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J-DSP Lab 2: the Z-Transform and Frequency Responses
J-DSP Lab 2: The Z-Transform and Frequency Responses Introduction This lab exercise will cover the Z transform and the frequency response of digital filters. The goal of this exercise is to familiarize you with the utility of the Z transform in digital signal processing. The Z transform has a similar role in DSP as the Laplace transform has in circuit analysis: a) It provides intuition in certain cases, e.g., pole location and filter stability, b) It facilitates compact signal representations, e.g., certain deterministic infinite-length sequences can be represented by compact rational z-domain functions, c) It allows us to compute signal outputs in source-system configurations in closed form, e.g., using partial functions to compute transient and steady state responses. d) It associates intuitively with frequency domain representations and the Fourier transform In this lab we use the Filter block of J-DSP to invert the Z transform of various signals. As we have seen in the previous lab, the Filter block in J-DSP can implement a filter transfer function of the following form 10 −i ∑bi z i=0 H (z) = 10 − j 1+ ∑ a j z j=1 This is essentially realized as an I/P-O/P difference equation of the form L M y(n) = ∑∑bi x(n − i) − ai y(n − i) i==01i The transfer function is associated with the impulse response and hence the output can also be written as y(n) = x(n) * h(n) Here, * denotes convolution; x(n) and y(n) are the input signal and output signal respectively. -
Basic Nonlinear Control Systems - D
CONTROL SYSTEMS, ROBOTICS, AND AUTOMATION – Vol. III – Basic Nonlinear Control Systems - D. P. Atherton BASIC NONLINEAR CONTROL SYSTEMS D. P. Atherton University of Sussex, UK Keywords: Nonlinearity, friction, saturation, relay, backlash, frequency response, describing function, limit cycle, stability, phase plane, subharmonics, chaos, synchronization, compensation. Contents 1. Introduction 2. Forms of nonlinearity 3. Structure and behavior 4. Stability 5. Aspects of design 6. Conclusions Glossary Bibliography Biographical Sketch Summary This section is written to introduce the reader to nonlinearity and its effect in control systems, and also to ‘set the stage’ for the subsequent articles. Nonlinearity is first defined and then some of the physical effects which cause nonlinear behavior are discussed. This is followed by some comments on the stability of nonlinear systems, the existence of limit cycles, and the unique behavioral aspects which nonlinear feedback systems may possess. Finally a few brief comments are made on designing controllers for nonlinear systems. 1. Introduction Linear systems have the important property that they satisfy the superposition principle. This leads to many important advantages in methods for their analysis. For example, in circuit theory when an RLC circuit has both d.c and a.c input voltages, the voltage or current elsewhereUNESCO in the circuit can be– found EOLSS by summing the results of separate analyses for the d.c. and a.c. inputs taken individually, also if the magnitude of the a.c. voltage is doubledSAMPLE then the a.c. voltages or CHAPTERScurrents elsewhere in the circuit will be doubled. Thus, mathematically a linear system with input x()t and output yt() satisfies the property that the output for an input ax12() t+ bx () t is ay12() t+ by () t , if y1()t and y2 ()t are the outputs in response to the inputs x1()t and x2 ()t , respectively, and a and b are constants. -
Metallurgical Engineering
ENSURING THE EXPERTISE TO GROW SOUTH AFRICA Discipline-specific Training Guideline for Candidate Engineers in Metallurgical Engineering R-05-MET-PE Revision No.: 2: 25 July 2019 ENGINEERING COUNCIL OF SOUTH AFRICA Tel: 011 6079500 | Fax: 011 6229295 Email: [email protected] | Website: www.ecsa.co.za ENSURING THE Document No.: Effective Date: Revision No.: 2 R-05-MET-PE 25/07/2019 Subject: Discipline-specific Training Guideline for Candidate Engineers in Metallurgical Engineering Compiler: Approving Officer: Next Review Date: Page 2 of 46 MB Mtshali EL Nxumalo 25/07/2023 TABLE OF CONTENTS DEFINITIONS ............................................................................................................................ 3 BACKGROUND ......................................................................................................................... 5 1. PURPOSE OF THIS DOCUMENT ......................................................................................... 5 2. AUDIENCE............................................................................................................................. 6 3. PERSONS NOT REGISTERED AS CANDIDATES OR NOT BEING TRAINED UNDER COMMITMENT AND UNDERTAKING (C&U) ..................................................... 7 4. ORGANISING FRAMEWORK FOR OCCUPATIONS ............................................................. 7 4.1 Extractive Metallurgical Engineering ..................................................................................... 8 4.2 Mineral Processing Engineering .......................................................................................... -
