Fuzzy Model Based Predictive Control by Instantaneous Linearisation, Fuzzy Sets and Systems

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。

1.1 如果是Matlab安装光盘上的工具箱，重新执行安装程序，选中即可；1.2 如果是单独下载的工具箱，一般情况下仅需要把新的工具箱解压到某个目录。

2 在matlab的file下面的set path把它加上。

3 把路径加进去后在file→Preferences→General的Toolbox Path Caching里点击update Toolbox Path Cache更新一下。

4 用which newtoolbox_command.m来检验是否可以访问。

如果能够显示新设置的路径，则表明该工具箱可以使用了。

把你的工具箱文件夹放到安装目录中“toolbox”文件夹中，然后单击“file”菜单中的“setpath”命令，打开“setpath”对话框，单击左边的“ADDFolder”命令，然后选择你的那个文件夹，最后单击“SAVE”命令就OK了。

MATLAB Toolboxes＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝/zsmcode.htmlBinaural-modeling software for MATLAB/Windows/home/Michael_Akeroyd/download2.htmlStatistical Parametric Mapping (SPM)/spm/ext/BOOTSTRAP MATLAB TOOLBOX.au/downloads/bootstrap_toolbox.htmlThe DSS package for MATLABDSS Matlab package contains algorithms for performing linear, deflation and symmetric DSS. http://www.cis.hut.fi/projects/dss/package/Psychtoolbox/download.htmlMultisurface Method Tree with MATLAB/~olvi/uwmp/msmt.htmlA Matlab Toolbox for every single topic !/~baum/toolboxes.htmleg. 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That should help Video Quality project.Filter Design packagehttp://www.ee.ryerson.ca:8080/~mzeytin/dfp/index.htmlOctave by Christophe COUVREUR (Generates normalized A-weigthing, C-weighting, octave and one-third-octave digital filters)/matlabcentral/fileexchange/loadFile.do?objectType=file&object Id=69Source Coding MATLAB Toolbox/users/kieffer/programs.htmlBio Medical Informatics (Top)CGH-Plotter: MATLAB Toolbox for CGH-data AnalysisCode: http://sigwww.cs.tut.fi/TICSP/CGH-Plotter/Poster: http://sigwww.cs.tut.fi/TICSP/CSB2003/Posteri_CGH_Plotter.pdfThe Brain Imaging Software Toolboxhttp://www.bic.mni.mcgill.ca/software/MRI Brain Segmentation/matlabcentral/fileexchange/loadFile.do?objectId=4879Chemometrics (providing PCA) (Top)Matlab Molecular Biology & Evolution Toolbox(Toolbox Enables Evolutionary Biologists to Analyze and View DNA and Protein Sequences) James J. 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We introduce a novel Matlab toolbox for microarray data analysis. This toolbox uses normalization based upon a normally distributed background and differential gene expression_r based on 5 statistical measures. The objects in this toolbox are open source and can be implemented to suit your application. AVAILABILITY: MDAT v1.0 is a Matlab toolbox and requires Matlab to run. MDAT is freely available at:/publications/2004/knowlton/MDAT.zipMIDI (Top)MIDI Toolbox version 1.0 (GNU General Public License)http://www.jyu.fi/musica/miditoolbox/Misc. (Top)MATLAB-The Graphing Tool/~abrecht/matlab.html3-D Circuits The Circuit Animation Toolbox for MATLAB/other/3Dcircuits/SendMailhttp://carol.wins.uva.nl/~portegie/matlab/sendmail/Coolplothttp://www.reimeika.ca/marco/matlab/coolplots.htmlMPI (Matlab Parallel Interface)Cornell Multitask Toolbox for MATLAB/Services/Software/CMTM/Beolab Toolbox for v6.5Thomas Abrahamsson (Professor, Chalmers University of Technology, Applied Mechanics,Göteborg, Sweden)http://www.mathworks.nl/matlabcentral/fileexchange/loadFile.do?objectId=1216&objectType =filePARMATLABNeural Networks (Top)SOM Toolboxhttp://www.cis.hut.fi/projects/somtoolbox/Bayes Net Toolbox for Matlab/~murphyk/Software/BNT/bnt.htmlNetLab/netlab/Random Neural Networks/~ahossam/rnnsimv2/ftp: ftp:///pub/contrib/v5/nnet/rnnsimv2/NNSYSID Toolbox (tools for neural network based identification of nonlinear dynamic systems) http://www.iau.dtu.dk/research/control/nnsysid.htmlOceanography (Top)WAFO. Wave Analysis for Fatigue and Oceanographyhttp://www.maths.lth.se/matstat/wafo/ADCP toolbox for MATLAB (USGS, USA)Presented at the Hydroacoustics Workshop in Tampa and at ADCP's in Action in San Diego /operations/stg/pubs/ADCPtoolsSEA-MAT - Matlab Tools for Oceanographic AnalysisA collaborative effort to organize and distribute Matlab tools for the Oceanographic Community /Ocean Toolboxhttp://www.mar.dfo-mpo.gc.ca/science/ocean/epsonde/programming.htmlEUGENE D. 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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]. 