fuzzy model predictive control for active power filter
Matlab的第三方工具箱大全
Matlab的第三方工具箱大全（强烈推荐）Matlab Toolboxes∙ADCPtools - acoustic doppler current profiler data processing∙AFDesign - designing analog and digital filters∙AIRES - automatic integration of reusable embedded software∙Air-Sea - air-sea flux estimates in oceanography∙Animation - developing scientific animations∙ARfit - estimation of parameters and eigenmodes of multivariate autoregressive methods∙ARMASA - power spectrum estimation∙AR-Toolkit - computer vision tracking∙Auditory - auditory models∙b4m - interval arithmetic∙Bayes Net - inference and learning for directed graphical models∙Binaural Modeling - calculating binaural cross-correlograms of sound∙Bode Step - design of control systems with maximized feedback∙Bootstrap - for resampling, hypothesis testing and confidence interval estimation ∙BrainStorm - MEG and EEG data visualization and processing∙BSTEX - equation viewer∙CALFEM - interactive program for teaching the finite element method∙Calibr - for calibrating CCD cameras∙Camera Calibration∙Captain - non-stationary time series analysis and forecasting∙CHMMBOX - for coupled hidden Markov modeling using maximum likelihood EM∙Classification - supervised and unsupervised classification algorithms∙CLOSID∙Cluster - for analysis of Gaussian mixture models for data set clustering ∙Clustering - cluster analysis∙ClusterPack - cluster analysis∙COLEA - speech analysis∙CompEcon - solving problems in economics and finance∙Complex - for estimating temporal and spatial signal complexities∙Computational Statistics∙Coral - seismic waveform analysis∙DACE - kriging approximations to computer models∙DAIHM - data assimilation in hydrological and hydrodynamic models∙Data Visualization∙DBT - radar array processing∙DDE-BIFTOOL - bifurcation analysis of delay differential equations∙Denoise - for removing noise from signals∙DiffMan - solving differential equations on manifolds∙Dimensional Analysis -∙DIPimage - scientific image processing∙Direct - Laplace transform inversion via the direct integration method ∙DirectSD - analysis and design of computer controlled systems with process-oriented models∙DMsuite - differentiation matrix suite∙DMTTEQ - design and test time domain equalizer design methods∙DrawFilt - drawing digital and analog filters∙DSFWAV - spline interpolation with Dean wave solutions∙DWT - discrete wavelet transforms∙EasyKrig∙Econometrics∙EEGLAB∙EigTool - graphical tool for nonsymmetric eigenproblems∙EMSC - separating light scattering and absorbance by extended multiplicative signal correction∙Engineering Vibration∙FastICA - fixed-point algorithm for ICA and projection pursuit∙FDC - flight dynamics and control∙FDtools - fractional delay filter design∙FlexICA - for independent components analysis∙FMBPC - fuzzy model-based predictive control∙ForWaRD - Fourier-wavelet regularized deconvolution∙FracLab - fractal analysis for signal processing∙FSBOX - stepwise forward and backward selection of features using linear regression∙GABLE - geometric algebra tutorial∙GAOT - genetic algorithm optimization∙Garch - estimating and diagnosing heteroskedasticity in time series models∙GCE Data - managing, analyzing and displaying data and metadata stored using the GCE data structure specification∙GCSV - growing cell structure visualization∙GEMANOVA - fitting multilinear ANOVA models∙Genetic Algorithm∙Geodetic - geodetic calculations∙GHSOM - growing hierarchical self-organizing map∙glmlab - general linear models∙GPIB - wrapper for GPIB library from National Instrument∙GTM - generative topographic mapping, a model