Model Predictive Control-Based Direct Torque Control for Matrix Converter-Fed Induction Motor with Reduced Torque Ripple

To reduce the torque ripple in motors resulting from the use of conventional direct torque control(DTC),a model predictive control(MPC)-based DTC strategy for a direct matrix converter-fed induction motor is proposed in this paper.Two new look-up tables are proposed,these are derived on the basis of the control of the electromagnetic torque and stator flux using all the feasible voltage vectors and their associated switching states.Finite control set model predictive control(FCS-MPC)has then been adopted to select the optimal switching state that minimizes the cost function related to the electromagnetic torque.Finally,the experimental results are shown to verify the reduced torque ripple performance of the proposed MPC-based DTC method.

Direct Model Predictive Control of Noninverting Buck-boost DC-DC Converter

In this paper, direct model predictive control(DMPC) of the noninverting buck-boost DC-DC converter with magnetic coupling between input and output is proposed. Unlike most of the other converters, the subject converter has the advantage of exhibiting minimum phase behavior in the boost mode. However, a major issue that arises in the classical control of the converter is the dead zone near the transition of the buck and boost mode. The reason for the dead zone is practically unrealizable duty cycles, which are close to zero or unity, of pulse width modulation(PWM) near the transition region. To overcome this issue, we propose to use DMPC. In DMPC, the switches are manipulated directly by the controller without the need of PWM.Thereby, avoiding the dead zone altogether. DMPC also offers several other advantages over classical techniques that include optimality and explicit current constraints. Simulations of the proposed DMPC technique on the converter show that the dead zone has been successfully avoided. Moreover, simulations show that the DMPC technique results in a significantly improved performance as compared to the classical control techniques in terms of response time, reference tracking, and overshoot.

Model Predictive Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) with Online Parameter Estimation Based on Extended Kalman Filter

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).

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.

Distributed model predictive control for multiagent systems with improved consistency

This paper addresses an improved distributed model predictive control （DMPC） scheme for multiagent systems with an attempt to improving its consistency. The deviation between what an agent is actually doing and what its neighbors believe that agent is doing is penalized in the cost function of each agent. At each sampling instant the compatibility constraint of each agent is set tighter than the previous sampling instant. Like the traditional approach, the performance cost is utilized as the Lyapunov function to prove closed-looped stability. The closed-loop stability is guaranteed if the weight matrix for deviation in the cost function are sufficiently large. The proposed distributed control scheme is formulated as quadratic programming with quadratic constraints. A numerical example is given to illustrate the effectiveness of the proposed scheme.

基于DMPC的无信控交叉口智能网联车辆多车协同轨迹规划

为了解决智能网联自动驾驶环境下无信控十字交叉口多车协同通行的冲突问题,该文提出了一种基于分布式模型预测控制(DMPC)的多车协同轨迹规划方法。采用分布式模型预测框架实现多车协同轨迹规划的分布式独立计算,利用滚动时域预测周车轨迹实现车-车未来状态交互,基于智能网联环境车-车交互通信功能实现规划结果共享;引入道路边界约束、加速度约束与碰撞约束等车辆安全约束条件,通过二次规划求解可以安全通行十字路口的多车轨迹;基于MATLAB驾驶场景生成模块建立无信控十字交叉口环境,并在2种场景下验证了该方法的有效性。结果表明:在直行工况和左转工况下多车间最小距离分别为2.58 m和2.99 m,均满足避撞的安全距离约束,实现了多车之间的协同避撞并且能够保证通行效率。

基于视线协同和DMPC的载机-防御弹群协同主动防御制导策略

在目标-攻击弹-防御弹群(target-attacker-defenders,TADs)系统中,防御弹群通过与目标(载机)异构协同、弹群间同构协同以保护载机并降低单弹脱靶的风险。针对TADs系统在二维平面下的协同主动防御模型进行了研究,采用机/弹协同和防御弹群协同的两层制导策略。在机弹协同方面,防御弹领弹与载机进行异构协同,考虑载机及防御弹领弹的机动能力限制,采用协同视线制导律(cooperative line of sight guidance,CLOSG)分别得到载机和防御弹领弹的制导指令;在防御弹群协同方面,考虑单弹计算能力约束,拦截时间约束和加速度约束,设计出基于分布式模型预测控制(distributed model predictive control,DMPC)的算法实现弹群从弹和防御弹领弹协同同时抵达并拦截攻击弹。仿真结果表明,多防御弹协同一致拦截制导算法能够实现TADs系统中载机和防御弹群的异构协同主动防御,并实现防御弹群的一致性同时拦截,以降低单弹脱靶的风险。