Control in Robotics
Control in Robotics Mark W. Spong and Masayuki Fujita Introduction The interplay between robotics and control theory has a rich history extending back over half a century. We begin this section of the report by briefly reviewing the history of this interplay, focusing on fundamentals—how control theory has enabled solutions to fundamental problems in robotics and how problems in robotics have motivated the development of new control theory. We focus primarily on the early years, as the importance of new results often takes considerable time to be fully appreciated and to have an impact on practical applications. Progress in robotics has been especially rapid in the last decade or two, and the future continues to look bright. Robotics was dominated early on by the machine tool industry. As such, the early philosophy in the design of robots was to design mechanisms to be as stiff as possible with each axis (joint) controlled independently as a single-input/single-output (SISO) linear system. Point-to-point control enabled simple tasks such as materials transfer and spot welding. Continuous-path tracking enabled more complex tasks such as arc welding and spray painting. Sensing of the external environment was limited or nonexistent. Consideration of more advanced tasks such as assembly required regulation of contact forces and moments. Higher speed operation and higher payload-to-weight ratios required an increased understanding of the complex, interconnected nonlinear dynamics of robots. This requirement motivated the development of new theoretical results in nonlinear, robust, and adaptive control, which in turn enabled more sophisticated applications. Today, robot control systems are highly advanced with integrated force and vision systems. -
Technical Report 05-73 the Methodology Center, Pennsylvania State University
Engineering Control Approaches for the Design and Analysis of Adaptive, Time-Varying Interventions Daniel E. Rivera and Michael D. Pew Control Systems Engineering Laboratory Department of Chemical and Materials Engineering Arizona State University Tempe, Arizona 85287-6006 Linda M. Collins The Methodology Center and Department of Human Development and Family Studies Penn State University State College, PA Susan A. Murphy Institute for Social Research and Department of Statistics University of Michigan Ann Arbor, MI May 30, 2005 Technical Report 05-73 The Methodology Center, Pennsylvania State University 1 Abstract Control engineering is the field that examines how to transform dynamical system behavior from undesirable conditions to desirable ones. Cruise control in automobiles, the home thermostat, and the insulin pump are all examples of control engineering at work. In the last few decades, signifi- cant improvements in computing and information technology, increasing access to information, and novel methods for sensing and actuation have enabled the extensive application of control engi- neering concepts to physical systems. While engineering control principles are meaningful as well to problems in the behavioral sciences, the application of this topic to this field remains largely unexplored. This technical report examines how engineering control principles can play a role in the be- havioral sciences through the analysis and design of adaptive, time-varying interventions in the prevention field. The basic conceptual framework for this work draws from the paper by Collins et al. (2004). In the initial portion of the report, a general overview of control engineering principles is presented and illustrated with a simple physical example. From this description, a qualitative description of adaptive time-varying interventions as a form of classical feedback control is devel- oped, which is depicted in the form of a block diagram (i.e., a control-oriented signal and systems representation). -
Principles of Instability and Stability in Digital PID Control Strategies and Analysis for a Continuous Alcoholic Fermentation Tank Process Start-Up
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 July 2019 doi:10.20944/preprints201809.0332.v2 Principles of Instability and Stability in Digital PID Control Strategies and Analysis for a Continuous Alcoholic Fermentation Tank Process Start-up Alexandre C. B. B. Filhoa a Faculty of Chemical Engineering, Federal University of Uberlândia (UFU), Av. João Naves de Ávila 2121 Bloco 1K, Uberlândia, MG, Brazil. [email protected] Abstract The art work of this present paper is to show properly conditions and aspects to reach stability in the process control, as also to avoid instability, by using digital PID controllers, with the intention of help the industrial community. The discussed control strategy used to reach stability is to manipulate variables that are directly proportional to their controlled variables. To validate and to put the exposed principles into practice, it was done two simulations of a continuous fermentation tank process start-up with the yeast strain S. Cerevisiae NRRL-Y-132, in which the substrate feed stream contains different types of sugar derived from cane bagasse hydrolysate. These tests were carried out through the resolution of conservation laws, more specifically mass and energy balances, in which the fluid temperature and level inside the tank were the controlled variables. The results showed the facility in stabilize the system by following the exposed proceedings. Keywords: Digital PID; PID controller; Instability; Stability; Alcoholic fermentation; Process start-up. © 2019 by the author(s). Distributed under a Creative Commons CC BY license. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 July 2019 doi:10.20944/preprints201809.0332.v2 1. -
