Introduction to Linear, Time-Invariant, Dynamic Systems for Students of Engineering
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2.161 Signal Processing: Continuous and Discrete Fall 2008
MIT OpenCourseWare http://ocw.mit.edu 2.161 Signal Processing: Continuous and Discrete Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. Massachusetts Institute of Technology Department of Mechanical Engineering 2.161 Signal Processing - Continuous and Discrete Fall Term 2008 1 Lecture 1 Reading: • Class handout: The Dirac Delta and Unit-Step Functions 1 Introduction to Signal Processing In this class we will primarily deal with processing time-based functions, but the methods will also be applicable to spatial functions, for example image processing. We will deal with (a) Signal processing of continuous waveforms f(t), using continuous LTI systems (filters). a LTI dy nam ical system input ou t pu t f(t) Continuous Signal y(t) Processor and (b) Discrete-time (digital) signal processing of data sequences {fn} that might be samples of real continuous experimental data, such as recorded through an analog-digital converter (ADC), or implicitly discrete in nature. a LTI dis crete algorithm inp u t se q u e n c e ou t pu t seq u e n c e f Di screte Si gnal y { n } { n } Pr ocessor Some typical applications that we look at will include (a) Data analysis, for example estimation of spectral characteristics, delay estimation in echolocation systems, extraction of signal statistics. (b) Signal enhancement. Suppose a waveform has been contaminated by additive “noise”, for example 60Hz interference from the ac supply in the laboratory. 1copyright c D.Rowell 2008 1–1 inp u t ou t p u t ft + ( ) Fi lte r y( t) + n( t) ad d it ive no is e The task is to design a filter that will minimize the effect of the interference while not destroying information from the experiment. -
Quantum Phase Space in Relativistic Theory: the Case of Charge-Invariant Observables
Proceedings of Institute of Mathematics of NAS of Ukraine 2004, Vol. 50, Part 3, 1448–1453 Quantum Phase Space in Relativistic Theory: the Case of Charge-Invariant Observables A.A. SEMENOV †, B.I. LEV † and C.V. USENKO ‡ † Institute of Physics of NAS of Ukraine, 46 Nauky Ave., 03028 Kyiv, Ukraine E-mail: [email protected], [email protected] ‡ Physics Department, Taras Shevchenko Kyiv University, 6 Academician Glushkov Ave., 03127 Kyiv, Ukraine E-mail: [email protected] Mathematical method of quantum phase space is very useful in physical applications like quantum optics and non-relativistic quantum mechanics. However, attempts to generalize it for the relativistic case lead to some difficulties. One of problems is band structure of energy spectrum for a relativistic particle. This corresponds to an internal degree of freedom, so- called charge variable. In physical problems we often deal with such dynamical variables that do not depend on this degree of freedom. These are position, momentum, and any combination of them. Restricting our consideration to this kind of observables we propose the relativistic Weyl–Wigner–Moyal formalism that contains some surprising differences from its non-relativistic counterpart. This paper is devoted to the phase space formalism that is specific representation of quan- tum mechanics. This representation is very close to classical mechanics and its basic idea is a description of quantum observables by means of functions in phase space (symbols) instead of operators in the Hilbert space of states. The first idea about this representation has been proposed in the early days of quantum mechanics in the well-known Weyl work [1]. -
Control Engineering for High School Students and Teachers: an Online Platform Development
Control Engineering for High School Students and Teachers: An Online Platform Development Farhad Farokhi and Iman Shames Contents Report ............................................ 1 1 Introduction . 1 2 Courses . 1 3 Interviews . 2 4 Educational games . 3 5 Remote laboratory . 4 6 Conclusions and future work . 5 Appendix .......................................... 6 A Example course 1: Feedback theory . 6 B Example course 2: Models . 9 C Example course 3: On/off control . 12 D Remote laboratory (RLAB) manual . 13 1 Introduction Based on our teen years and feedback from many of our colleagues and friends, we believe that the control engineering, although being a major building block of automated system in many processes and infrastructures, is a fairly alien subject to the students, parents, and teachers. Therefore, there is a need for introducing feedback control and its application to high school students and their teachers to recruit the next generation of engineers and scientists in this field. We also believe that the academic community has a responsibility to disseminate the information cheaply, if not freely, to a wide range of interested audience, be it students, parents, or teachers, across the globe. This way, we can guarantee that people from different socio- economic backgrounds and in different countries can make informed decisions regarding their careers and those of their friends and families. Motivated by these needs, in this project, we have attempted at developing an online platform for the students and their educators to read about the control engineering, watch lectures by researchers from academia and industry, access interviews with successful people in the control engineering community, and play online games to test their understanding and to possibly learn about the applications of the automatic control. -
