Chapter 6. PID Control
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Adaptive Digital PID Control of First‐Order‐Lag‐Plus‐Dead‐Time Dynamics with Sensor, Actuator, and Feedback Nonlineari
Received: 15 May 2019 Revised: 26 September 2019 Accepted: 1 October 2019 DOI: 10.1002/adc2.20 ORIGINAL ARTICLE Adaptive digital PID control of first-order-lag-plus-dead-time dynamics with sensor, actuator, and feedback nonlinearities Mohammadreza Kamaldar1 Syed Aseem Ul Islam1 Sneha Sanjeevini1 Ankit Goel1 Jesse B. Hoagg2 Dennis S. Bernstein1 1Department of Aerospace Engineering, University of Michigan, Ann Arbor, Abstract Michigan Proportional-integral-derivative (PID) control is one of the most widely used 2 Department of Mechanical Engineering, feedback control strategies because of its ability to follow step commands University of Kentucky, Lexington, Kentucky and reject constant disturbances with zero asymptotic error, as well as the ease of tuning. This paper presents an adaptive digital PID controller for Correspondence Dennis S. Bernstein, Department of sampled-data systems with sensor, actuator, and feedback nonlinearities. The Aerospace Engineering, 1320 Beal linear continuous-time dynamics are assumed to be first-order lag with dead Avenue, University of Michigan, Ann time (ie, delay). The plant gain is assumed to have known sign but unknown Arbor, MI 48109. Email: [email protected] magnitude, and the dead time is assumed to be unknown. The sensor and actu- ator nonlinearities are assumed to be monotonic, with known trend but are Funding information AFOSR under DDDAS, Grant/Award otherwise unknown, and the feedback nonlinearity is assumed to be monotonic, Number: FA9550-18-1-0171; ONR, but is otherwise unknown. A numerical investigation is presented to support a Grant/Award Number: N00014-18-1-2211 simulation-based conjecture, which concerns closed-loop stability and perfor- and N00014-19-1-2273 mance. -
Metaheuristic Tuning of Linear and Nonlinear PID Controllers to Nonlinear Mass Spring Damper System
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 10 (2017) pp. 2320-2328 © Research India Publications. http://www.ripublication.com Metaheuristic Tuning of Linear and Nonlinear PID Controllers to Nonlinear Mass Spring Damper System Sudarshan K. Valluru1 and Madhusudan Singh2 Department of Electrical Engineering, Delhi Technological University Bawana Road, Delhi-110042, India. 1ORCID: 0000-0002-8348-0016 Abstract difficulties such as monotonous tuning via priori knowledge into the controller. Currently several metaheuristic algorithms such This paper makes an evaluation of the metaheuristic algorithms as genetic algorithms (GA) and particle swarm optimization namely multi-objective genetic algorithm (MOGA) and (PSO) etc. have been proposed to estimate the optimised tuning adaptive particle swarm optimisation (APSO) algorithms to gains of PID controllers[4] for linear systems. But these basic determine optimal gain parameters of linear and nonlinear PID variants of GA and PSO algorithms have the tendency to controllers. The optimal gain parameters, such as Kp, Ki and Kd premature convergence for multi-dimensional systems[5]. This are applied for controlling of laboratory scaled benchmarked has been the motivation in the direction to use improved variants nonlinear mass-spring-damper (NMSD) system in order to of these algorithms, such as MOGA and APSO techniques to gauge their efficacy. A series of experimental simulation compute L-PID and NL-PID gains according to the bench scaled results reveals that the performance of the APSO tuned NMSD system, such that premature convergence and tedious tunings are diminished. In recent past several bio-inspired nonlinear PID controller is better in the aspects of system metaheuristics such as GA and PSO used for tuning of overshoot, steady state error, settling time of the trajectory for conventional PI and PID controllers [6]–[10]. -
Application of the Pid Control to the Programmable Logic Controller Course