CBF-Based Distributed Model Predictive Control for Safe Formation of Autonomous Mobile Robots

A distributed model predictive control(DMPC)method based on robust control barrier function(RCBF)is developed to achieve the safe formation target of multi-autonomous mobile robot systems in an uncertain disturbed environment.The first step is to analyze the safety requirements of the system during safe formation and categorize them into collision avoidance and distance connectivity maintenance.RCBF constraints are designed based on collision avoidance and connectivity maintenance requirements,and security constraints are achieved through a combination.Then,the specified safety constraints are integrated with the objective of forming a multi-autonomous mobile robot formation.To ensure safe control,the optimization problem is integrated with the DMPC method.Finally,the RCBF-DMPC algorithm is proposed to ensure iterative feasibility and stability while meeting the constraints and expected objectives.Simulation experiments illustrate that the designed algorithm can achieve cooperative formation and ensure system security.

Model predictive control of indoor thermal environment conditioned by a direct expansion air conditioning system

Temperature and humidity are two important factors that influence both indoor thermal comfort and air quality.Through varying compressor and supply fan speeds of a direct expansion(DX)air conditioning(A/C)unit,the air temperature and humidity in the conditioned space can be regulated simultaneously.However,most existing controllers are designed to minimize the tracking errors between the system outputs with their corresponding settings as quickly as possible.The energy consumption,which is directly influenced by the compressor and supply fan speeds,is not considered in the relevant controller formulations,and thus the system may not operate with the highest possible energy efficiency.To effectively control temperature and humidity while minimizing the system energy consumption,a model predictive control(MPC)strategy was developed for a DX A/C system,and the development results are presented in this paper.A physically-based dynamic model for the DX A/C system with both sensible and latent heat transfers being considered was established and validated by experiments.To facilitate the design of MPC,the physical model was further linearized.The MPC scheme was then developed by formulating the objective function which sought to minimize the tracking errors of temperature and moisture content while saving energy consumption.Based on the results of command following and disturbance rejection tests,the proposed MPC scheme was capable of controlling temperature and humidity with adequate control accuracy and sensitivity.In comparison to linear-quadratic-Gaussian(LQG)controller,better control accuracy and lower energy consumption could be realized when using the proposed MPC strategy to simultaneously control temperature and humidity.

计及弱磁效应的直驱式波浪发电系统变系数模型预测控制策略

为提高波浪发电系统(wave energy converter,WEC)能量捕获效率,并保证其在运行过程中满足系统的各种约束条件,该文以平均捕获功率最优为目标,提出计及弱磁效应的直驱式波浪发电系统变系数模型预测控制(model predictive control,MPC)策略。针对波浪激励力过大导致的变流器输出电压过调制问题,将弱磁控制引入优化算法,在满足发电机弱磁极限的前提下增大发电系统的运行范围,提高WEC的能量捕获效率。此外,基于仿真分析,发现了规则波下MPC目标函数的正则系数与平均捕获功率的对应关系。在此基础上,引入快速傅里叶变换(fast Fourier transformation,FFT)与叠加定理,构建了适用于不规则波输入的直驱式WEC变系数MPC控制策略。仿真结果表明,在不规则波浪下,所提方案能在满足系统约束条件的同时,有效提高能量捕获效率。

磁链分区的磁场增强型永磁电机全速域模型预测直接转矩控制

磁场增强型内置式永磁(flux intensifying interior permanent magnet,FI-IPM)电机的反凸极(L_(q)<L_(d))特性,使得正向d轴电流增强磁场,提高输出转矩的同时,也使得弱磁升速时d轴电流正负交替变化,电流角度变化大,磁链波动大,进而造成转矩脉动大、电流畸变振荡、响应速度慢等问题。针对这些问题,该文提出一种基于磁链分区的磁场增强型内置式永磁电机全速域模型预测直接转矩控制控制策略。根据FI-IPM电机速度运行区域与磁链状态映射关系,构建磁链和转矩预测模型。在此基础上,提出磁链分区控制,引入磁链差值判断增磁和弱磁运行区,并进行磁链补偿,解决不同速度运行域之间切换的震荡和不稳定问题。最后,通过实验验证所提出控制策略的有效性,为该类电机在电驱动系统中的应用提供技术方案。

