SIMULATION USING MATLAB

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Control Systems Simulation Using Matlab and Simulink pot

CONTROL SYSTEMS SIMULATION USING MATLAB AND SIMULINK POT

Control Systems SimulationUsing Matlab and Simulink1 Introduction2 Control of Second Order System2.1 Open loop unit-step response2.2 Continuous Time (C.T.) closed loop unit-step response2.3 Discrete Time (D.T.) closed loop unit-step response3 Two-mass vibratory system4 Ball-and-Beam5 Report: ( One report p er group)5.1 Recommended Form for Lab Reports:5.2 Helpful Hints0 20 40 60 80 100 12000.20.40.60.811.21.4time (secs)positionStep Response of second order system6 “Hands-on” Design of Control Systems6.1 Performance Specification6.2 Feedback Control6.3 Sampling Time with PID controllers
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TÀI LIỆU CONTROL SYSTEMS SIMULATION USING MATLAB AND SIMULINK PPTX

TÀI LIỆU CONTROL SYSTEMS SIMULATION USING MATLAB AND SIMULINK PPTX

UNIVERSITY OF CALIFORNIA AT BERKELEYDepartment of Mechanical EngineeringME134 Automatic Control SystemsSpring 2002Report Due: Tuesday, February 26 One report per group is required.Control Systems SimulationUsing Matlab and Simulink1 IntroductionIn ME134, we will make extensive use of Matlab and Simulink in order to design, analyze andsimulate the response of control systems.2 Control of Second Order SystemWe will simulate the open loop and closed loop step response of the dynamic system described bythe state and output equationsddtx1= −.1 x1+ .1 x2(1)ddtx2= −.2 x2+ .1 uy = x1

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ENGINEERING EDUCATION AND RESEARCH USING MATLAB docx

ENGINEERING EDUCATION AND RESEARCH USING MATLAB DOCX

drives (Castellan et al., 2007) and power generation systems (Sulligoi et al., 2010). Regardless of whether they are used as motors or generators, multiphase electrical machines are almost always connected to power electronics systems (inverters for motors, rectifiers for generators), which interface them to the electric grid (Sulligoi et al., 2010; Castellan et al. 2008). Therefore, if the dynamic behaviour of a multiphase machine is to be predicted through simulations in the design and development stage, it is essential to do this by means of system-level simulations, where not only the electric machine is included, but also the power electronics and control systems that interact with it. Such a system-level simulation approach makes it difficult to use Finite-Element (FE) methods due to the complexity of the domain to be modelled and to the well-known computational heaviness of time-stepping FE simulations. Conversely, lumped-parameter models, to be implemented in the Matlab/Simulink environment, may provide designers with a powerful mean of analysis and investigation, provided that all the system components to be studied are modelled with an adequate level of accuracy and completeness. As concerns power electronics systems usually interfaced to multiphase machines, whether operating as motors or generators, the Matlab/Simulink environment offers wide and complete libraries where the designer can find reliable pre-defined blocks (for electronic switches, snubbers, diodes, etc.) to be used in building the application-related apparatus models. The same pertains to control and regulation blocks, which can be built up directly based on their transfer functions and logics. A possible criticality can be encountered when it comes to build the multiphase machine model. In fact, no predefined blocks are presently available in the Matlab/Simulink environment for this purpose. On the other side, building a dedicated user-defined machine Engineering Education and Research Using MATLAB 2 model for any specific multiphase arrangement may require a non-trivial work due to the wide variety of multiphase schemes (in terms of phase number and distribution) and due to the several different modelling approaches which can be derived from the current literature
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using matlab in linear algebra

USING MATLAB IN LINEAR ALGEBRA

+ , In this section we discuss some computational tools that can be used in studies of real vectorspaces. Focus is on linear span, linear independence, transition matrices and the Gram-Schmidtorthogonalization.21Linear spanConcept of the linear span of a set of vectors in a vector space is one of the most important onesin linear algebra. Using MATLAB one can determine easily whether or not given vector is in thespan of a set of vectors. Function span takes a vector, say v, and an unspecified numbers ofvectors that form a span. All inputted vectors must be of the same size. On the output a messageis displayed to the screen. It says that either v is in the span or that v is not in the span.function span(v, varargin)% Test whether or not vector v is in the span of a set% of vectors.A = [];n = length(varargin);for i=1:n u = varargin{i}; u = u'; A = [A u(:)];endv = v';v = v(:);if rank(A) == rank([A v]) disp(' Given vector is in the span.')else disp(' Given vector is not in the span.')endThe key fact used in this function is a well-known result regarding existence of a solution to thesystem of linear equations. Recall that the system of linear equations Ax = b possesses a solution
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Solution digital signal processing using MATLAB