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用MATLAB的时重要MATLAB toolsADCPtools - acoustic doppler current profiler data processingAFDesign - designing analog and digital filtersAIRES - automatic integration of reusable embedded softwareAir-Sea - air-sea flux estimates in oceanographyAnimation - developing scientific animationsARfit - estimation of parameters and eigenmodes of multivariate autoregressive methodsARMASA - power spectrum estimationAR-Toolkit - computer vision trackingAuditory - auditory modelsb4m - interval arithmeticBayes Net - inference and learning for directed graphical modelsBinaural Modeling - calculating binaural cross-correlograms of soundBode Step - design of control systems with maximized feedbackBootstrap - for resampling, hypothesis testing and confidence interval estimationBrainStorm - MEG and EEG data visualization and processingBSTEX - equation viewerCALFEM - interactive program for teaching the finite element methodCalibr - for calibrating CCD camerasCamera CalibrationCaptain - non-stationary time series analysis and forecastingCHMMBOX - for coupled hidden Markov modeling using maximum likelihood EM Classification - supervised and unsupervised classification algorithmsCLOSIDCluster - for analysis of Gaussian mixture models for data set clusteringClustering - cluster analysisClusterPack - cluster analysisCOLEA - speech analysisCompEcon - solving problems in economics and financeComplex - for estimating temporal and spatial signal complexitiesComputational StatisticsCoral - seismic waveform analysisDACE - kriging approximations to computer modelsDAIHM - data assimilation in hydrological and hydrodynamic modelsData VisualizationDBT - radar array processingDDE-BIFTOOL - bifurcation analysis of delay differential equationsDenoise - for removing noise from signalsDiffMan - solving differential equations on manifoldsDimensional Analysis -DIPimage - scientific image processingDirect - Laplace transform inversion via the direct integration methodDirectSD - analysis and design of computer controlled systems with process-oriented modelsDMsuite - differentiation matrix suiteDMTTEQ - design and test time domain equalizer design methodsDrawFilt - drawing digital and analog filtersDSFWAV - spline interpolation with Dean wave solutionsDWT - discrete wavelet transformsEasyKrigEconometricsEEGLABEigTool - graphical tool for nonsymmetric eigenproblemsEMSC - separating light scattering and absorbance by extended multiplicative signal correctionEngineering VibrationFastICA - fixed-point algorithm for ICA and projection pursuitFDC - flight dynamics and controlFDtools - fractional delay filter designFlexICA - for independent components analysisFMBPC - fuzzy model-based predictive controlForWaRD - Fourier-wavelet regularized deconvolutionFracLab - fractal analysis for signal processingFSBOX - stepwise forward and backward selection of features using linear regressionGABLE - geometric algebra tutorialGAOT - genetic algorithm optimizationGarch - estimating and diagnosing heteroskedasticity in time series modelsGCE Data - managing, analyzing and displaying data and metadata stored using the GCE data structure specificationGCSV - growing cell structure visualizationGEMANOVA - fitting multilinear ANOVA modelsGenetic AlgorithmGeodetic - geodetic calculationsGHSOM - growing hierarchical self-organizing mapglmlab - general linear modelsGPIB - wrapper for GPIB library from National InstrumentGTM - generative topographic mapping, a model for density modeling and data visualizationGVF - gradient vector flow for finding 3-D object boundariesHFRadarmap - converts HF radar data from radial current vectors to total vectorsHFRC - importing, processing and manipulating HF radar dataHilbert - Hilbert transform by the rational eigenfunction expansion methodHMM - hidden Markov modelsHMMBOX - for hidden Markov modeling using maximum likelihood EMHUTear - auditory modelingICALAB - signal and image processing using ICA and higher order statisticsImputation - analysis of incomplete datasetsIPEM - 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 slicingLPSVM - Newton method for LP support vector machine for machine learning problemsLSDPTOOL - robust control system design using the loop shaping design procedure LS-SVMlabLSVM - Lagrangian support vector machine for machine learning problemsLyngby - functional neuroimagingMARBOX - for multivariate autogressive modeling and cross-spectral estimationMatArray - 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 networksNNSYSID - neural net based identification of nonlinear dynamic systemsNSVM - newton support vector machine for solving machine learning problemsNURBS - 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 networksReBEL - sigma-point Kalman filtersRegression - basic multivariate data analysis and regressionRegularization ToolsRegularization Tools XPRestore ToolsRobot - robotics functions, e.g. kinematics, dynamics and trajectory generationRobust 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 problemsSTATBAG - 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 analysisTSTOOL - nonlinear time series analysisT_Tide - harmonic analysis of tidesUTVtools - computing and modifying rank-revealing URV and UTV decompositions Uvi_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 experimentsVOICEBOXVRMLplot - 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。