for density modeling and data visualization∙GVF - gradient vector flow for finding 3-D object boundaries∙HFRadarmap - converts HF radar data from radial current vectors to total vectors ∙HFRC - importing, processing and manipulating HF radar data∙Hilbert - Hilbert transform by the rational eigenfunction expansion method∙HMM - hidden Markov models∙HMMBOX - for hidden Markov modeling using maximum likelihood EM∙HUTear - auditory modeling∙ICALAB - signal and image processing using ICA and higher order statistics∙Imputation - analysis of incomplete datasets∙IPEM - perception based musical analysisJMatLink - Matlab Java classesKalman - Bayesian Kalman filterKalman Filter - filtering, smoothing and parameter estimation (using EM) for linear dynamical systemsKALMTOOL - state estimation of nonlinear systemsKautz - Kautz filter designKrigingLDestimate - estimation of scaling exponentsLDPC - low density parity check codesLISQ - wavelet lifting scheme on quincunx gridsLKER - Laguerre kernel estimation toolLMAM-OLMAM - Levenberg Marquardt with Adaptive Momentum algorithm for training feedforward neural networksLow-Field NMR - for exponential fitting, phase correction of quadrature data and slicing LPSVM - Newton method for LP support vector machine for machine learning problems LSDPTOOL - robust control system design using the loop shaping design procedureLS-SVMlabLSVM - Lagrangian support vector machine for machine learning problemsLyngby - functional neuroimagingMARBOX - for multivariate autogressive modeling and cross-spectral estimation MatArray - analysis of microarray dataMatrix Computation - constructing test matrices, computing matrix factorizations, visualizing matrices, and direct search optimizationMCAT - Monte Carlo analysisMDP - Markov decision processesMESHPART - graph and mesh partioning methodsMILES - maximum likelihood fitting using ordinary least squares algorithmsMIMO - multidimensional code synthesisMissing - functions for handling missing data valuesM_Map - geographic mapping toolsMODCONS - multi-objective control system designMOEA - multi-objective evolutionary algorithmsMS - estimation of multiscaling exponentsMultiblock - analysis and regression on several data blocks simultaneouslyMultiscale Shape AnalysisMusic Analysis - feature extraction from raw audio signals for content-based music retrievalMWM - multifractal wavelet modelNetCDFNetlab - neural network algorithmsNiDAQ - data acquisition using the NiDAQ libraryNEDM - nonlinear economic dynamic modelsNMM - numerical methods in Matlab textNNCTRL - design and simulation of control systems based on neural networks NNSYSID - neural net based identification of nonlinear dynamic systemsNSVM - newton support vector machine for solving machine learning problems NURBS - non-uniform rational B-splinesN-way - analysis of multiway data with multilinear modelsOpenFEM - finite element developmentPCNN - pulse coupled neural networksPeruna - signal processing and analysisPhiVis - probabilistic hierarchical interactive visualization, i.e. functions for visual analysis of multivariate continuous dataPlanar Manipulator - simulation of n-DOF planar manipulatorsPRTools - pattern recognitionpsignifit - testing hyptheses about psychometric functionsPSVM - proximal support vector machine for solving machine learning problemsPsychophysics - vision researchPyrTools - multi-scale image processingRBF - radial basis function neural networksRBN - simulation of synchronous and asynchronous random boolean networks ReBEL - sigma-point Kalman filtersRegression - basic multivariate data analysis and regressionRegularization