SOLUTION DIGITAL SIGNAL PROCESSING USING MATLAB

SolutionDigitalSignalProcessingUsingMATLAB for ebook Digital Signal Processing Using MATLAB 3rd EditionSlicer Hướng dẫn bài tập Matlab Đại học bách khoa. Sử dụng matlab Thiết kế bộ lọc. FIR, IIR

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shallow liquid simulation using matlab 2001 neumann pot

SHALLOW LIQUID SIMULATION USING MATLAB 2001 NEUMANN POT

slower and also less accurate.Note that these modifications are only needed when solving equation (25) withmatrix methods. We do not need the modification when using the FFT method,or in setting up equation (37) for time stepping.3.4 Comparing SolversWe have various methods of solving equation (15), such as FFT, Gaussian Elim-ination, LU decomposition, and iterative solvers. To check that they work cor-rectly we e xamine how well the resulting ψ satisfies the equation (15). We usesecond order central differences on ψ to approximate ∇2ψ and compare thisto ω.Another consideration is that in solving equation (15) we expect ψ to be sym-metric given a symmetric ω.3.5 An FFT problemWhen using the Fast Fourier Transform to solve equation (15) we make use ofequation (32). But when both kxand kyare ze ro, the right hand side goes toinfinity. To avoid this problem we add a small amount, such as 10−4, to thezero entry in the matrix of k numbers.It turns out that if this value is too small, on the order of 10−14, then theresult is not usable. This can be established using the technique described insection 3.4. My guess is that the resulting component is so large that it swamps
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Tài liệu Control Systems Simulation using Matlab and Simulink doc

TÀI LIỆU CONTROL SYSTEMS SIMULATION USING MATLAB AND SIMULINK DOC

Li-Pheng Khoo Nanyang Technological University Lian-Yin Zhai Nanyang Technological University ©2001 CRC Press LLC Many theories, techniques, and algorithms have been developed to deal with the analysis of impreciseor inconsistent data in recent years. The most successful ones are fuzzy set theory and Dempster–Shafertheory of evidence. On the other hand, rough set theory, which was introduced by Pawlak [1982] in theearly 1980s, is a new mathematical tool that can be employed to handle uncertainty and vagueness.Basically, rough set handles inconsistent information using two approximations, namely the upper andlower approximations. Such a technique is different from fuzzy set theory or Dempster–Shafer theory ofevidence. Furthermore, rough set theory focuses on the discovery of patterns in inconsistent data setsobtained from information sources [Slowinski and Stefanowski, 1989; Pawlak, 1996] and can be used asthe basis to perform formal reasoning under uncertainty, machine learning, and rule discovery [Ziarko,1994; Pawlak, 1984; Yao et al., 1997]. Compared to other approaches in handling uncertainty, rough settheory has its unique advantages [Pawlak, 1996, 1997]. It does not require any preliminary or additionalinformation about the empirical training data such as probability distribution in statistics; the basicprobability assignment in the Dempster–Shafer theory of evidence; or grades of membership in fuzzyset theory [Pawlak et al., 1995]. Besides, rough set theory is more justified in situations where the set ofempirical or experimental data is too small to employ standard statistical method [Pawlak, 1991].In less than two decades, rough set theory has rapidly established itself in many real-life applications
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USING MATLAB V.4 P1

USING MATLAB V.4 P1

M.ATLAB and PC MATLAB are registered trademarks of The Mathworks, Inc. The MathWorks, Inc. is the developer of MATLAB, the high-performance computational software introduced in this book. For further information on MATLAB and other MathWorks products — including SIMULINKTM and MATLAB Appli- cation Toolboxes for math and analysis, control system design, system identifcation, and other disciplines—— contact The MathWorks at 24 Prime Park Way, Natick, MA 01760 (phone: 508-653-1415; fax: 508-653-2997; email: info@mathworks.com). You can also sign up t© receive the MathWorks quarterly newsletter and register fox the user gTOUD.
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KỸ THUẬT TRUYỀN SỐ LIỆU A3