AC 2009-955: APPLICATION OF THE PID CONTROL TO THE PROGRAMMABLE LOGIC CONTROLLER COURSE Shiyoung Lee, Pennsylvania State University, Berks Page 14.224.1 Page © American Society for Engineering Education, 2009 Application of the PID Control to the Programmable Logic Controller Course Abstract The proportional, integral, and derivative (PID) control is the most widely used control technique in the automation industries. The importance of the PID control is emphasized in various automatic control courses. This topic could easily be incorporated into the programmable logic controller (PLC) course with both static and dynamic teaching components. In this paper, the integration of the PID function into the PLC course is described. The proposed new PID teaching components consist of an oven heater and a light dimming control as the static applications, and the closed-loop velocity control of a permanent magnet DC motor (PMDCM) as the dynamic application. The RSLogix500 ladder logic programming software from Rockwell Automation has the PID function. After the theoretical background of the PID control is discussed, the PID function of the SLC500 will be introduced. The first exercise is the pure mathematical implementation of the PID control algorithm using only mathematical PLC instructions. The Excel spreadsheet is used to verify the mathematical PID control algorithm. This will give more insight into the PID control. Following this, the class completes the exercise with the PID instruction in RSLogix500. Both methods will be compared in terms of speed, complexity, and accuracy. The laboratory assignments in controlling the oven heater temperature and dimming the lamp are given to the students so that they experience the effectiveness of the PID control. -
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......................................................................................... -
Internal Model Control Approach to PI Tunning in Vector Control of Induction Motor
International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 4 Issue 06, June-2015 Internal Model Control Approach to PI Tunning in Vector Control of Induction Motor Vipul G. Pagrut, Ragini V. Meshram, Bharat N. Gupta , Pranao Walekar Department of Electrical Engineering Veermata Jijabai Technological Institute Mumbai, India Abstract— This paper deals with the design of PI controllers 휓푟 Space phasor of rotor flux linkages to meet the desired torque and speed response of Induction motor 휓푑푟 Direct axis rotor flux linkages drive. Vector control oriented technique controls the speed and 휓 Quadrature axis flux linkages torque of induction motor separately. This algorithm is executed 푞푟 in two control loops, the current loop is fast innermost and outer 푇푟 Rotor time constant speed loop is slow compared to inner loop. Fast control loop 퐽 Motor Inertia executes two independent current control loop, are direct and quadrature axis current PI controllers. Direct axis current is used to control rotor magnetizing flux and quadrature axis II. INTRODUCTION current is to control motor torque. If the acceleration or HIGH-SPEED capability machines are needed in numerous deceleration rate is high and the flux variation is fast, then the applications for example, pumps, ac servo, traction and shaft flux of the induction machine is controlled instantaneously. For drive. In Induction Motor this can be easily achieved by means this the flux regulator is incorporated in the control loop of the of vector Control/field orientation control methodology [6]. induction machine. For small flux and rapid variation of speed it And hence Field orientation control of an Induction Motor is is difficult to obtained maximum torque. -
Control Theory
Control theory S. Simrock DESY, Hamburg, Germany Abstract In engineering and mathematics, control theory deals with the behaviour of dynamical systems. The desired output of a system is called the reference. When one or more output variables of a system need to follow a certain ref- erence over time, a controller manipulates the inputs to a system to obtain the desired effect on the output of the system. Rapid advances in digital system technology have radically altered the control design options. It has become routinely practicable to design very complicated digital controllers and to carry out the extensive calculations required for their design. These advances in im- plementation and design capability can be obtained at low cost because of the widespread availability of inexpensive and powerful digital processing plat- forms and high-speed analog IO devices. 1 Introduction The emphasis of this tutorial on control theory is on the design of digital controls to achieve good dy- namic response and small errors while using signals that are sampled in time and quantized in amplitude. Both transform (classical control) and state-space (modern control) methods are described and applied to illustrative examples. The transform methods emphasized are the root-locus method of Evans and fre- quency response. The state-space methods developed are the technique of pole assignment augmented by an estimator (observer) and optimal quadratic-loss control. The optimal control problems use the steady-state constant gain solution. Other topics covered are system identification and non-linear control. System identification is a general term to describe mathematical tools and algorithms that build dynamical models from measured data. -
