMs

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.

Predictive current control system of PMSM based on LADRC

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.

Hybrid Predictive Control with Simple Linear Control Based Circulating Current Suppression for Modular Multilevel Converters

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.

Model predictive current control for PMSM driven by three-level inverter based on fractional sliding mode speed observer

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.

Finite Control Set Model Predictive Current Control of a Five-Phase PMSM with Virtual Voltage Vectors and Adaptive Control Set

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.

Model Predictive Control Coupled with Artificial Intelligence for Eddy Current Dynamometers

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.

Zero-Sequence Current Suppression Strategy for Open-End Winding Permanent Magnet Synchronous Motor Based on Model Predictive Control

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.

An Improved Deadbeat Predictive Current Control Method for SPMSM Drives with a Novel Adaptive Disturbance Observer

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.

Load Torque Compensator for Model Predictive Direct Current Control in High Power PMSM Drive Systems

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.

Model Predictive Control Circuit of the Current Source Matrix Converter

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.

Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

Reliable load frequency control LFC is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-Area interconnected power system with wind turbines, this paper presents a distributed model predictive control DMPC based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints GRCs, load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed-loop performance, and computational burden with the physical constraints. © 2014 Chinese Association of Automation.

Predictive cruise control for heavy trucks based on slope information under cloud control system

With the advantage of fast calculation and map resources on cloud control system(CCS), cloud-based predictive cruise control(CPCC) for heavy trucks has great potential to improve energy efficiency, which is significant to achieve the goal of national carbon neutrality. However, most investigations focus on the on-board predictive cruise control(PCC) system,lack of research on CPCC architecture under CCS. Besides, the current PCC algorithms have the problems of a single control target and high computational complexity, which hinders the improvement of the control effect. In this paper, a layered architecture based on CCS is proposed to effectively address the realtime computing of CPCC system and the deployment of its algorithm on vehicle-cloud. In addition, based on the dynamic programming principle and the proposed road point segmentation method(RPSM), a PCC algorithm is designed to optimize the speed and gear of heavy trucks with slope information. Simulation results show that the CPCC system can adaptively control vehicle driving through the slope prediction, with fuel-saving rate of 6.17% in comparison with the constant cruise control. Also,compared with other similar algorithms, the PCC algorithm can make the engine operate more in the efficient zone by cooperatively optimizing the gear and speed. Moreover, the RPSM algorithm can reconfigure the road in advance, with a 91% roadpoint reduction rate, significantly reducing algorithm complexity.Therefore, this study has essential research significance for the economic driving of heavy trucks and the promotion of the CPCC system.

Simulation on model predictive control for PMSM drive system based on double extended state observer

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.

Model-predictive control of power supply for particle accelerators

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.

基于MPCC的PMSM电流传感器故障诊断与容错控制

针对电流传感器在永磁同步电机系统运行时发生的故障,提出一种基于模型预测电流控制的电流传感器故障诊断与容错控制方案。控制器依次遍历8种电压矢量,得出下一周期最优预测电流,并与下一周期的实测电流比较,以实现快速诊断。利用预测相电流代替故障相电流实现故障后的容错控制。仿真验证了系统在电流传感器突发故障时诊断与容错方法的有效性与可行性。

基于ESM-REKF的直线电机无差拍预测电流控制

永磁同步直线电机(PMSLM)无差拍预测电流控制(DPCC)在实际系统中受采样延时和电机参数失配的影响,电流跟踪精度下降,从而引起推力波动,为此提出一种基于扩张状态建模(ESM)和抗差扩展卡尔曼滤波(REKF)的无差拍预测电流控制策略。首先,对PMSLM电气子系统的扰动进行分析,借鉴扩张状态观测器的思想,将扰动作为一个高阶积分器以构建ESM;其次,结合ESM设计包含参数变化扰动估计的EKF,在此基础上,为了抑制粗差对状态估计的影响,设计抗差误差协方差阵,从而得到ESM-REKF的扰动估计方法;最后,利用电流预测值和扰动估计值对DPCC进行改进与补偿。仿真和实验结果表明,相对传统DPCC,所提控制策略在标称值下,电流谐波畸变率降低了约5.38%;在电感参数失配下,电流谐波畸变率降低了约9.38%,电磁推力的超调下降了约7%,明显削减采样延时和参数失配对电流预测精度的影响,实现对电流快速准确地跟踪,有效抑制推力波动。

基于负载观测器的PMSM预测电流控制

针对永磁同步电机(permanent magnet synchronous motor,PMSM)预测电流控制系统在线优化计算负担大和抗负载扰动性能不足的问题,提出了一种基于负载转矩观测器的PMSM预测电流控制策略。首先,基于离散空间矢量调制构建虚拟电压矢量,并利用无差拍的思想对备选电压矢量控制集进行优化,降低电压矢量寻优的计算负担。然后,为了提高系统的抗负载扰动能力,设计降阶观测器对负载转矩进行实时观测,并将负载转矩观测值按比例前馈至电流环,补偿由负载转矩变化引起的电流波动。仿真结果表明,所提电压矢量寻优策略在保证控制效果的同时有效降低了计算负担,降阶观测器的引入明显提高了系统的抗负载扰动能力。

基于矢量分类的NPC型三电平双三相PMSM的改进模型预测电压控制

多电平多相电机驱动系统存在电压矢量数目多、冗余复杂及多目标非线性约束等问题。以二极管钳位型三电平六相逆变器驱动双三相永磁同步电机为研究对象,以电机谐波电流抑制和逆变器直流侧中点电位平衡控制为目标,提出一种基于电压矢量三角区域分类的改进模型预测电压控制策略。首先,以基波子平面大投影幅值为原则,对729个电压矢量进行初筛;接着,基于三角区域分类方法对筛选后的电压矢量进行分类,并对期望电压进行所属三角区域定位、确定候选矢量。设计满足电机定子电流谐波抑制及逆变器中点电位平衡控制的目标函数;根据电机数学模型和逆变器特征研究多目标控制权重因子的理论设计方法。不同工况下的有效性及对比实验表明,所研究的控制策略不仅能改善多电平多相电机驱动系统的多目标控制性能,还能有效降低控制器计算负担。