KỸ THUẬT TRUYỀN SỐ LIỆU A3

TRANG 1 DIGITAL COMMUNICATION USING MATLAB®V.6 DR.[r]

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Chuong 1_KTDKHD_Gioi thieu pptx

CHUONG 1_KTDKHD_GIOI THIEU PPTX

Ngày 23 tháng 8 năm 2012 – Trường ĐHCN T/P Hồ Chí Minh 1KỸ THUÂT ĐIỀU KHIỂN HIỆN ĐẠI Giảng viên : TS Nguyễn Văn Trung Khoa Cơ khí – Bộ Môn Cơ điện tử Trường Đại học Công nghiệp thành phố Hồ Chí Minh Ngày 23 tháng 8 năm 2012 – Trường ĐHCN T/P Hồ Chí Minh 1NỘI DUNG MÔN HỌC • CHƯƠNG 1 : GIỚI THIỆU TỔNG QUAN VỀ MÔN HỌC •CHƯƠNG 2 : ĐIỀU KHIỂN PHẢN HỒI TRẠNG THÁI VÀ ƯỚC LƯỢNG TRẠNG THÁI •CHƯƠNG 3 : ĐIỀU KHIỂN TỐI ƯU •CHƯƠNG 4 : ĐIỀU KHIỂN MỜ •CHƯƠNG 1 : MẠNG NƠRON Ngày 23 tháng 8 năm 2012 – Trường ĐHCN T/P Hồ Chí Minh 1TÀI LIỆU HỌC TẬP [1] : Lý Thuyết Điều khiển tự động hiện đại, Nguyễn Thị Phương Hà, NXB ĐHQG 2007 . [2]: Bài giảng Lý Thuyết Điều khiển tự động hiện đại, Huỳnh Minh Ngọc, NXB ĐHCNTPHCM- Khoa Điện tử . TÀI LIỆU THAM KHẢO [1] : Modern Control System, Otaga, John Wiley & Sons, 1989 [2] : Modern Control System Analysis and Design Using Matlab, Robert H. Behop, Texas. 1998
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Proceedings VCM 2012 21 chiến lược điều khiển nghịch lưu nguồn z nối lưới cho trạm phát

PROCEEDINGS VCM 2012 21 CHIẾN LƯỢC ĐIỀU KHIỂN NGHỊCH LƯU NGUỒN Z NỐI LƯỚI CHO TRẠM PHÁT

Tóm tắt Bài báo này trình bày chiến lược điều khiển nghịch lưu nguồn Z nối lưới cho trạm phát điện sức gió sử dụng máy phát đồng bộ nam châm vĩnh cửu. Toàn bộ cấu trúc điều khiển được đưa ra phân tích, mô hình hóa để xây dựng thuật toán điều khiển đảm bảo để công suất đưa từ máy phát lên lưới lớn nhất dưới các tốc độ gió khác nhau và điện áp một chiều trên tụ C mạch nguồn Z giữ giá trị mong muốn đảm bảo điều kiện nối lưới. Kết quả mô phỏng offline bằng Matlab và kết quả mô phỏng thời gian thực khi kết hợp DSPTMS320F2812 với card DS1103 đã minh họa kiểm chứng cấu trúc toàn bộ hệ thống đã được xây dựng. Abstract: This paper presents the control strategy Z source inverter grid connected for the wind power using permanent magnet synchronous generator. The control strategy is thoroughly analyzed and modeled to figure out control algorithm in order to assure that the power transmitted from generator to grid is maximum under different wind speeds while the DC voltage of capacitors remains the required valueof grid connecting condition. The offline simulation result using Matlab and real time result using DSP TMS320F2812 connected to DS1103 card verify the previously formed structure.
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LABEX8 doc

