Cascaded Model Predictive Control of Six-phase Permanent Magnet Synchronous Motor with Fault Tolerant Ability

In the field of high-power electric drives, multiphase motors have the advantages of high power-density, excellent fault tolerance and control flexibility. But their decoupling control and modulation process are much more complicated compared with three-phase motors due to the increased degree of freedom. Finite control set model predictive control can reduce the difficulties of controlling six-phase motors because it does not require modulation process. In this paper, a cascaded model predictive control strategy is proposed for the optimal control of high-power six-phase permanent magnet synchronous motors. Firstly, the current prediction model of torque and harmonic subspaces are established by decoupling the six-phase spatial variables. Secondly, a cascaded cost function with fault-tolerant capability is proposed to eliminate the weighting factor in the cost function. And finally, the proposed strategy is demonstrated through theoretical analysis and experiments. It is validated that the proposed method is able to maintain excellent steady-state control accuracy and fast dynamic response while significantly reduce the control complexity of the system. Besides, it can easily achieve fault-tolerant operation under open-phase fault.

Novel high-safety aeroengine performance predictive control method based on adaptive tracking weight

Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation aeroengine.The latest research demonstrates that Subspace-based Improved Model Predictive Control(SIMPC)can overcome the difficulty in solving the predictive model in MPC/NMPC applications.However,applying constant design parameters cannot maintain consistent control effects in all states.Meanwhile,the designed system relies too much on sensor-measured data,and thus it is difficult to thoroughly validate the safety of the system because of its high complexity.This means that any potential hardware/software faults will endanger the engine.Therefore,this paper first presents a novel nonlinear mapping relationship to adaptively tune the tracking weight online with the change of Power Lever Angle(PLA)and real-time relative tracking error.Thus,without introducing additional design parameters,an Adaptive Tracking Weight-based SIMPC(ATW-SIMPC)controller is designed to improve the control performance in all operating states effectively.Then,a Primary/Backup Hybrid Control(PBHC)strategy with the ATW-SIMPC controller as the primary system and the traditional speed(Nf)controller as the backup system is proposed to ensure safety.The designed affiliated switching controller and the real-time monitor therein can be used to realize reasonable and smooth switching between primary/backup systems,so as to avoid bump transition.The PBHC system switches to the Nf controller when the ATW-SIMPC controller is wrong because of potential hardware/software faults;otherwise,the ATW-SIMPC controller keeps acting on the engine.The main results prove that the ATW-SIMPC controller with the optimal nonlinear mapping relationship,compared with the existing SIMPC controller,uplifts the dynamic control performance by 32%and reduces overshoots to an allowable limit,resulting in a better control effect in full state.The comparison results consistently indicate that the PBHC can guarantee engine safety in occurrence of hardware/software faults,such as sensor/onboard adaptive model faults.The approach proposed is applicable to the design of a model-based engine intelligent control system.

Low-complexity model predictive control of a four-level active neutral point clamped inverter without weighting factors

The four-level active neutral point clamped(ANPC)inverter has become increasingly widely used in the renewable energy indus-try since it offers one more voltage level without increasing the total number of active switches compared to the three-level ANPC inverter.The model predictive current control(MPCC)is a promising control method for multi-level inverters.However,the conven-tional MPCC suffers from high computational complexity and tedious weighting factor tuning in multi-level inverter applications.A low-complexity MPCC without weighting factors for a four-level ANPC inverter is proposed in this paper.The computational burden and voltage vector candidate set are reduced according to the relationship between voltage vector and neutral point voltage balance.The proposed MPCC shows excellent steady-state and dynamics performances while ensuring the neutral point voltage balancing.The efficacy of the proposed MPCC is verified by simulation and experimental results.

Multivariable PI Type Generalized Predictive Control

This paper presents a multivariable generalized predictive controller with proportion and integration structure by modifying the quadratic criterion of the usual MGPC. The control performance has been improved greatly. The effectiveness of the controller is demonstrated by the simulation result.

Adaptive predictive functional control based on Takagi-Sugeno model and its application to pH process

In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.