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 -
Eagle: Tcl Implementation in C
Eagle: Tcl Implementation in C# Joe Mistachkin <[email protected]> 1. Abstract Eagle [1], Extensible Adaptable Generalized Logic Engine, is an implementation of the Tcl [2] scripting language for the Microsoft Common Language Runtime (CLR) [3]. It is designed to be a universal scripting solution for any CLR based language, and is written completely in C# [4]. Su- perficially, it is similar to Jacl [5], but it was written from scratch based on the design and imple- mentation of Tcl 8.4 [6]. It provides most of the functionality of the Tcl 8.4 interpreter while bor- rowing selected features from Tcl 8.5 [7] and the upcoming Tcl 8.6 [8] in addition to adding en- tirely new features. This paper explains how Eagle adds value to both Tcl/Tk and CLR-based applications and how it differs from other “dynamic languages” hosted by the CLR and its cousin, the Microsoft Dy- namic Language Runtime (DLR) [9]. It then describes how to use, integrate with, and extend Ea- gle effectively. It also covers some important implementation details and the overall design phi- losophy behind them. 2. Introduction This paper presents Eagle, which is an open-source [10] implementation of Tcl for the Microsoft CLR written entirely in C#. The goal of this project was to create a dynamic scripting language that could be used to automate any host application running on the CLR. 3. Rationale and Motivation Tcl makes it relatively easy to script applications written in C [11] and/or C++ [12] and so can also script applications written in many other languages (e.g. -
Preview Tcl-Tk Tutorial (PDF Version)
Tcl/Tk About the Tutorial Tcl is a general purpose multi-paradigm system programming language. It is a scripting language that aims at providing the ability for applications to communicate with each other. On the other hand, Tk is a cross platform widget toolkit used for building GUI in many languages. This tutorial covers various topics ranging from the basics of the Tcl/ Tk to its scope in various applications. Audience This tutorial is designed for all those individuals who are looking for a starting point of learning Tcl/ Tk. Therefore, we cover all those topics that are required for a beginner and an advanced user. Prerequisites Before proceeding with this tutorial, it is advisable for you to understand the basic concepts of computer programming. This tutorial is self-contained and you will be able to learn various concepts of Tcl/Tk even if you are a beginner. You just need to have a basic understanding of working with a simple text editor and command line. Disclaimer & Copyright Copyright 2015 by Tutorials Point (I) Pvt. Ltd. All the content and graphics published in this e-book are the property of Tutorials Point (I) Pvt. Ltd. The user of this e-book is prohibited to reuse, retain, copy, distribute, or republish any contents or a part of contents of this e-book in any manner without written consent of the publisher. We strive to update the contents of our website and tutorials as timely and as precisely as possible, however, the contents may contain inaccuracies or errors. Tutorials Point (I) Pvt. -
Python and Epics: Channel Access Interface to Python
Python and Epics: Channel Access Interface to Python Matthew Newville Consortium for Advanced Radiation Sciences University of Chicago October 12, 2010 http://cars9.uchicago.edu/software/python/pyepics3/ Matthew Newville (CARS, Univ Chicago) Epics and Python October 12, 2010 Why Python? The Standard Answers Clean Syntax Easy to learn, remember, and read High Level Language No pointers, dynamic memory, automatic memory Cross Platform code portable to Unix, Windows, Mac. Object Oriented full object model, name spaces. Also: procedural! Extensible with C, C++, Fortran, Java, .NET Many Libraries GUIs, Databases, Web, Image Processing, Array math Free Both senses of the word. No, really: completely free. Matthew Newville (CARS, Univ Chicago) Epics and Python October 12, 2010 Why Python? The Real Answer Scientists use