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【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。

Ｔoolbox工具箱序号工具箱备注一、数学、统计与优化1Symbolic Math Toolbox符号数学工具箱Symbolic Math Toolbox™提供用于求解和推演符号运算表达式以及执行可变精度算术的函数。

您可以通过分析执行微分、积分、化简、转换以及方程求解。

另外，还可以利用符号运算表达式为MATLAB®、Simulink®和Simscape™生成代码。

Symbolic Math Toolbox 包含MuPAD®语言，并已针对符号运算表达式的处理和执行进行优化。

该工具箱备有MuPAD 函数库，其中包括普通数学领域的微积分和线性代数，以及专业领域的数论和组合论。

此外，还可以使用MuPAD 语言编写自定义的符号函数和符号库。

MuPAD 记事本支持使用嵌入式文本、图形和数学排版格式来记录符号运算推导。

您可以采用HTML 或PDF 的格式分享带注释的推导。

2Partial Differential Euqation Toolbox偏微分方程工具箱偏微分方程工具箱™提供了用于在2D，3D求解偏微分方程（PDE）以及一次使用有限元分析。

它可以让你指定和网格二维和三维几何形状和制定边界条件和公式。

你能解决静态，时域，频域和特征值问题在几何领域。

功能进行后处理和绘图效果使您能够直观地探索解决方案。

你可以用偏微分方程工具箱，以解决从标准问题，如扩散，传热学，结构力学，静电，静磁学，和AC电源电磁学，以及自定义，偏微分方程的耦合系统偏微分方程。

3Statistics Toolbox统计学工具箱Statistics and Machine Learning Toolbox 提供运用统计与机器学习来描述、分析数据和对数据建模的函数和应用程序。