Model predictive flux control of permanent magnet synchronous motor driven by three-level inverter based on fine-division strategy

Aiming at the difficulty of setting the weight coefficient in the value function of model predictive torque control(MPTC)for permanent magnet synchronous motor(PMSM)driven by three-level inverter,a fine-division model predictive flux control(MPFC)method is proposed.First,establish a mathematical model between the motor torque and the stator flux linkage according to the mathematical equations of PMSM.Thus,the control of the motor torque and stator flux linkage in the MPTC is transformed into the control of a single stator flux linkage vector,omitting the cumbersome weight setting process in the traditional MPTC.The midpoint potential control strategy is proposed,which uses the characteristics of redundant small vectors to balance the midpoint potential.After that,a fine-division strategy is proposed,which effectively reduces the number of candidate vectors and the computational burden of the system.Finally,the proposed MPFC is compared with MPTC by simulation.The results show that the proposed fine-division MPFC effectively reduces the system calculation,and has the advantages of simple principle and better dynamic and steady-state control performance.The feasibility of the control strategy is verified.

Icing tolerance envelope protection based on variable-weighted multiple-model predictive control

Multiple-model predictive control(MMPC) is a fundamental icing tolerance envelope protection(ITEP) design method that can systematically handle nonlinear and time-varying constraints. However, few studies have addressed the envelope protection failure that results from the inaccurate prediction of multiple linear predictive models when actual conditions deviate from design conditions. In this study, weights that vary with icing conditions and flight parameters are considered to develop an effective and reliable envelope protection control strategy. First, an ITEP structure based on variable-weighted MMPC was implemented to improve the protection performance with condition departure information. Then, a variable-weighted rule was proposed to guarantee the stability of variable-weighted MMPC. A design approach involving a variable-weighted function that uses icing conditions and flight parameters as arguments was also developed with the proposed rules. Finally, a systematic ITEP design method on variable-weighted MMPC was constructed with additional design criteria for other normal control parameters.Simulations were conducted, and the results show that the proposed method can effectively enhance ITEP performance.

计及SOC自恢复的混合储能平抑风电功率波动控制

混合储能系统能够较好地应对复杂的风电波动,有效地提高电网的稳定性和安全性。在混合储能平抑风电功率波动的典型应用场景下,该文首先提出一种计及荷电状态(SOC)自恢复的混合储能平抑风电功率波动控制方法,在满足风电平抑需求的情况下,通过模型预测控制快速调节储能在平抑功率过程中的荷电状态,提高储能持续稳定运行能力;然后,为提高混合储能系统协调运行能力,设计了加权滑动平均(WMA)-模糊控制策略对超级电容和蓄电池功率进行动态分配;最后,结合实际风电功率数据,通过仿真验证了所提策略能有效平衡储能寿命和平抑风电波动的矛盾,能充分考虑两种储能设备的特性差异并提高功率分配的合理性。

采用改进MPC提高储能系统电压暂态特性研究

储能系统通过换流器实现并网运行,常规换流器因为其开关频率低,在采样、计算环节存在延时导致储能系统暂态特性差,甚至引起整个电网不稳定。本文采用模型预测控制MPC(model predictive control)来实现储能系统功率快速响应,避免延时影响。引入功率权重价值函数在有功、无功MPC控制中,计算储能换流器输出电压最优值。针对因为滤波电感参数偏移导致的MPC模型不准确问题,本文采用电感误差补偿控制来提高模型精度。通过Matlab/Simulink仿真和实验来验证本文所提方案可提高储能系统暂态特性,有效消除误差对MPC控制性能的影响。

基于多策略麻雀搜索算法的径向基神经网络时延预测方法

网络诱导时延序列存在随机性和不稳定性,单一的预测算法难以准确预测,针对该问题,提出一种基于多策略麻雀搜索算法(ISSA)的径向基神经网络(RBF)时延预测方法来准确预测网络时延。首先,为了应对麻雀种群初始分布不均匀的问题,增加改进Tent混沌映射来提高种群早期的分布质量,并引入新的惯性权重因子改进发现者位置更新策略,扩大麻雀早期寻优范围;随后引入正余弦优化算法(SCA)更新跟随者位置,避免种群后期陷入局部最优。其次,考虑到RBF隐含层节点中心,幅值以及输出层权值不确定的问题,提出利用ISSA算法寻优求取。最后,搭建改进的时延预测模型并输入实测时延数据得到预测时延值。实验研究结果表明:相较于传统SSA-RBF模型,文中提出的ISSA-RBF时延预测模型的MSE、MAE和MAPE分别提高了69.46%、32.83%、34.43%,可有效预测网络时延,为之后的时延补偿提供基础。