ToolsRegularization Tools XPRestore ToolsRobot - robotics functions, e.g. kinematics, dynamics and trajectory generation Robust Calibration - robust calibration in statsRRMT - rainfall-runoff modellingSAM - structure and motionSchwarz-Christoffel - computation of conformal maps to polygonally bounded regions SDH - smoothed data histogramSeaGrid - orthogonal grid makerSEA-MAT - oceanographic analysisSLS - sparse least squaresSolvOpt - solver for local optimization problemsSOM - self-organizing mapSOSTOOLS - solving sums of squares (SOS) optimization problemsSpatial and Geometric AnalysisSpatial RegressionSpatial StatisticsSpectral MethodsSPM - statistical parametric mappingSSVM - smooth support vector machine for solving machine learning problems STATBAG - for linear regression, feature selection, generation of data, and significance testingStatBox - statistical routinesStatistical Pattern Recognition - pattern recognition methodsStixbox - statisticsSVM - implements support vector machinesSVM ClassifierSymbolic Robot DynamicsTEMPLAR - wavelet-based template learning and pattern classificationTextClust - model-based document clusteringTextureSynth - analyzing and synthesizing visual texturesTfMin - continous 3-D minimum time orbit transfer around EarthTime-Frequency - analyzing non-stationary signals using time-frequency distributions Tree-Ring - tasks in tree-ring analysisTSA - uni- and multivariate, stationary and non-stationary time series analysis TSTOOL - nonlinear time series analysisT_Tide - harmonic analysis of tidesUTVtools - computing and modifying rank-revealing URV and UTV decompositionsUvi_Wave - wavelet analysisvarimax - orthogonal rotation of EOFsVBHMM - variation Bayesian hidden Markov modelsVBMFA - variational Bayesian mixtures of factor analyzersVMT - VRML Molecule Toolbox, for animating results from molecular dynamics experiments VOICEBOXVRMLplot - generates interactive VRML 2.0 graphs and animationsVSVtools - computing and modifying symmetric rank-revealing decompositionsWAFO - wave analysis for fatique and oceanographyWarpTB - frequency-warped signal processingWAVEKIT - wavelet analysisWaveLab - wavelet analysisWeeks - Laplace transform inversion via the Weeks methodWetCDF - NetCDF interfaceWHMT - wavelet-domain hidden Markov tree modelsWInHD - Wavelet-based inverse halftoning via deconvolutionWSCT - weighted sequences clustering toolkitXMLTree - XML parserYAADA - analyze single particle mass spectrum dataZMAP - quantitative seismicity analysis。
无人机飞行控制方法概述
2017-10-08 GaryLiu 于四川绵阳无人机的飞行控制是无人机研究领域主要问题之一。
在飞行过程中会受到各种干扰，如传感器的噪音与漂移、强风与乱气流、载重量变化及倾角过大引起的模型变动等等。
这些都会严重影响飞行器的飞行品质，因此无人机的控制技术便显得尤为重要。
传统的控制方法主要集中于姿态和高度的控制，除此之外还有一些用来控制速度、位置、航向、3D轨迹跟踪控制。
多旋翼无人机的控制方法可以总结为以下三个主要的方面。
1.线性飞行控制方法常规的飞行器控制方法以及早期的对飞行器控制的尝试都是建立在线性飞行控制理论上的，这其中就有诸如PID、H∞、LQR以及增益调度法。
1)PIDPID控制属于传统控制方法，是目前最成功、用的最广泛的控制方法之一。
其控制方法简单，无需前期建模工作，参数物理意义明确，适用于飞行精度要求不高的控制。
2)H∞H∞属于鲁棒控制的方法。
经典的控制理论并不要求被控对象的精确数学模型来解决多输入多输出非线性系统问题。
现代控制理论可以定量地解决多输入多输出非线性系统问题，但完全依赖于描述被控对象的动态特性的数学模型。
鲁棒控制可以很好解决因干扰等因素引起的建模误差问题，但它的计算量非常大，依赖于高性能的处理器，同时，由于是频域设计方法，调参也相对困难。
3)LQRLQR是被运用来控制无人机的比较成功的方法之一，其对象是能用状态空间表达式表示的线性系统，目标函数是状态变量或控制变量的二次函数的积分。
而且Matlab软件的使用为LQR的控制方法提供了良好的仿真条件，更为工程实现提供了便利。
4)增益调度法增益调度（Gain scheduling）即在系统运行时，调度变量的变化导致控制器的参数随着改变，根据调度变量使系统以不同的控制规律在不同的区域内运行，以解决系统非线性的问题。
该算法由两大部分组成，第一部分主要完成事件驱动，实现参数调整。
如果系统的运行情况改变，则可通过该部分来识别并切换模态；第二部分为误差驱动，其控制功能由选定的模态来实现。
模型预测控制
，得最优控制率：
根据滚动优化原理，只实施目前控制量u2(k)：
式中：
多步优化MAC旳特点: 优点: (i)控制效果和鲁棒性优于单步MAC算法简朴;
(ii)合用于有时滞或非最小相位对象。 缺陷: (i)算法较单步MAC复杂;
(ii)因为以u作为控制量, 造成MAC算法不可防止地出现稳态误差.
第5章 模型预测控制
5.3.1.2 反馈校正 为了在模型失配时有效地消除静差，能够在模型预测值ym旳基础上 附加一误差项e，即构成反馈校正（闭环预测）。