基于模型预测的直驱式波浪发电机机侧最优功率控制技术

直驱式波浪发电系统具有结构简单、成本低、效率高等优点,得到了国内外学者的广泛关注。该文以直驱式波浪发电系统为研究对象,分析并建立了直驱式波浪发电装置的水动力数学模型和永磁直线电机的动态数学模型。推导了基于模型预测的功率控制策略用于直驱式波浪发电系统。在功率控制策略中从功率捕获策略和功率跟踪控制策略展开研究以实现将波浪能转换为电能并汇入直流母线。重点围绕功率捕获策略开展基于模型预测控制技术研究,分别从控制策略、预测区间和成本函数三个角度分析了基于模型预测的最优功率捕获策略。建立了仿真模型并进行仿真分析,结果表明,采用基于模型预测的最优功率捕获策略可有效提高不规则波下发电机从波浪发电系统捕获能量。此外,采用以直流母线功率汇入最大为目标的成本函数可以有效地提高发电机输入直流侧能量,具有重要的实用价值。最后通过实验室模拟平台进行了实验测试。

基于MPC的耦合电感飞跨电容双向DC/DC变换器功率均衡解耦控制策略

耦合电感功率变换器因耦合电感阻抗、电感值等参数差异易导致功率不均衡。针对耦合电感飞跨电容双向DC/DC变换器,提出一种基于模型预测控制(MPC)的功率均衡解耦控制策略。通过对变换器原理进行分析,建立基于电感电流解耦的数学模型,得到包括电感电流和飞跨电容电压等6个控制变量的解耦控制方法。在此基础上,提出基于MPC的功率均衡解耦控制策略。同时,为降低MPC算法的运算负荷,根据解耦控制模型重构模型预测价值函数,实现各控制变量独立动态寻优,使系统能在稳定控制输出电压及飞跨电容电压的同时,实现两相耦合电感的功率均衡控制。最后,通过理论分析及实验对所提策略进行有效验证。

双浮体直驱波浪发电装置建模分析与基于模型预测控制的能量提取算法研究

基于振荡浮体结构的直驱波浪发电装置具有损耗低、控制灵活等优点,近年来,逐渐成为波浪发电领域的研究热点。双浮体直驱波浪发电装置不需要海上固定平台,能够通过锚系结构在海水中工作,是一种工程可行的直驱波浪发电方案,但其动力学模型更为复杂,相关控制算法缺少实际装置测试。该文通过等效电路的方法对双浮体装置进行建模分析,指出采用张力锚结构可将双浮体装置等效为单浮体装置进行分析,简化控制器的分析与设计,从而建立基于模型预测控制的高效波浪能提取问题。提出一种结合波浪激励力辨识、预测与快速模型预测求解的控制算法,并且设计了双浮体直驱波浪发电装置,在不同波浪条件下进行了相关实验测试。实验结果表明,所提控制算法能够在有效降低运算复杂度的前提下提高装置的波浪能提取效率。

基于直接转矩控制的双矢量直接电压模型预测控制策略

模型预测控制(Model predictive control,MPC)是目前电力电子领域研究较为广泛的一种控制策略,不需要调制环节,具有动态响应快、容易实现多目标灵活控制、简单且容易实施等特点。但是这种控制方法由于存在循环寻优的计算过程,算法计算量较大且对控制器的要求较高,不利于实际应用。参考直接转矩控制思想,提出一种双矢量直接电压模型预测控制(Double vector based-direct voltage predictive control,2VB-DVPC)策略,这种控制方式利用查表法省去繁琐的循环寻优计算过程,减少了控制算法的计算量。在Matlab/Simulink仿真平台上搭建传统MPC与2VB-DVPC的仿真模型进行仿真对比,仿真结果验证了所提2VB-DVPC控制策略在保证动稳态性能的情况下,能够有效减少算法的计算量,并且能够固定平均开关频率,降低了谐波滤除的难度。