基于模糊排序的永磁同步电动机模型预测电流控制

针对永磁同步电动机模型预测电流控制的成本函数中权重系数不容易确定的问题,提出一种基于模糊排序法的模型预测电流控制策略。首先,通过排序法对不同电压矢量所对应的电流误差及切换时的开关频率进行排序,从而无须再对权重系数进行设计。由于控制目标重要性并不完全等同,设计一种带有变增益系数的排序优化方法,调节控制目标的重要程度。传统的权重系数有着连续的取值范围,而变增益系数的取值范围有着离散性和分段性的特点,相比较之下,后者的调整和设计过程更加的简易。再通过模糊控制,对变增益系数进行动态优化,从而更好地适应电机不断变化的运行状态。最后,仿真和半实物实验结果证明了所提方法的有效性和可行性。

MMC逆向模型预测控制策略研究

模块化多电平换流器MMC(modular multilevel converter)的模型预测控制存在权重因子难以精确设计和计算负荷大等问题,针对这些问题,提出一种MMC逆向模型预测控制策略。通过逆向预测交流侧相电压和每相上、下桥臂总电压,并利用取整函数和目标函数寻优,计算上、下桥臂投入子模块数,独立控制交流侧电流和桥臂环流。所提方法在无需设计权重因子,且每个采样周期只需进行2次循环寻优的同时,还可使交流侧最大输出2N+1电平,并有效控制桥臂环流。此外,采用子模块电容电压分组排序算法来平衡子模块电容电压。最后通过Matlab/Simulink平台上的仿真,验证了所提方法的正确性和有效性。

基于无权重PID型代价函数的永磁同步电机模型预测转矩控制

【目的】为解决永磁同步电机控制模型参数失配时,模型预测转矩控制(MPTC)算法存在鲁棒性较差的问题,提出了一种基于无权重比例积分微分(PID)型代价函数的MPTC算法。【方法】PID型代价函数通过构建积分误差价值函数消除静态误差,构建微分误差价值函数抑制转矩和磁链误差的振荡变化。由于转矩与磁链量纲不同,PID型代价函数仍然存在权重系数。为解决此问题,又提出一种无权重的双代价函数并行策略,将多目标代价函数转换为单目标代价函数求电压矢量集合,采用电流型代价函数作为评价最优电压矢量的最终依据,从而消除权重系数。最后,通过仿真将文本所提无权重PID型代价函数的MPTC与传统代价函数MPTC进行对比分析。【结果】仿真结果表明,采用无权重PID型代价函数的MPTC能够抑制转矩脉动和磁链脉动,降低MPTC对模型参数的依赖性,解决因存在权重系数而难以适应不同工况条件的问题,同时保留MPTC动态响应快的优点。【结论】本文所提代价函数在永磁同步电机MPTC上具有良好的可行性。

基于LightGBM的集中供热系统预测控制策略研究

针对城市集中供热系统大时滞非线性、换热站负荷预测与调控过度依赖人工经验、系统预测及调控过程不精细、不及时以及系统能耗偏高等现状,提出了一种基于轻梯度提升机(Light Gradient Boosting Machine,LightGBM)的集中供热系统预测控制策略。首先,结合供热系统气候补偿工艺原理,建立了基于LightGBM换热站回水温度预测模型,用以确保回水温度预测目标的及时性与可靠性;其次,设计了基于移动加权平均算法的预测函数,结合PID控制算法实现了集中供热系统的精准负荷预测与高性能调控。实践结果表明,所提出的基于LightGBM的集中供热系统预测控制策略,可及时精准地预测换热站运行负荷且具有更好的超前控制效果,有效地提高了集中供热控制系统的可靠稳定性能。

基于模糊逻辑的空调系统广义预测控制参数整定

针对多输入多输出(multiple-input-multiple-output,MIMO)变风量空调系统(variable air volume,VAV),本文提出了一种基于模糊逻辑和事件触发的广义预测控制(generalized predictive control,GPC)参数整定方法。针对多变量VAV空调系统,基于输出斜率等新型模糊目标参数及高斯双边形隶属度函数建立模糊预测模型,提高了隶属函数拟合度以更贴切地反映系统当前时刻状态。利用麻雀智能算法(sparrow search algorithm,SSA)构建隶属度函数,对GPC控制器中的柔化因子和加权系数进行在线分段整定,有效提升了系统性能。此外,在参数整定过程中,引入事件触发机制(event-triggered mechanism,ETM),在保证控制性能的同时,避免了不必要的控制器采样与更新,降低了系统在线计算量并减少了能源消耗。最后通过VAV空调系统仿真实验验证,证明了本文提出方法的可行性和有效性。