详细做法：将第k时刻旳实际对象旳输出测量值与预测模型输出之间 旳误差附加到模型旳预测输出ym(k+i)上，得到闭环预测模型，用 yp(k+i)表达：
第5章 模型预测控制
5.1 引言
一 什么是模型预测控制(MPC)？
模型预测控制（Model Predictive Control）是一种基于模型旳闭环 优化控制策略，已在炼油、化工、冶金和电力等复杂工业过程中得到 了广泛旳应用。
其算法关键是:可预测过程将来行为旳动态模型，在线反复优化计
算并滚动实施旳控制作用和模型误差旳反馈校正。
2. 动态矩阵控制（DMC）旳产生：
动态矩阵控制(DMC, Dynamic Matrix Control)于1974年应用在美国壳牌石 油企业旳生产装置上，并于1980年由Culter等在美国化工年会上公开刊登，
3. 广义预测控制（GPC）旳产生：
1987年，Clarke等人在保持最小方差自校正控制旳在线辨识、输出预测、 最小方差控制旳基础上，吸收了DMC和MAC中旳滚动优化策略，基于参数 模型提出了兼具自适应控制和预测控制性能旳广义预测控制算法。
化工总控工技术总结范文
化工总控工技术总结范文英文回答：Chemical Process Control Technology Summary.Chemical process control technology plays a crucialrole in the operation and optimization of chemical plants. As a chemical engineer with extensive experience in the field, I have encountered various challenges and learned valuable lessons. In this summary, I will share my insights and experiences in chemical process control.One of the key aspects of chemical process control is the selection and implementation of control systems. It is essential to choose the right control system that suits the specific process requirements. For instance, in a continuous distillation process, I have successfully implemented a distributed control system (DCS) that allowed for real-time monitoring and control of various parameters such as temperature, pressure, and flow rate. This systemgreatly improved the stability and efficiency of the distillation process.In addition to selecting the appropriate control system, it is crucial to develop effective control strategies. Awell-designed control strategy can ensure the stability and optimal performance of the process. For example, in a polymerization reactor, I implemented a cascade control strategy that involved controlling the temperature and the reactant feed rate. This strategy helped to maintain the desired product quality and minimize the occurrence of undesirable side reactions.Furthermore, process modeling and simulation are essential tools in chemical process control. By developing accurate process models and simulating different scenarios, it is possible to predict the behavior of the process and optimize the control strategy. In a recent project, I useda dynamic simulation software to model a complex chemical reaction and optimize the control parameters. This approach resulted in significant cost savings and improved process efficiency.Another important aspect of chemical process control is the use of advanced control techniques. Advanced control techniques, such as model predictive control (MPC) andfuzzy logic control, can handle complex and nonlinear processes more effectively. In a wastewater treatment plant, I implemented an MPC algorithm to control the pH level and optimize the dosing of chemicals. This approach improvedthe treatment efficiency and reduced the overall operating costs.In conclusion, chemical process control technology is a critical aspect of chemical engineering. By selecting the appropriate control system, developing effective control strategies, utilizing process modeling and simulation, and implementing advanced control techniques, it is possible to optimize the operation of chemical plants and achieve desired outcomes. Through my experiences and examplesshared above, I have witnessed the significant impact of chemical process control in improving process efficiency, product quality, and cost-effectiveness.中文回答：化工总控工技术总结。
艾默生DELTAV系统常见英语单词编译
艾默生Deltav系统常见英文单词编译
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Extensible Parameter External Phase
基于径向基神经网络的列车速度跟踪控制研究