Programmable Logic Controller
Revised 10/07/19 SPECIFICATIONS - DETAILED PROVISIONS Section 17010 - Programmable Logic Controller C O N T E N T S PART 1 - GENERAL ....................................................................................................................... 1 1.01 DESCRIPTION .............................................................................................................. 1 1.02 RELATED SECTIONS ...................................................................................................... 1 1.03 REFERENCE STANDARDS AND CODES ............................................................................ 2 1.04 DEFINITIONS ............................................................................................................... 2 1.05 SUBMITTALS ............................................................................................................... 3 1.06 DESIGN REQUIREMENTS .............................................................................................. 8 1.07 INSTALLED-SPARE REQUIREMENTS ............................................................................. 13 1.08 SPARE PARTS............................................................................................................. 13 1.09 MANUFACTURER SERVICES AND COORDINATION ........................................................ 14 1.10 QUALITY ASSURANCE................................................................................................. 15 PART 2 - PRODUCTS AND MATERIALS......................................................................................... -
Neural Control for Nonlinear Dynamic Systems
Neural Control for Nonlinear Dynamic Systems Ssu-Hsin Yu Anuradha M. Annaswamy Department of Mechanical Engineering Department of Mechanical Engineering Massachusetts Institute of Technology Massachusetts Institute of Technology Cambridge, MA 02139 Cambridge, MA 02139 Email: [email protected] Email: [email protected] Abstract A neural network based approach is presented for controlling two distinct types of nonlinear systems. The first corresponds to nonlinear systems with parametric uncertainties where the parameters occur nonlinearly. The second corresponds to systems for which stabilizing control struc tures cannot be determined. The proposed neural controllers are shown to result in closed-loop system stability under certain conditions. 1 INTRODUCTION The problem that we address here is the control of general nonlinear dynamic systems in the presence of uncertainties. Suppose the nonlinear dynamic system is described as x= f(x , u , 0) , y = h(x, u, 0) where u denotes an external input, y is the output, x is the state, and 0 is the parameter which represents constant quantities in the system. The control objectives are to stabilize the system in the presence of disturbances and to ensure that reference trajectories can be tracked accurately, with minimum delay. While uncertainties can be classified in many different ways, we focus here on two scenarios. One occurs because the changes in the environment and operating conditions introduce uncertainties in the system parameter O. As a result, control objectives such as regulation and tracking, which may be realizable using a continuous function u = J'(x, 0) cannot be achieved since o is unknown. Another class of problems arises due to the complexity of the nonlinear function f. -
Warren Mcculloch and the British Cyberneticians
Warren McCulloch and the British cyberneticians Article (Accepted Version) Husbands, Phil and Holland, Owen (2012) Warren McCulloch and the British cyberneticians. Interdisciplinary Science Reviews, 37 (3). pp. 237-253. ISSN 0308-0188 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/id/eprint/43089/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher’s version. Please see the URL above for details on accessing the published version. Copyright and reuse: Sussex Research Online is a digital repository of the research output of the University. Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available. Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. http://sro.sussex.ac.uk Warren McCulloch and the British Cyberneticians1 Phil Husbands and Owen Holland Dept. Informatics, University of Sussex Abstract Warren McCulloch was a significant influence on a number of British cyberneticians, as some British pioneers in this area were on him. -
Overview of Distributed Control Systems Formalisms 253