制冷站双目标权重自适应非线性预测控制

针对传统制冷站控制系统易产生振荡,且无法实现系统性能整体优化的问题,本文提出一种制冷站非线性预测控制策略,优化目标函数设计为满足建筑冷量需求的同时,尽可能提高系统整体能效.为解决上述两个优化目标之间的矛盾关系,本文采用模糊逻辑设计了优化目标权重自适应模块,实时求取权重因子最优解;针对非线性系统在线优化求解困难问题,本文提出了基于神经网络的非线性滚动优化算法,采用神经网络作为反馈优化控制器,并将系统优化目标函数作为在线寻优性能指标,结合Euler-Lagrange方法和随机梯度下降法对控制器权值和阈值进行在线寻优,算法计算量小,占用存储空间适中,便于采用低成本的现场控制器实现制冷站预测控制.仿真实验结果表明,本文所提出的预测控制策略与PID控制相比,在未加入优化目标函数权重自适应模块情况下,系统平均能效比提高约32.5%;进行优化目标函数权重自适应寻优后,系统平均能效提高约39.43%.

永磁同步电机模糊自适应低开关频率模型预测电流控制

在永磁同步电机有限集模型预测控制中没有脉宽调制环节,最优电压矢量直接作用于逆变器,开关频率高。为降低开关频率,拟通过永磁同步电机模型预测电流控制系统的代价函数中,引入权重系数自适应调整的开关频率控制项。首先,建立模型预测电流控制模型,分析运行工况及控制周期对开关频率的影响;其次,在代价函数中引入开关频率控制项,分析开关频率控制项权重系数对开关频率和电机控制性能的影响规律;然后,基于模糊控制理论设计权重系数自适应调整的模型预测电流控制系统,通过检测定子电流和参考电流的控制误差值作为模糊控制的输入量,开关项权重系数作为模糊输出量,实现开关项权重系数的自适应调整。仿真结果表明,模糊自适应开关项权重系数控制方式相比于固定权重系数控制,在转速为500r/min时电流谐波增大0.36%时而开关频率降低1.5kHz左右,在2000r/min时开关频率降低0.8kHz左右。

基于加权变时域MPC的无人车辆轨迹跟踪控制

为提高无人车辆轨迹跟踪的准确性和稳定性,提出一种基于加权变时域模型预测控制(MPC)的车辆运动控制方法。通过灰色关联分析法确定不同速度工况下的最优时域,使用傅里叶逼近法对时域参数进行拟合,将控制器计算时间作为权重系数,得到时域参数随车速变化的加权变时域MPC半经验模型。该模型可根据车辆轨迹跟踪速度的变化选择相对最优时域,提升控制器在不同车速下跟踪的精度和稳定性。选取50和100 km/h为代表车速,对传统定时域MPC控制器和加权变时域MPC控制器的控制效果仿真对比。结果表明:加权变时域控制器有效提升了控制器跟踪性能,最大横向偏差降低1.98%,最大横摆角偏差降低60%,控制器求解时间减少7.2%,同时对不同的目标车速工况也有较强的适应性。

采用MPC-MTPA的新能源车永磁同步电机控制研究

针对传统永磁同步电机控制器结构复杂,负载突变时动态响应速度慢,导致电机抗干扰能力差、工作效率低的问题,提出了一种模型预测控制(model predictive control,MPC)融合最大转矩比控制(maximum torque per ampere,MTPA)的电机非级联控制方法。构建MPC和MTPA控制器,利用MPC中的代价函数同时完成转矩控制和MTPA控制,在代价函数中引入动态分配的权重系数优化非级联MPC-MTPA控制器。仿真和试验结果表明:相比传统电机控制方法,提出的变权重系数非级联MPC-MTPA控制方法的稳定性提高约26%,动态响应特性提高约50%。

基于终端代价函数的连续搅拌反应釜无穷时域经济性预测控制优化

针对连续搅拌反应釜,设计一种基于不变集技术和终端代价函数的无穷时域经济性预测控制优化算法。在经济指标中引入动态指标,构造基于寻优动态指标加权矩阵系数的二次复合性能指标,进而通过不变集技术离线计算终端约束集和终端代价函数;将有限时域内的复合性能指标附加终端代价函数,构造无穷时域性能指标,对连续搅拌反应釜进行无穷时域优化控制,改善经济性能;仿真验证了所设计算法的有效性。