LABEX8 DOC

observe that the simulation is _________ correct.Q8.5 The two different partial- fraction expansions of the transfer function generated by Program P8_1 obtained using Program P6_2 are given below :The MATLAB program for the simulation of the Parallel Form I realization based on the partial- fraction expansion in z–1 with each section implemented in Direct Form II is given below :< Insert program code here. Copy from m- file(s) and paste. >Using strucver, from the impulse response of the simulated cascade form we observe that the simulation is _________ correct.Q8.6 The MATLAB program for the simulation of the Parallel Form II realization based on the partial- fraction expansion in z with each section implemented in Direct Form II is given below :< Insert program code here. Copy from m- file(s) and paste. >Using strucver, from the impulse response of the simulated cascade form we observe that the simulation is _________ correct.Q8.7 The data generated by running Program P8_2 are -From the above results we conclude that the simulation is _________ correct.2Q8.8 The transfer function of the elliptic lowpass filter meeting the specifications of Question Q8.8 and generated using MATLAB is as given below :The Gray- Markel cascaded lattice realization of the above transfer function obtained using Program P6_4 is shown below :The modified Program P8_2 for the simulation of the above cascaded lattice realization is given below :< Insert program code here. Copy from m- file(s) and paste. >The data generated by running modified Program P8_2 are -From the above results we conclude that the simulation is _________ correct.Project 8.2 Illustr at ion of Filterin g
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MATLAB Primer potx

MATLAB PRIMER POTX

www.dbeBooks.com - An Ebook LibraryMATLAB® PrimerSeventh Edition CHAPMAN & HALL/CRCA CRC Press CompanyBoca Raton London New York Washington, D.C.MATLAB® PrimerSeventh EditionTimothy A. DavisKermit Sigmon This book contains information obtained from authentic and highly regardedsources. Reprinted material is quoted with permission, and sources are indi-cated. A wide variety of references are listed. Reasonable efforts have beenmade to publish reliable data and information, but the author and the publishercannot assume responsibility for the validity of all materials or for the con-sequences of their use.Neither this book nor any part may be reproduced or transmitted in any formor by any means, electronic or mechanical, including photocopying, micro-filming, and recording, or by any information storage or retrieval system,without prior permission in writing from the publisher.The consent of CRC Press does not extend to copying for general distribution,for promotion, for creating new works, or for resale. Specific permission mustbe obtained in writing from CRC Press for such copying.Direct all inquiries to CRC Press, 2000 N.W. Corporate Blvd., Boca Raton,Florida 33431.
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Smart Antenna Research Laboratory potx

SMART ANTENNA RESEARCH LABORATORY POTX

of these samples and appending them in front. A subsequent up-conversion then gives the real signal s(t) centered on the frequency cf. 2.2 FFT Implementation The first task to consider is that the OFDM spectrum is centered on cf; i.e., subcarrier 1 is 7.612 MHz to the left of the carrier and subcarrier 1,705 is 7.612 MHz to the right. One simple way to achieve the centering is to use a 2N-IFFT [2] and T/2 as the elementary period. As we can see in Table 1, the OFDM symbol duration, TU, is specified considering a 2,048-IFFT (N=2,048); therefore, we shall use a 54,096-IFFT. A block diagram of the generation of one OFDM symbol is shown in Figure 2.1 where we have indicated the variables used in the Matlab code. The next task to consider is the appropriate simulation period. T is defined as the ele-mentary period for a baseband signal, but since we are simulating a passband sig-
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MATLAB PRIMER 7TH EDITION - PREFACE

MATLAB PRIMER 7TH EDITION - PREFACE

help you begin to use MATLAB. It is not intended to be a substitute for the online help facility or the MATLAB documentation (such as Getting Started with MATLAB, available in printed form and online). The Primer can best be used hands-on. You are encouraged to work at the computer as you read the Primer and freely experiment with the examples. This Primer, along with the online help facility, usually suffices for students in a class requiring the use of MATLAB. Start with the examples at the beginning of each chapter. In this way, you will create all of the matrices and M-files used in the examples. Some examples depend on code you write in previous chapters. Larger examples (M-files and MEX-files) are on the web at http://www.cise.ufl.edu/research/sparse/MATLAB and http://www.crcpress.com. Pull-down menu selections are described using the following style. Selecting the Desktop menu, and then the Desktop Layout submenu, and then the Default vimenu item is written as
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Nuclear Power Part 7 ppt