您可以使用用于探查数据分析的描述性统计和绘图，使用概率分布拟合数据，生成用于Monte Carlo 仿真的随机数，以及执行假设检验。

回归和分类算法用于依据数据执行推理并构建预测模型。

绿色建筑智能化系统在节能减排中的应用研究*王聚鸿(山东峄州生态建设有限公司山东枣庄277300)摘要本研究聚焦于分析绿色建筑智能化系统在降低能源消耗与减少排放方面的实际效能,对建筑内外环境实时高频监控与深度解析,利用先进的传感器技术,精准调控建筑系统的功能运作㊂在系统设计中,我们采用了基于大数据分析的智能决策模型,历史能耗数据的研究与学习,系统根据实需,能瞬时对各场景实施智能化适应㊂实际建筑项目中的应用验证显示,该方法成效显著,该系统在日常能耗管理方面表现卓越,在极端气候条件下,其卓越适应性得以显现,建筑行业提升层次迈向可持续发展的目标,先进技术赋予深远且实质的保障㊂关键词绿色建筑智能化系统节能减排大数据分析传感器技术中图分类号:T U201.5文献标识码:A 文章编号:1002 2872(2024)04 0213 03近期,城市化全球加速引发建筑领域对能源环境可持续的重视㊂能源消耗的关键领域之一就是这,我国建筑业所面临的资源紧缺和环保压力日益加剧㊂传统绿色建筑设计的核心在于被动房设计和建材挑选,在一定程度上,建筑的能源效益得到了提升,然而,实际操作中,精确把握能源应用及适时调整环保策略仍存困难㊂鉴于气候变化及碳排放难题日益加剧,绿色建筑领域迫切需要创新和智能化解决方案㊂在这样的背景下,绿色建筑的智能化系统逐步崭露头角,先进可持续发展技术推动建筑领域攀至新峰㊂企业采用自动化设备,提升生产效率,实现产能显著增长,智能绿色建筑系统能精准实时监测建筑环境参数,并实施有效管理㊂碳中和等全球环保目标推进中,绿色建筑智能化研究迫在眉睫,新时代建筑行业依托尖端科技,更能契合资源紧缩与环保诉求,可持续发展获得稳固支撑㊂1绿色建筑智能化系统设计与优化1.1传感器技术在系统中的应用绿色建筑的智能化系统之中,传感器技术在各领域发挥关键作用㊂传感器在建筑环境中扮演着关键角色,实时监测各类参数,对内外环境进行全面把控㊂常见传感器种类涉及温度㊁湿度及光照等领域,利用这些探测器搜集的数据,系统实时精准把控建筑内外环境的动态变化㊂例如,温度传感器的应用领域呈现出广泛性,借助建筑物内部署的多种传感器,实时监测系统具备敏锐的区域温度波动捕捉能力,因此,针对暖通空调系统进行优化,以实现精准的室内温度控制[1]㊂这种精微调控的温度管理,舒适度与能源消耗均得到显著优化㊂1.2大数据分析在智能决策模型中的运用绿色建筑的智能化系统之中,大型数据解析被认为是实现明智决策的关键要素㊂对大量历史能耗数据进行深度剖析,我们得以证实,该系统具备学习建筑能耗特性之功能,重构智能决策模式,达成建筑运维优化管控㊂智能决策模型中关键的大数据分析之一,对历史能耗数据进行趋势性分析,预估能源消耗走势变动㊂对过去几年能耗数据进行全面评估与剖析,系统拥有识别建筑在不同季节及各类气象条件下能耗变化规律的功能,因此,未来能源管理提前预测可行,重置建筑架构以更节能方式应对各类能源诉求㊂实时传感器数据深度剖析,大数据分析能实时监控建筑运行状况,从而实现以下目标㊂监控建筑环境实时数据,我们立刻辨别潜在的能耗异常现象,并迅速执行对应的智能判别㊂例如,在温度异常升高时,系统可自主启动节能模式,调整空调设备以降低能耗㊂1.3先进控制算法的设计与实现先进控制算法在绿色建筑智能化系统中具有关键地位,能够实现精准的能源管理㊂常见的先进控制算法包括模型预测控制(M o d e lP r e d i c t i v eC o n t r o l)㊃312㊃(绿色建筑)2024年04月*作者简介:王聚鸿(1988 ),本科,中级工程师;研究方向为建筑工程㊂等,动态规划(D P)及模糊控制器(F u z z y C o n t r o l