Spacetime Diagrams(1D in Space)
PH300 Modern Physics SP11 Last time: • Time dilation and length contraction Today: • Spacetime • Addition of velocities • Lorentz transformations Thursday: • Relativistic momentum and energy “The only reason for time is so that HW03 due, beginning of class; HW04 assigned everything doesn’t happen at once.” 2/1 Day 6: Next week: - Albert Einstein Questions? Intro to quantum Spacetime Thursday: Exam I (in class) Addition of Velocities Relativistic Momentum & Energy Lorentz Transformations 1 2 Spacetime Diagrams (1D in space) Spacetime Diagrams (1D in space) c · t In PHYS I: v In PH300: x x x x Δx Δx v = /Δt Δt t t Recall: Lucy plays with a fire cracker in the train. (1D in space) Spacetime Diagrams Ricky watches the scene from the track. c· t In PH300: object moving with 0<v<c. ‘Worldline’ of the object L R -2 -1 0 1 2 x object moving with 0>v>-c v c·t c·t Lucy object at rest object moving with v = -c. at x=1 x=0 at time t=0 -2 -1 0 1 2 x -2 -1 0 1 2 x Ricky 1 Example: Ricky on the tracks Example: Lucy in the train ct ct Light reaches both walls at the same time. Light travels to both walls Ricky concludes: Light reaches left side first. x x L R L R Lucy concludes: Light reaches both sides at the same time In Ricky’s frame: Walls are in motion In Lucy’s frame: Walls are at rest S Frame S’ as viewed from S ... -3 -2 -1 0 1 2 3 .. -
Lecture 2 LSI Systems and Convolution in 1D
Lecture 2 LSI systems and convolution in 1D 2.1 Learning Objectives Understand the concept of a linear system. • Understand the concept of a shift-invariant system. • Recognize that systems that are both linear and shift-invariant can be described • simply as a convolution with a certain “impulse response” function. 2.2 Linear Systems Figure 2.1: Example system H, which takes in an input signal f(x)andproducesan output g(x). AsystemH takes an input signal x and produces an output signal y,whichwecan express as H : f(x) g(x). This very general definition encompasses a vast array of di↵erent possible systems.! A subset of systems of particular relevance to signal processing are linear systems, where if f (x) g (x)andf (x) g (x), then: 1 ! 1 2 ! 2 a f + a f a g + a g 1 · 1 2 · 2 ! 1 · 1 2 · 2 for any input signals f1 and f2 and scalars a1 and a2. In other words, for a linear system, if we feed it a linear combination of inputs, we get the corresponding linear combination of outputs. Question: Why do we care about linear systems? 13 14 LECTURE 2. LSI SYSTEMS AND CONVOLUTION IN 1D Question: Can you think of examples of linear systems? Question: Can you think of examples of non-linear systems? 2.3 Shift-Invariant Systems Another subset of systems we care about are shift-invariant systems, where if f1 g1 and f (x)=f (x x )(ie:f is a shifted version of f ), then: ! 2 1 − 0 2 1 f (x) g (x)=g (x x ) 2 ! 2 1 − 0 for any input signals f1 and shift x0. -
EE C128 Chapter 10
Lecture abstract EE C128 / ME C134 – Feedback Control Systems Topics covered in this presentation Lecture – Chapter 10 – Frequency Response Techniques I Advantages of FR techniques over RL I Define FR Alexandre Bayen I Define Bode & Nyquist plots I Relation between poles & zeros to Bode plots (slope, etc.) Department of Electrical Engineering & Computer Science st nd University of California Berkeley I Features of 1 -&2 -order system Bode plots I Define Nyquist criterion I Method of dealing with OL poles & zeros on imaginary axis I Simple method of dealing with OL stable & unstable systems I Determining gain & phase margins from Bode & Nyquist plots I Define static error constants September 10, 2013 I Determining static error constants from Bode & Nyquist plots I Determining TF from experimental FR data Bayen (EECS, UCB) Feedback Control Systems September 10, 2013 1 / 64 Bayen (EECS, UCB) Feedback Control Systems September 10, 2013 2 / 64 10 FR techniques 10.1 Intro Chapter outline 1 10 Frequency response techniques 1 10 Frequency response techniques 10.1 Introduction 10.1 Introduction 10.2 Asymptotic approximations: Bode plots 10.2 Asymptotic approximations: Bode plots 10.3 Introduction to Nyquist criterion 10.3 Introduction to Nyquist criterion 10.4 Sketching the Nyquist diagram 10.4 Sketching the Nyquist diagram 10.5 Stability via the Nyquist diagram 10.5 Stability via the Nyquist diagram 10.6 Gain margin and phase margin via the Nyquist diagram 10.6 Gain margin and phase margin via the Nyquist diagram 10.7 Stability, gain margin, and -
Reflection Invariant and Symmetry Detection