基于径向基神经网络的列车速度跟踪控制研究黄娟;魏宗寿【摘要】针对高速列车运行过程的非线性和运行环境复杂性,提出一种基于径向基(Radial Basis Function,RBF)神经网络模型的广义预测控制方法.采用数据驱动建模方法建立高速列车运行过程径向基神经网络模型,利用广义预测控制算法对列车速度进行跟踪.将神经网络所建模型作为广义预测控制的预测模型,进而推导出RBF 广义预测控制律的灵敏度公式.将该方法与PID、固定模型广义预测控制方法(Fixed Structure Generalized Predictive Control,FGPC)进行仿真对比,该方法体现出较高的精确度和鲁棒性.【期刊名称】《兰州工业学院学报》【年(卷),期】2019(026)002【总页数】5页(P74-78)【关键词】高速列车;径向基神经网络;广义预测控制;跟踪控制【作者】黄娟;魏宗寿【作者单位】[1]兰州交通大学自动控制研究所,甘肃兰州730070;[2]甘肃省高原交通信息工程及控制重点实验室,甘肃兰州730070;[1]兰州交通大学自动控制研究所,甘肃兰州730070;[2]甘肃省高原交通信息工程及控制重点实验室,甘肃兰州730070;【正文语种】中文【中图分类】TP273从近年来城市轨道交通无人自动驾驶技术的发展来看，自动驾驶(ATO)技术也必然会成为高速列车运行控制系统的趋势之一[1].列车自动驾驶需要解决的主要问题是自动调整列车运行速度，使列车安全、可靠、准时、高效地运行.因此，针对高速列车运行工况复杂、环境多变、非线性问题对高速列车运行过程进行精确建模与有效的速度跟踪控制方法进行研究具有重要的现实意义[2].在高速列车建模方面：文献[3]用T-S模型描述高速列车模型的非线性和时变性，但受线路条件的限制；文献[4]利用聚类算法建立多模型来描述高速列车的非线性与不确定性，但不能有效地处理模型间的平滑切换；文献[5]建立高速列车自动驾驶Hammerstein模型，但速度以设定值为中心波动较为明显.在控制方面：广义预测控制在处理复杂的非线性系统方面有明显优势[6]，也开始应用于高速列车速度追踪控制中，如文献[7～9]通过广义预测控制对高速列车速度位移进行跟踪控制，取得了较好的控制效果.径向基神经网络能够逼近任意的非线性函数，可以处理系统内的难以解析的规律性，具有良好的泛化能力，并有很快的学习收敛速度，文献[10]中提出径向基神经网络广义预测控制方法，应用到大型锌湿法炼铁厂除铁工艺控制过程中，工业实验证明了所提方法具有较好的跟踪控制性能和鲁棒性.文献[11]将基于径向基神经网络的模型预测方法用于废水处理过程的溶解氧浓度控制中，提供了一个结构动态变化的预测模型，并分析了闭环系统的稳定性和收敛性，提高了控制性能；故本文通过径向基神经网络描述高速列车运行过程，将径向基神经网络与广义预测控制结合，来实现对高速列车高精度和高鲁棒性的速度跟踪控制.1 基于RBF的多步预测模型设非线性系统由非线性离散时间(NARMAX)模型表示，即y(k)=F[y(t-1),…,y(t-ny);u(t-d),…,u(t-d-nu)]，式中:u(·)和y(·)分别为系统的输入和输出；F(·)为一个未知的连续非线性函数；d为非线性的时滞；ny和nu分别是系统的输出输入阶次.为了建立上述非线性系统模型，将RBF神经网络也选为NARMAX模型，即径向基神经网络结构如图1所示，网络输入为x(t)=[y(t-1),…,y(t-ny),u(t-d),…,u(t-d-nu)]T.包含1个输入层，1个输出层，和1个隐藏层，K个隐藏层节点的输出可以描述为(1)式中：ωk为第k个隐藏神经元和输出神经元的连接权重；k是隐藏神经元的数目；θk是第k个隐藏神经元的输出.且(2)式中：μk是第k个隐藏节点高斯核函数的中心向量，且μk=[μk1,μk2,…,μkn]T；σk是第k个隐藏节点高斯核函数的宽度；‖x(t)-μk‖是x和μk的欧氏距离.图1 RBF神经网络结构RBF神经网络采用梯度下降法调整连接权重ωk(t)、高斯核函数中心μk(t)和宽度σk(t)，来获得参数较优的神经网络模型.首先，定义一个目标函数(3)式中：y为系统实际输出；为神经网络输出，此过程的目标是使期望目标函数E最小.参数更新公式为(4)(5)式中：η为参数学习率；α为动量项因子(α∈[0,1)).2 速度跟踪广义预测控制广义预测控制是自校正控制与预测控制相结合的产物，是一类性能稳定且鲁棒性较强的控制系统.因此，本文通过设计广义预测控制器来对高速列车期望速度进行追踪.基于RBF模型的高速列车速度跟踪预测控制框图如图2所示.图2 基于RBF模型的速度跟踪控制框图定义如下广义预测性能指标(6)受限于(7)式中:分别为未来参考轨迹和预测输出；N1、N2、Nu分别为最小输出长度、预测长度和控制长度；λj为控制加权序列；Δu(t+j-1)为控制增量.将性能指标表示为如下矩阵形式，即(8)其中，Yr=[yr(t+N1)，yr(t+N1+1)，…,yr(t+N2)]TΔU=[Δu(t),Δu(t+1),…,Δu(k+Nu-1)]T,R=diag(λ1,λ2,…,λNu).(9)梯度下降法的基本观点是通过最小化性能指标来求得未来控制时域内的控制量，控制输入序列通过如下梯度更新，即(10)这里η>0是控制输入序列的优化步长，并且(11)其中(12)由式(6)～(9)可得(13)则(14)实现上述广义预测控制律，需计算雅可比矩阵中的灵敏度导数.本文采用较大的预测长度N2，保留广义预测控制中多步预测优势，同时考虑列车速度跟踪控制的实时性，取Nu=1，则雅可比矩阵简化为一个行向量.利用求导法则推导出灵敏度导数为(15)式中：i=0,…,N2-d.3 系统仿真3.1 RBF神经网络模型本文用多输入单输出RBF神经网络来描述高速列车这一非线性系统，模型定义如下(16)式中：y为系统实际输出；为神经网络输出.采集京沪铁路CRH380AL型高速列车某区间的实际运行数据，1 150组运行速度和控制力数据，选择900组数据样本训练RBF神经网络模型，剩余250组数据作为测试数据.RBF初始网络结构为5-6-1，参数学习率η=0.5，动量项因子为α=0.01，时滞d=3，训练过程中加入噪声，RBF测试数据误差曲线如图3所示，可以看出，高速列车测试输出与实际模型的输出误差为[-0.127 6, 0.102 3]km/h,在允许范围内.图3 RBF数据输出误差曲线3.2 速度跟踪控制列车按“牵引-恒速-惰行-恒速-牵引-恒速-惰行-制动”方式运行，基于上述所建立的RBF模型，采用广义预测控制对京沪高铁运营的CRH380AL列车进行速度跟踪控制，控制器参数N1=3，N2=3，Nu=1，R=0.3I.将仿真结果与PID、固定模型广义预测控制方法FGPC进行对比.速度跟踪曲线与误差曲线如图4～5所示.由图4可知，PID控制与FGPC在高速列车追踪控制启动过程中速度曲线偏离较大，在恒速和降速过程中发生振荡现象，从仿真结果中看出径向基广义预测控制(Radial Basis Function Generalized Predictive Control, RBF-GPC)跟踪精度高，控制效果好，对列车运行的复杂工况具有很好的适应性.图5通过速度追踪误差的对比，进一步说明RBF-GPC控制器速度误差小，满足CTCS-3列控系统的误差要求[12].图6为高速列车位移曲线，由图可看出，PID与FGPC两种控制方法与目标曲线偏差较大，RBF-GPC几乎能完全追踪目标参考曲线，体现了该方法较好的控制性能.图4 速度跟踪曲线对比图5 速度跟踪误差对比图6 位移曲线对比4 结语针对高速列车建模难和控制复杂的难题，提出了RBF-GPC方法，基于径向基神经网络建立高速列车运行过程的预测模型，仿真表明建模准确性高，所提出的控制方法能够有效追踪速度曲线，与PID和FGPC相比，本文方法控制性能好，鲁棒性更高.参考文献：【相关文献】[1] DONG H, NING B, CAI B, et al. Automatic Train Control System Development and Simulation for High-Speed Railways[J]. Circuits &Systems Magazine IEEE, 2010, 10(2):6-18.[2] LI Zhongqi, YANG Hui, ZHANG Kunpeng, et al. Distributed Model Predictive Control Based on Multi-agent Model for Electric Multiple Units[J]. Acta Automatic Sinica, 2014,40(11): 2625-2631.