基于奖惩阶梯型碳价机制的能源枢纽低碳优化策略

为进一步降低碳排放水平以及源-荷不确定性对系统运行的影响,文中提出一种基于奖惩阶梯型碳价机制和分布式模型预测控制(distributed model predictive control,DMPC)的能源枢纽(energy hub,EH)日前-日内-实时多时间尺度低碳优化调度策略。引入奖惩阶梯型碳价计算方法,构建EH日前低碳优化调度模型,并制定基于DMPC的日内滚动和实时调整的反馈闭环优化策略,降低源-荷预测误差,提高传统模型预测控制(model predictive control,MPC)的求解效率。在日内阶段,构建以阶梯型碳成本、运行成本和储能调整惩罚成本之和最小为目标的日内滚动优化模型;在实时阶段,分解整体优化问题,建立基于DMPC的多智能体实时调整模型。算例结果表明,文中所提策略能够有效提升系统经济效益,降低源-荷不确定性,实现EH的低碳经济、稳定可靠运行。

双重化PWM整流器自抗扰模型预测直接功率控制

针对车载双重化脉宽调制(pulse width modulation,PWM)整流器控制性能易受到模型不确定性和列车运行条件(输入电压、功率等级、电路参数等)变化影响的问题,提出一种基于自抗扰控制(active disturbance rejection control,ADRC)和模型预测直接功率控制(model predictive direct power control,MPDPC)的双闭环控制算法。其中,外环基于自抗扰控制理论,构建了基于误差驱动的ADRC(error-based ADRC,EADRC)控制器调节直流侧电压;内环结合基于内模原理的功率补偿方案使用两步MPDPC算法实现电流信号的控制。仿真和实验将所提自抗扰模型预测直接功率控制(ADRC-MPDPC)算法与传统基于比例积分的直接功率控制(proportional integral-based direct power control,PI-DPC)算法和PI-MPDPC方法进行对比,结果表明所提策略在系统启动、负载变化及工况切换等场景表现出更优的动态特性和鲁棒性能。

智能跑步机异步电机变频调速直接转矩控制

因智能跑步机控制系统存在延时的问题,导致跑步机的运行损耗高、效率低。为此,提出智能跑步机异步电机变频调速直接转矩控制方法。分析异步电机的变频调速效果,构建跑步机控制约束,将模型预测直接转矩控制算法作为非线性内部控制器的控制算法,估计定子磁链后预测定子磁链和电磁转矩,建立合适的目标函数,并采用末端输出控制法获取定子磁链和电磁转矩参考值,对跑步机系统延时加以补偿,完成智能跑步机异步电机变频调速直接转矩控制。实验结果表明,所提方法控制下的智能跑步机振幅曲线波动最小,电磁转矩在(0.5~1.5)N·m之间,稳定后的运行损耗和运行效率控制在67 W和0.77%左右,可有效减小跑步机的电磁转矩脉动,降低运行损耗,提高运行效率,且具有良好的平稳性。

基于无电网电压传感器的Vienna整流器滑模模型预测控制

Vienna整流器的控制系统设计与电网电压参数密不可分,然而,传统的电压传感器增加了系统的成本和复杂性.针对此问题,提出一种Vienna整流器无电网电压传感器模型预测控制策略.通过建立Vienna整流器虚拟磁通数学模型来估计电网电压,利用二阶低通滤波器消除了积分初值和直流偏置问题,进而提高观测效果.其次,结合模型预测直接功率控制(DPMPC),以功率跟踪误差最小为目标得到三桥臂的开关序列.实验结果表明,所提出的控制策略谐波含量低,电压能准确跟踪给定值,具有良好的稳态和动态性能.

不平衡电压下的DFIG模型预测直接功率控制

为提高双馈风力发电机在不平衡电网电压工况下的运行性能,提出一种扩张状态观测器与模型预测控制相结合的直接功率控制策略。首先,采用扩张状态观测器对系统中因负序分量引起的不确定项、模型误差及外部扰动等不确定因素进行参数估计。然后,结合模型预测控制理论建立双馈风力发电机模型预测直接功率控制数学模型。最后,针对不平衡电网电压下的功率振荡分量讨论功率优化补偿方案。仿真结果验证了所提方法的有效性。