1 Reflection Invariant and Symmetry Detection Erbo Li and Hua Li Abstract—Symmetry detection and discrimination are of fundamental meaning in science, technology, and engineering. This paper introduces reflection invariants and defines the directional moments(DMs) to detect symmetry for shape analysis and object recognition. And it demonstrates that detection of reflection symmetry can be done in a simple way by solving a trigonometric system derived from the DMs, and discrimination of reflection symmetry can be achieved by application of the reflection invariants in 2D and 3D. Rotation symmetry can also be determined based on that. Also, if none of reflection invariants is equal to zero, then there is no symmetry. And the experiments in 2D and 3D show that all the reflection lines or planes can be deterministically found using DMs up to order six. This result can be used to simplify the efforts of symmetry detection in research areas,such as protein structure, model retrieval, reverse engineering, and machine vision etc. Index Terms—symmetry detection, shape analysis, object recognition, directional moment, moment invariant, isometry, congruent, reflection, chirality, rotation F 1 INTRODUCTION Kazhdan et al. [1] developed a continuous measure and dis- The essence of geometric symmetry is self-evident, which cussed the properties of the reflective symmetry descriptor, can be found everywhere in nature and social lives, as which was expanded to 3D by [2] and was augmented in shown in Figure 1. It is true that we are living in a spatial distribution of the objects asymmetry by [3] . For symmetric world. Pursuing the explanation of symmetry symmetry discrimination [4] defined a symmetry distance will provide better understanding to the surrounding world of shapes. -
Introduction to Aircraft Aeroelasticity and Loads
JWBK209-FM-I JWBK209-Wright November 14, 2007 2:58 Char Count= 0 Introduction to Aircraft Aeroelasticity and Loads Jan R. Wright University of Manchester and J2W Consulting Ltd, UK Jonathan E. Cooper University of Liverpool, UK iii JWBK209-FM-I JWBK209-Wright November 14, 2007 2:58 Char Count= 0 Introduction to Aircraft Aeroelasticity and Loads i JWBK209-FM-I JWBK209-Wright November 14, 2007 2:58 Char Count= 0 ii JWBK209-FM-I JWBK209-Wright November 14, 2007 2:58 Char Count= 0 Introduction to Aircraft Aeroelasticity and Loads Jan R. Wright University of Manchester and J2W Consulting Ltd, UK Jonathan E. Cooper University of Liverpool, UK iii JWBK209-FM-I JWBK209-Wright November 14, 2007 2:58 Char Count= 0 Copyright C 2007 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England Telephone (+44) 1243 779777 Email (for orders and customer service enquiries): [email protected] Visit our Home Page on www.wileyeurope.com or www.wiley.com All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailed to [email protected], or faxed to (+44) 1243 770620. -
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Memorial Tributes: Volume 3 HENDRIK WADE BODE 50 Copyright National Academy of Sciences. All rights reserved. Memorial Tributes: Volume 3 HENDRIK WADE BODE 51 Hendrik Wade Bode 1905–1982 By Harvey Brooks Hendrik Wade Bode was widely known as one of the most articulate, thoughtful exponents of the philosophy and practice of systems engineering—the science and art of integrating technical components into a coherent system that is optimally adapted to its social function. After a career of more than forty years with Bell Telephone Laboratories, which he joined shortly after its founding in 1926, Dr. Bode retired in 1967 to become Gordon McKay Professor of Systems Engineering (on a half-time basis) in what was then the Division of Engineering and Applied Physics at Harvard. He became professor emeritus in July 1974. He died at his home in Cambridge on June 21, 1982, at the age of seventy- six. He is survived by his wife, Barbara Poore Bode, whom he married in 1933, and by two daughters, Dr. Katharine Bode Darlington of Philadelphia and Mrs. Anne Hathaway Bode Aarnes of Washington, D.C. Hendrik Bode was born in Madison, Wisconsin, on December 24, 1905. After attending grade school in Tempe, Arizona, and high school in Urbana, Illinois, he went on to Ohio State University, from which he received his B.A. in 1924 and his M.A. in 1926, both in mathematics. He joined Bell Labs in 1926 to work on electrical network theory and the design of electric filters. While at Bell, he also pursued graduate studies at Columbia University, receiving his Ph.D. -
Quasilinear Control of Systems with Time-Delays and Nonlinear Actuators and Sensors Wei-Ping Huang University of Vermont