[3] YANG H, FU Y T, ZHANG K P. Generalized predictive control based on neurofuzzy model for electric multiple unit[C]// Proceedings of the Third International Conference on Digital Manufacturing and Automation, Guilin:IEEE,2012. 422-445.[4] 杨辉, 张坤鹏, 王昕, 等. 高速列车多模型广义预测控制方法[J]. 铁道学报. 2011, 34(8): 16-21.[5] 郭红弋, 孙志毅, 张春美. 动车组列车制动系统Hammerstein模型的广义预测控制研究[J]. 铁道学报, 2014, 36(6): 47-54.[6] WU M, WANG C, CAO W, et al. Design and application of generalized predictive control strategy with closed-loop identification for burn-through point in sintering process[J].Control Engineering Practice, 2012, 20(10): 1065-1074.[7] 李中奇, 杨振村, 杨辉, 等. 高速列车双自适应广义预测控制方法[J]. 中国铁道科学, 2015, 36(6): 120-126.[8] 杨辉, 刘盼, 李中奇. 基于Elman模型的高速列车速度跟踪控制[J]. 控制理论与应用, 2017, 34(1): 125-130.[9] 李中奇, 杨辉, 刘明杰, 等. 高速动车组制动过程的建模及跟踪控制[J].中国铁道科学, 2016,37(5):80-85.[10] XIE Shiwen, XIE Yongfang, HUANG Tingwen Huang et al. Generalized predictive control for industrial processes based on neuron adaptive splitting and merging RBF neural network[J]. IEEE Trans. Indus. Electric，2018,11(09):1-10.[11] HAN Hong-Gui, ZHANG Lu, HOU Ying, et al. Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network[J]. IEEE Transactions on Neural Networks and Learning Systems，2016,27(2):402-415.[12] FU Yating, YANG Hui, WANG Dianhui. Real-time optimal control of tracking running for high-speed electric multiple unit[J]. Information Sciences, 2017, 376:202-215.。
matlab完整版（三相闭环）
matlab完整版（三相闭环）三闭环错位选触⽆环流可逆直流调速系统1. 系统仿真应⽤软件及其简介三闭环错位选触⽆环流可逆直流调速系统仿真应⽤的软件是MATLAB 7.0。
MATLAB是为了在科学研究和⼯程应⽤中，克服⼀般语⾔对⼤量数学运算，尤其是涉及矩阵运算时编制程序复杂、调试⿇烦等困难，美国Math Works公司于1967年构思并开发了矩阵实验室（Matri Laboratory’，MATLAB）软件包。
经过不断的更新和扩充，该公司于1984年推出了MATLAB的正式版，特别是1992年推出具有跨时代意义的MATLAB 4.0版，并于1993年推出其微机版，以配合当时⽇益流⾏的Microsoft Windows 操作系统⼀起使⽤。
截⽌到2005年，该公司先后推出了MATLAB 4.x，MATLAB 5.x，MATLAB 6.x，MATLAB 7.xD等版本，该软件的应⽤范围越来越⼴。
常见的MATLAB⼯具箱有以下⼏种。
Control System Toolbox——控制系统⼯具箱Communication Toolbox——通讯⼯具箱Financial Toolbox——财政⾦融⼯具箱System Identification Toolbox——系统辨识⼯具箱Fuzzy Logic Toolbox——模糊逻辑⼯具箱Higher-Order Spectral Analysis Toolbox——⾼阶谱分析⼯具箱Image Processing Toolbox——图象处理⼯具箱computer vision system toolbox----计算机视觉⼯具箱LMI Control Toolbox——线性矩阵不等式⼯具箱Model predictive Control Toolbox——模型预测控制⼯具箱µ-Analysis and Synthesis Toolbox——µ分析⼯具箱Neural Network Toolbox——神经⽹络⼯具箱Optimization Toolbox——优化⼯具箱Partial Differential Toolbox——偏微分⽅程⼯具箱Robust Control Toolbox——鲁棒控制⼯具箱Signal Processing Toolbox——信号处理⼯具箱Spline Toolbox——样条⼯具箱Statistics Toolbox——统计⼯具箱Symbolic Math Toolbox——符号数学⼯具箱Simulink Toolbox——动态仿真⼯具箱Wavele Toolbox——⼩波⼯具箱DSP system toolbox-----DSP处理⼯具箱这⾥只重点介绍MATLAB在本系统中Simulink的仿真环境。
自动化专业英语词汇表
自动化专业英语词汇表自动化专业是应用一系列科学技术和方法，通过使用自动控制系统和自动装置，使生产过程自动进行的一门学科。
在这个专业中经常会遇到一些与自动化相关的英语词汇，下面是一个自动化专业英语词汇表，供大家参考。
一、控制系统相关词汇1.1 控制系统 - Control System1.2 自动控制 - Automatic Control1.3 反馈控制 - Feedback Control1.4 前馈控制 - Feedforward Control1.5 PID控制 - PID Control1.6 闭环控制 - Closed-loop Control1.7 开环控制 - Open-loop Control1.8 控制器 - Controller1.9 传感器 - Sensor1.10 执行器 - Actuator1.11 控制信号 - Control Signal1.12 输出信号 - Output Signal1.13 输入信号 - Input Signal1.14 控制策略 - Control Strategy1.15 控制精度 - Control Accuracy二、自动化设备相关词汇2.1 自动装置 - Automatic Device 2.2 自动机械 - Automated Machinery 2.3 机器人 - Robot2.4 运动控制 - Motion Control2.5 伺服系统 - Servo System2.6 步进电机 - Stepper Motor2.7 传动装置 - Transmission Device 2.8 传动比 - Gear Ratio2.9 电气驱动 - Electrical Drive2.10 液压驱动 - Hydraulic Drive2.11 气动驱动 - Pneumatic Drive 2.12 PLC程序 - PLC Program2.13 HMI界面 - HMI Interface2.14 人机交互 - Human-Machine Interaction2.15 自动化线 - Automation Line三、控制算法相关词汇3.1 模糊控制 - Fuzzy Control3.2 神经网络控制 - Neural Network Control 3.3 遗传算法 - Genetic Algorithm3.4 