l e r)等,构建并优化建筑系统的动态模型,它们保证了高效运行,实现了对建筑能源的精细化管理㊂建筑系统动态模型驱动的前沿控制策略即为模型预测控制(M P C)㊂核心目标为搭建建筑系统的物理特性模型,预估短期内系统行为展示,根据预测数据,我们对控制方案实施相应优化㊂M P C则是通过优化问题求解流程来实现目标,预判并确立关乎操控的指令,以应对未来时段,因此,在未来阶段,系统将达至最佳效能㊂该方法全面考虑系统的时变性和非线性特点,适用于处理复杂建筑结构㊂M P C的基本优化问题可以表达为:m i n u j(X,u)其中,j是性能指标,x是系统状态,u是控制输入㊂通过不断求解这个优化问题,M P C能够在每个采样时刻实现最优的控制输入,以适应建筑系统的动态变化[2]㊂具体原理如图1所示:图1模型预测控制原理图被视为模糊控制系统,这是一种先进的操控方式㊂建筑领域内的模糊控制体系,采用融合模糊规则至控制逻辑的方法来实现,实现系统非精确建模与控制任务㊂模糊控制系统中,输入与输出端的信号均用模糊集合予以阐述,经过一系列不确定性规则的推导,最终形成模糊控制成效㊂最后,经解模糊化,将模糊输出转换为具体控制指令㊂具体原理如图2所示:图2模糊控制原理图模糊控制的基本原理可以用以下的模糊规则表示:当y属于B时,z便属于C㊂其中,A㊁B㊁C均为不确定性集合,1㊁2㊁3象征输入与输出㊂这样的模糊规则能依托专家经验,通过系统学习亦可获取,从而让系统更具应变能力,更好地适应建筑领域各种变化,特别是在应对不确定性与模糊性场景时,控制系统中的模糊性展现出特殊优势㊂绿色智能化建筑系统成功实现,得益于高效控制算法的运用,为其提供了卓越的控制能力㊂利用模型预测控制策略,实现建筑系统长期优化管理;运用模糊控制系统,我们能在复杂且波动的环境中实现更富弹性的操控㊂运用这两种算法的智能系统实现对建筑设备的更高效管理,优化能源运用效率至较高水平,因此,精确达成建筑节能降耗目标㊂2绿色建筑智能化系统在节能减排中的实际应用2.1实时监测与精准控制效果全面评估绿色建筑智能化系统的实时监控与精准调控效果,经研究,我们选定了一座具有现代商业特色的综合体作为特定建筑设计目标㊂这座建筑主要由三个部分构成,办公区㊁商业区及公共服务区三者皆为不同功能区域,繁多空间用途及能耗要求的复杂空间㊂在此特定建筑内布置了众多传感器,涵盖温湿度及光照三大监测器,以15m i n为周期,对环境指标进行持续检测,一周时长内,全天候24h持续监测[3]㊂该项研究针对特定实际应用场景展开,对各模块进行精细剖析,以洞悉其作用与性能,基于实际应用场景,评估系统在各种环境下响应速度及稳定性能,对收集到的数据进行全面解析,以评估系统性能的优势与不足㊂表1样本数据时间实际温度(ħ)设定温度(ħ)P I D控制输出温度误差(ħ)光照强度(l x) 8:00AM22.522400.5800 12:00P M22.32235-0.31200 6:00P M22.12245-0.9600 12:00AM22.22242-0.2200对实时追踪温度误差方面,P I D调控策略的研究与分析得以深入,执行各类比例㊁积分及微分计算,调整暖通空调产品制造效能㊂在凌晨8ʒ00时,AM系统实测温度为22.5ħ,相较于基准温度22ħ,实际温度偏差仅为0.5ħ㊂P I D调节策略对输出进行优化,使其值为40,实现了对实际温度的高精度控制㊂在12ʒ00时刻的实际气温为22.3ħ,相对偏差为-0.3ħ㊂P I D调节策略输出结果为35,实际温度已精准调㊃412㊃(绿色建筑)2024年04月控至预定温度区间内㊂类似的效果在6ʒ00P M 和12ʒ00时刻的呈现亦获关注㊂光照强度在各个时段呈现波动特征,然而,P I D 策略具备实时调节温度偏差的功能,温度已成功控制在预定范围内㊂光照强度波动对温度影响不大,系统拥有高度适应性,以应对环境变动㊂绿色建筑智能化系统在实时监测与精准控制方面展现出显著优势㊂P