Python. Matthew Newville (CARS, Univ Chicago) Epics and Python October 12, 2010 All of these tools use the C implementation of Python. NOT Jython (Python in Java) or IronPython (Python in .NET): I am not talking about Jython. Why Do Scientists Use Python? Python is great. The tools are even better: numpy Fast arrays. matplotlib Excellent Plotting library scipy Numerical Algorithms (FFT, lapack, fitting, . ) f2py Wrapping Fortran for Python sage Symbolic math (ala Maple, Mathematica) GUI Choices Tk, wxWidgets, Qt, . Free Python is Free. All these tools are Free (BSD). Matthew Newville (CARS, Univ Chicago) Epics and Python October 12, 2010 Why Do Scientists Use Python? Python is great. The tools are even better: numpy Fast arrays. matplotlib Excellent Plotting library scipy Numerical Algorithms (FFT, lapack, fitting, . ) f2py Wrapping Fortran for Python sage Symbolic math (ala Maple, Mathematica) GUI Choices Tk, wxWidgets, Qt, . -
Introduction to Feedback Control
ECE4510/5510: Feedback Control Systems. 1–1 INTRODUCTION TO FEEDBACK CONTROL 1.1: What is feedback control? I Control-system engineers often face this question (or, “What is it that you do, anyway?”) when trying to explain their professional field. I Loosely speaking, control is the process of getting “something” to do what you want it to do (or “not do,” as the case may be). The “something” can be almost anything. Some obvious • examples: aircraft, spacecraft, cars, machines, robots, radars, telescopes, etc. Some less obvious examples: energy systems, the economy, • biological systems, the human body. I Control is a very common concept. e.g.,Human-machineinteraction:Drivingacar. ® Manual control. e.g.,Independentmachine:Roomtemperaturecontrol.Furnacein winter, air conditioner in summer. Both controlled (turned “on”/“off”) by thermostat. (We’ll look at this example more in Topic 1.2.) ® Automatic control (our focus in this course). DEFINITION: Control is the process of causing a system variable to conform to some desired value, called a reference value. (e.g., variable temperature for a climate-control system) = Lecture notes prepared by and copyright c 1998–2013, Gregory L. Plett and M. Scott Trimboli ! ECE4510/ECE5510, INTRODUCTION TO FEEDBACK CONTROL 1–2 tp Mp I Usually defined in terms 1 0.9 of the system’s step re- sponse, as we’ll see in notes Chapter 3. 0.1 t tr ts DEFINITION: Feedback is the process of measuring the controlled variable (e.g.,temperature)andusingthatinformationtoinfluencethe value of the controlled variable. I Feedback is not necessary for control. But, it is necessary to cater for system uncertainty, which is the principal role of feedback. -
RAND Suicide Prevention Program Evaluation TOOLKIT
CHILDREN AND FAMILIES The RAND Corporation is a nonprofit institution that helps improve policy and EDUCATION AND THE ARTS decisionmaking through research and analysis. ENERGY AND ENVIRONMENT HEALTH AND HEALTH CARE This electronic document was made available from www.rand.org as a public service INFRASTRUCTURE AND of the RAND Corporation. TRANSPORTATION INTERNATIONAL AFFAIRS LAW AND BUSINESS Skip all front matter: Jump to Page 16 NATIONAL SECURITY POPULATION AND AGING PUBLIC SAFETY Support RAND SCIENCE AND TECHNOLOGY Purchase this document TERRORISM AND Browse Reports & Bookstore HOMELAND SECURITY Make a charitable contribution For More Information Visit RAND at www.rand.org Explore the RAND National Defense Research Institute View document details Limited Electronic Distribution Rights This document and trademark(s) contained herein are protected by law as indicated in a notice appearing later in this work. This electronic representation of RAND intellectual property is provided for non- commercial use only. Unauthorized posting of RAND electronic documents to a non-RAND website is prohibited. RAND electronic documents are protected under copyright law. Permission is required from RAND to reproduce, or reuse in another form, any of our research documents for commercial use. For information on reprint and linking permissions, please see RAND Permissions. This report is part of the RAND Corporation tool series. RAND tools may include models, databases, calculators, computer code, GIS mapping tools, practitioner guide- lines, web applications, and various toolkits. All RAND tools undergo rigorous peer review to ensure both high data standards and appropriate methodology in keeping with RAND’s commitment to quality and objectivity. RAND Suicide Prevention Program Evaluation TOOLKIT Joie D.