自适应控制 - Adaptive Control3.5 模型预测控制 - Model Predictive Control 3.6 最优控制 - Optimal Control3.7 鲁棒控制 - Robust Control3.8 软件开发 - Software Development3.9 编程语言 - Programming Language3.10 程序调试 - Program Debugging3.11 系统优化 - System Optimization3.12 数据采集 - Data Acquisition3.13 实时控制 - Real-time Control3.14 开发工具 - Development Tool3.15 算法设计 - Algorithm Design四、自动化监控相关词汇4.1 监控系统 - Monitoring System 4.2 故障诊断 - Fault Diagnosis4.3 警报系统 - Alarm System4.4 远程监控 - Remote Monitoring 4.5 数据分析 - Data Analysis4.6 数据可视化 - Data Visualization 4.7 运行状态 - Operating Status4.8 故障报警 - Fault Alarm4.9 监控设备 - Monitoring Equipment 4.10 实时监测 - Real-time Monitoring 4.11 数据记录 - Data Logging4.12 故障排除 - Trouble Shooting 4.13 监测指标 - Monitoring Index 4.14 运行参数 - Operating Parameters 4.15 监测报告 - Monitoring Report总结：以上是自动化专业英语词汇表，涵盖了控制系统、自动化设备、算法和监控等多个方面的词汇。
model predictive control 参考课程
model predictive control参考课程【释义】model predictive control模型预测控制：一种先进的控制策略，通过预测未来的系统行为来优化控制器的性能。
【短语】1Model predictive Control Toolbox模型预测控制工具箱;控制工具箱2nonlinear model predictive control非线性模型预测控制3model predictive control mpc模型预测控制4Linear Model Predictive Control线性预测控制;引言线性预测控制5multiple model predictive control多模型预测控制6robust model predictive control鲁棒模型预测控制;鲁棒预测控制7novel internal model predictive control新型内模预测控制8OPC model predictive controlOPC模型预测控制【例句】1The application of Model Predictive Control to PTA equipment is presented.介绍了模型预测控制在PTA装置中的应用。
2Firstly,it considers the simple linear model predictive control algorithms.首先考虑简单的线性预测控制。
3A nonlinear model predictive control(NMPC)strategy based on T_S fuzzy model is proposed.提出了一种新的基于T_S模糊模型的非线性预测控制策略。
4Model predictive control based on the local linearization state-space model is introduced in detail.详细的介绍了基于局部线性化状态空间模型的预测控制算法。
控制系统中的自适应控制算法研究
控制系统中的自适应控制算法研究自适应控制算法是控制系统中一种重要的控制方法，它具有自学习能力和自调节能力，能够对未知的变化环境进行适应和调整，提高控制系统的性能和鲁棒性。
本文将从自适应控制算法的定义、分类和应用方面进行详细的研究。
首先，自适应控制算法是一种能够根据系统输出和输入之间的误差进行自动调整的控制方法。
它通过不断地对系统建模和参数调整，来适应不同的工作状态和外部干扰。
自适应控制算法的核心思想是通过反馈机制来实时监测系统的状态，将监测到的信息用于对系统模型和参数进行更新，从而不断优化控制效果。
根据自适应控制算法的不同特点和应用，可以将其分为多种类型。
其中，最常见的自适应控制算法包括模型参考自适应控制 (Model Reference Adaptive Control，MRAC)、最小二乘法自适应控制 (Least Mean Squares Adaptive Control，LMS)、自适应模糊控制(Adaptive Fuzzy Control，AFC)、神经网络自适应控制 (Neural Network Adaptive Control，NNAC) 等。
每种算法都有其特定的适用范围和优势，可以根据控制系统的具体要求选择合适的自适应控制算法。
自适应控制算法在各种领域中广泛应用。
在工业自动化中，自适应控制算法能够应对系统参数变化和外部干扰，提高控制系统的鲁棒性和稳定性。
在机器人控制中，自适应控制算法能够实现对不同工作环境和任务的自动学习和调整，提高机器人的自主性和适应性。
在电力系统控制中，自适应控制算法能够对复杂的电力系统进行优化调节，提高电力系统的稳定性和效率。
在交通控制中，自适应控制算法能够根据交通流量和路况情况自动调整信号灯的控制策略，提高交通流量的效率和安全性。
随着科学技术的不断发展，自适应控制算法也在不断演进和改进。
目前，一些新兴的自适应控制算法如模型预测控制 (Model Predictive Control，MPC)、强化学习控制(Reinforcement Learning Control，RLC)、深度学习控制(Deep Learning Control，DLC) 等正在被广泛研究和应用。
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This work was supported in part by china national science h n d under Grant 60075008. Fan Shaosheng is with the Department of Electrical Engineering, Hunan University, Changsha, China (e-mail: fanss50X~j;) .Wang Yaonan is with the Department of Electrical Engineering, Hunan University, Changsha, China (e-mail:yaonan@).
Index term-Fuzzy model predictive control