I D 调控策略能高效且准确地解决温度变化问题,确保建筑物内部的温度在预定范围内波动较小㊂这一实况强调了该系统在动态环境下出色的温控性能,建筑能源效益的提升获得坚实支持㊂2.2对历史能耗数据的学习与智能调整深入研究绿色建筑智能化系统在历史能耗数据学习与智能调整方面的性能表现,调查领域涉及一座商务综合体建筑㊂对该建筑的历史能耗数据进行学习,以期提高能源利用效率,我们得以确认,目标系统具备在各种应用环境下进行智能能源消耗优化调整的能力㊂我们收集了这座建筑过去两年电力㊁水资源及天然气使用情况的相应数据,对各月数据实施分类并统计[4]㊂以下是对部分样本数据的统计:表2 历史能耗数据月份电力能耗(k W h )水能耗(m 3)气能耗(m 3)2023年1月25001201002023年2月2300110952023年3月27001301102023年4月2600125105 基于历史能耗数据的学习,针对该建筑物,系统优化调整构建了智能优化体系㊂该模型结合了各类能源的历史消费状况与建筑内外环境指标,运用大数据分析技术预测未来能源消耗情况㊂先进技术应用于智能调整模型,在综合考虑节假日㊁工作日及天气等因素后作出决策,经全面审视,增强建筑体系的自我调节功能;提升系统内部组件协同效应;采用尖端科技提升系统运行效能;实时监控与分析系统状况;构建智能调控体系,提升建筑全体能源效益及环保特性㊂历史能耗数据研究与学习,系统精准掌握了建筑能耗的季节性和周期性等规律㊂以2023年5月为基准进行考察,预测电能消耗将达到2550k W h ,实际电能消耗值为2600k W h ,预测水量消耗将达到122m 3,实际水耗高达125m 3,预估气体能耗攀升至102m 3,实际气体能耗达105m 3㊂系统展示了极高精度的预测效果,误差极小㊂探究模型在各异季及应用场景中的性能表现,智能调整模型被证实可以高效应对建筑能耗变化㊂针对高温环境,系统自动调整空调能源消耗以适应,针对寒冷时节,我们对暖气设备运作实施优化调整㊂这种智能调节既提升了能源效益,又减少了运营费用㊂绿色智能化建筑系统在能源消耗管理方面展现出高效性能,智能调整模型在实际应用场景中发挥作用,体现了高精度和广泛适应性,建筑能源智能优化技术获得实际可行支撑㊂表3 样本数据日期预测电力能耗(k W h)预测水能耗(m 3)预测气能耗(m 3)2023年5月25501221022023年6月2450118982023年7月26001261052023年8月25501231013结语综合审视绿色建筑智能化系统在节能减排领域凸显出的技术优势㊂系统运用了实时监控㊁精准控制㊁传感器技术㊁大数据分析㊁智能决策模型㊁先进控制算法㊁历史能耗数据的学习与智能调整等方法,其应对异常气象的应变技巧,建筑能源效益及环境舒适度的提升优化,系统表现出优异的实力㊂这项研究为建筑领域迈向更高层次的可持续发展提供了深刻且实用的技术支持㊂实际应用中,绿色建筑智能化系统展现了卓越效能,可持续发展建筑的坚实基础得以奠定㊂参考文献[1]操剑飞,陈权.智能化建筑材料在绿色生态节能建筑中的应用[J ].建筑技术开发,2019,46(12):157-158.[2]张峰.绿色理念在建筑暖通空调系统节能设计中的应用研究[J ].城镇建设,2021(12):290.[3]林忠城.建筑智能化在绿色建筑体系中的应用研究[J ].智能城市,2019,5(3):36-37.[4]韩杰.智能优化节能系统在工程建筑节能中的应用[J ].房地产导刊,2020(11):219-220.[5]管琴.浅析建筑智能管理系统建设在绿色节能中的应用意义[J ].居舍,2022(4):151-153.㊃512㊃ (绿色建筑)2024年04月。

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。