NUCLEAR POWER PART 7 PPT

5.2.2.1 PROBABILISTIC ANALYSIS RESULTS USING MONTE CARLO SIMULATION METHOD Using the MCS probabilistic analysis method, the probabilities of limit states and the probability of failure f[r]

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Hardware Acceleration of EDA Algorithms- P7 pot

HARDWARE ACCELERATION OF EDA ALGORITHMS- P7 POT

comparing its results with a CPU-based serial fault simulator. An extended abstractof this work is available in [10].8.3 Previous WorkOver the last three decades, several research efforts have attempted to accelerate theproblem of fault simulation in a scalable and cost-effective fashion, by exploitingthe parallelism inherent in the problem These efforts can be divided into algorithmparallel, model parallel, and data parallel.Algorithm-parallel efforts aim at parallelizing the fault simulation algorithm, dis-tributing workload, and/or pipelining the tasks, such that the frequency of commu-nication and synchronization between processors is reduced [12, 6, 4, 5]. In contrastto these approaches, our approach is data parallel. In [12], the authors aim at heuris-tically assigning fault set partitions (and corresponding circuit partitions) to severalmedium-grain multiprocessors. This assignment is based on a performance modeldeveloped by comparing the communication (message passing or shared memoryaccess) to computation ratio of the multiprocessor units. The results reported in [12]are based on an implementation of fault simulation on a multiprocessor prototype122 8 Accelerating Fault Simulation Using Graphics Processorswith up to eight processing units. Our results, on the other hand, are based on off-the-shelf GPU cards (the NVIDIA GeForce GTX 280 GPU). The authors of [6]present a methodology to predict and characterize workload distribution, whichcan aid in parallelizing fault simulation. The approach discussed in [4] suggestsa pipelined design, where each functional unit performs a specific task. MARS [5],a hardware accelerator, is based on this design. However, the application of theaccelerator to fault simulation has been limited [12].In a model-parallel approach [13, 20, 12], the circuit to be simulated is partitionedinto several (possibly non-disjoint) components. Each component is assigned to oneor more processors. Further, in order to keep the partitioning balanced, dynamicre-partitioning [16, 17] is performed. This increases algorithm complexity and mayimpact simulation time [16, 17].Numerous data-parallel approaches for fault simulation have been developed in
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Programming 8 bit pic microcontrollers in C pot

PROGRAMMING 8 BIT PIC MICROCONTROLLERS IN C POT

Foreword Embedded microcontrollers are everywhere today. In the average household you will find them far beyond the obvious places like cell phones, calculators, and MP3 players. Hardly any new appliance arrives in the home without at least one controller and, most likely, there will be several—one microcontroller for the user interface (buttons and display), another to control the motor, and perhaps even an overall system manager. This applies whether the appliance in question is a washing machine, garage door opener, curling iron, or toothbrush. If the product uses a rechargeable battery, modern high density battery chemistries require intelligent chargers. A decade ago, there were significant barriers to learning how to use microcontrollers. The cheapest programmer was about a hundred dollars and application development required both erasable windowed parts—which cost about ten times the price of the one time programmable (OTP) version—and a UV Eraser to erase the windowed part. Debugging tools were the realm of professionals alone. Now most microcontrollers use Flash-based program memory that is electrically erasable. This means the device can be reprogrammed in the circuit—no UV eraser required and no special packages needed for development. The total cost to get started today is about twenty-five dollars which buys a PICkit™ 2 Starter Kit, providing programming and debugging for many Microchip Technology Inc. MCUs. Microchip Technology has always offered a free Integrated Development Environment (IDE) including an assembler and a simulator. It has never been less expensive to get started with embedded microcontrollers than it is today. While MPLAB® includes the assembler for free, assembly code is more cumbersome to write, in the first place, and also more difficult to maintain. Developing code using C frees the programmer from the details of multi-byte math and paging and generally improves code readability and maintainability. CCS and Hi-Tech both offer free “student” versions of the compiler to get started and even the full versions are relatively inexpensive once the savings in development time has been taken into account. www.newnespress.com
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