predictive control [4] has been applied and is shown to improve the overall performance of APF in response time and accuracy. However, this control strategy necessitates that the mathematical model of the control system should be exactly known which is not the case in most situation. In this paper, fuzzy model predictive control is proposed for APF, it has the advantages of without using prior knowledge of the structure and parameters of the controlled system and can be considered as a powerful tool for the control of complex system. T-S fuzzy models are suitable to model a large class of non-linear systems, it has the ability to achieve complicated mappings and therefore can be used to predict harmonic compensating current at various conditions. During fuzzy modeling, fuzzy model is derived fiom input-output measured data by means of fuzzy clustering, similarity driven rule base simplification is applied to detect and merges compatible fuzzy sets in the model and a novel method is proposed to determine the number of clusters . All these techniques ensure the fuzzy model compact and accurate. Based on the predictive model, the value of control variable is acquired by means of optimization technique. Iterative optimization techniques
past and present values of the compensating current and past values of the control vector. Based on the predictive output, the value of control vector U can be figured out by model predictive control algorithm. This control vector is adequately modulated by means of a space vector PWM, which generates proper gating patterns of the inverter switches to maintain tracking of dynamic reference current without tracking error.
Fan Shaosheng and Wang Yaonan Abstract- A fuzzy model predictive control strategy for active power filter is presented in this paper. In the strategy, T-S fuzzy model is employed to predict future harmonic compensating current. The fuzzy model is derived from input-output data by means of product-space fuzzy clustering.
In order to make the fuzzy model compact and aer to produce harmonic compensating currents and cancels wave distortions greatly. The effectiveness of an APF depends on its detection and control strategy. In order to effectively compensate the harmonics produced by loads, it is important to track the variations of both the load and the harmonic conditions for the APF and make control strategy properly. Research has shown that conventional APF control methods such as hysteresis control, digital adaptive control demonstrate the effective performance in some situation, but all these controllers are unable to perform optimally over the full range of operation conditions and disturbances, due to the highly complex, non-linear nature of controlled systems
[ 1],[2],[3]. In order to overcome this difficulty, non-linear
similarity driven rule base simplification is applied to detect and merge compatible fuzzy sets in the model and a new validity measure is proposed to determine appropriate number of the clusters. Based on the model output, branch-and-bound optimization method is adopted to produce proper value of control vector, this value is adequately modulated by means of a space vector P W M modulator which generate proper gating patterns of the inverter switches to maintain tracking of reference current. The fuzzy model predictive algorithm is used in internal model control scheme to compensate for process disturbances, measurement noise and modeling errors. The proposed control is applied to compensate the harmonic produced by the variable non-linear load. Simulation results show the fuzzy model based predictive controller is effective and feasible.
are mostly slow due to computational complexity, this hampers its application to fast system. In order to solve the problem, branch-and-bound optimization method is adopted. The fkzzy model predictive algorithm is used in internal model control scheme to compensate for process disturbances, measurement noise and modeling errors. Simulation test under various conditions is implemented, the effectiveness of the control scheme is proved.