University of Vermont ScholarWorks @ UVM Graduate College Dissertations and Theses Dissertations and Theses 2018 Quasilinear Control of Systems with Time-Delays and Nonlinear Actuators and Sensors Wei-Ping Huang University of Vermont Follow this and additional works at: https://scholarworks.uvm.edu/graddis Part of the Electrical and Electronics Commons Recommended Citation Huang, Wei-Ping, "Quasilinear Control of Systems with Time-Delays and Nonlinear Actuators and Sensors" (2018). Graduate College Dissertations and Theses. 967. https://scholarworks.uvm.edu/graddis/967 This Thesis is brought to you for free and open access by the Dissertations and Theses at ScholarWorks @ UVM. It has been accepted for inclusion in Graduate College Dissertations and Theses by an authorized administrator of ScholarWorks @ UVM. For more information, please contact [email protected]. Quasilinear Control of Systems with Time-Delays and Nonlinear Actuators and Sensors A Thesis Presented by Wei-Ping Huang to The Faculty of the Graduate College of The University of Vermont In Partial Fulfillment of the Requirements for the Degree of Master of Science Specializing in Electrical Engineering October, 2018 Defense Date: July 25th, 2018 Thesis Examination Committee: Hamid R. Ossareh, Ph.D., Advisor Eric M. Hernandez, Ph.D., Chairperson Jeff Frolik, Ph.D. Cynthia J. Forehand, Ph.D., Dean of Graduate College Abstract This thesis investigates Quasilinear Control (QLC) of time-delay systems with non- linear actuators and sensors and analyzes the accuracy of stochastic linearization for these systems. QLC leverages the method of stochastic linearization to replace each nonlinearity with an equivalent gain, which is obtained by solving a transcendental equation. -
Applying Classical Control Theory to an Airplane Flap Model on Real Physical Hardware
Applying Classical Control Theory to an Airplane Flap Model on Real Physical Hardware Edgar Collado Alvarez, BSIE, EIT, Janice Valentin, Jonathan Holguino ME.ME in Design and Controls Polytechnic University of Puerto Rico, [email protected],, [email protected], [email protected] Mentor: Prof. Bernardo Restrepo, Ph.D, PE in Aerospace and Controls Polytechnic University of Puerto Rico Hato Rey Campus, [email protected] Abstract — For the trimester of Winter -14 at Polytechnic University of Puerto Rico, the course Applied Control Systems was offered to students interested in further development and integration in the area of Control Engineering. Students were required to develop a control system utilizing a microprocessor as the controlling hardware. An Arduino Mega board was chosen as the microprocessor for the job. Integration of different mechanical, electrical, and electromechanical parts such as potentiometers, sensors, servo motors, etc., with the microprocessor were the main focus in developing the physical model to be controlled. This paper details the development of one of these control system projects applied to an airplane flap. After physically constructing and integrating additional parts, control was programmed in Simulink environment and further downloads to the microprocessor. MATLAB’s System Identification Tool was used to model the physical system’s transfer function based on input and output data from the physical plant. Tested control types utilized for system performance improvement were proportional (P) and proportional-integrative (PI) control. Key Words — embedded control systems, proportional integrative (PI) control, MATLAB, Simulink, Arduino. I. INTRODUCTION II. FIRST PHASE - POTENTIOMETER AND SERVO MOTOR CONTROL During the master’s course of Mechanical and Aerospace Control System, students were interested in The team started in the development of a potentiometer and developing real-world models with physical components servo motor control circuit. -
Basic Four-Momentum Kinematics As
L4:1 Basic four-momentum kinematics Rindler: Ch5: sec. 25-30, 32 Last time we intruduced the contravariant 4-vector HUB, (II.6-)II.7, p142-146 +part of I.9-1.10, 154-162 vector The world is inconsistent! and the covariant 4-vector component as implicit sum over We also introduced the scalar product For a 4-vector square we have thus spacelike timelike lightlike Today we will introduce some useful 4-vectors, but rst we introduce the proper time, which is simply the time percieved in an intertial frame (i.e. time by a clock moving with observer) If the observer is at rest, then only the time component changes but all observers agree on ✁S, therefore we have for an observer at constant speed L4:2 For a general world line, corresponding to an accelerating observer, we have Using this it makes sense to de ne the 4-velocity As transforms as a contravariant 4-vector and as a scalar indeed transforms as a contravariant 4-vector, so the notation makes sense! We also introduce the 4-acceleration Let's calculate the 4-velocity: and the 4-velocity square Multiplying the 4-velocity with the mass we get the 4-momentum Note: In Rindler m is called m and Rindler's I will always mean with . which transforms as, i.e. is, a contravariant 4-vector. Remark: in some (old) literature the factor is referred to as the relativistic mass or relativistic inertial mass. L4:3 The spatial components of the 4-momentum is the relativistic 3-momentum or simply relativistic momentum and the 0-component turns out to give the energy: Remark: Taylor expanding for small v we get: rest energy nonrelativistic kinetic energy for v=0 nonrelativistic momentum For the 4-momentum square we have: As you may expect we have conservation of 4-momentum, i.e.