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by DSpace at VSB Technical University of Ostrava Overview of distributed control systems formalisms 253 OVERVIEW OF DISTRIBUTED CONTROL SYSTEMS FORMALISMS P. Holeko Department of Control and Information Systems, Faculty of Electrical Engineering, University of Žilina Univerzitná 8216/1, SK 010 26, Žilina, Slovak republic, tel.: +421 41 513 3343, e-mail: [email protected] Summary This paper discusses a chosen set of mainly object-oriented formal and semiformal methods, methodics, environments and tools for specification, analysis, modeling, simulation, verification, development and synthesis of distributed control systems (DCS). 1. INTRODUCTION interconnected by a network for the purpose of communication and monitoring. Increasing demands on technical parameters, In the next section the problem of formalizing reliability, effectivity, safety and other the processes of DCS’s life-cycle will be discussed. characteristics of industrial control systems initiate distribution of its control components across the 3. FORMAL METHODS plant. The complexity requires involving of formal The main motivations of using formal concepts methods in the process of specification, analysis, are [9]: modeling, simulation, verification, development, and ° In the process of formalizing informal in the optimal case in synthesis of such systems. requirements, ambiguities, omissions and contradictions will often be discovered; 2. DISTRIBUTED CONTROL SYSTEM ° The formal model -
'A Superb Explanatory Device': the MONIAC, an Early Hydraulic Analog Computer
‘A superb explanatory device’ The MONIAC, an early hydraulic analog computer Anna Corkhill Since 1953, the University of when it was rediscovered, partially rubber tubing and powered by a Melbourne has owned a rare restored and given a position in a mechanical pump. (In the prototype, machine: a hydraulic analog simple display case on level 1 of the the pump’s motor was recycled from computer capable of explaining the Economics and Commerce Building. a World War II Lancaster bomber, economy and performing complex The machine has recently been but the university’s MONIAC economic calculations. It is generally moved to the entrance of the new was commercially made.) It is known as the MONIAC (which Giblin Eunson Business, Economics approximately two metres high and stands for ‘MOnetary National and Education Library in the ICT one metre wide, with a metal backing Income Analog Computer’), Building, 111 Barry Street. enclosing the machine’s pump and though it has also been called Though a reasonably accurate connecting tubing. The machine has the ‘Financephalograph’, the computational device, the MONIAC’s three main water tanks, representing ‘National Income Monetary Flow key aim was to demonstrate taxes and government spending, Demonstrator’ and simply the Keynesian economic models in a savings and investment, and import– ‘Phillips Machine’, after its inventor, clear, visual way. As a pedagogical aid, export. The ‘active balances’ tank at Bill Phillips. The machine, one of it used coloured water to represent the bottom represents the total stock approximately 12 ever created, was money flowing through the economy, of currency and bank credit in the purchased by the Department of and showed the relationships between economy at any given time. -
An Introduction to Control Systems; K. Warwick
An Introduction to Control Systems; K. Warwick 362 pages; World Scientific, 1996; K. Warwick; 9810225970, 9789810225971; 1996; An Introduction to Control Systems; This significantly revised edition presents a broad introduction to Control Systems and balances new, modern methods with the more classical. It is an excellent text for use as a first course in Control Systems by undergraduate students in all branches of engineering and applied mathematics. The book contains: A comprehensive coverage of automatic control, integrating digital and computer control techniques and their implementations, the practical issues and problems in Control System design; the three-term PID controller, the most widely used controller in industry today; numerous in-chapter worked examples and end-of-chapter exercises. This second edition also includes an introductory guide to some more recent developments, namely fuzzy logic control and neural networks. file download wici.pdf The Breakthrough in Artificial Intelligence; While horror films and science fiction have repeatedly warned of robots running amok, Kevin Warwick takes the threats out of the realm of fiction and into the real world, truly; Computers; K. Warwick; 307 pages; ISBN:0252072235; 1997; March of the Machines Control Bruce O. Watkins; Introduction to control systems; UOM:39015002007683; Technology & Engineering; 625 pages; 1969 Robot Control; ISBN:0863411282; Jan 1, 1988; K. Warwick, Alan Pugh; Technology & Engineering; 238 pages; Theory and Applications Automatic control; ISBN:0750622989; Davinder K. Anand; 730 pages; Since the second edition of this classic text for students and engineers appeared in 1984, the use of computer-aided design software has become an important adjunct to the; Introduction to Control Systems; Jan 1, 1995 An Introduction to Control Systems pdf download 596 pages; Mar 18, 1993; STANFORD:36105004050907; based on the proceedings of a conference on Robotics, applied mathematics and computational aspects; K.