% ****** Start of file aipsamp.tex ****** % % This file is part of the AIP files in the AIP distribution for REVTeX 4. % Version 4.1 of REVTeX, October 2009 % % Copyright (c) 2009 American Institute of Physics. % % See the AIP README file for restrictions and more information. % % TeX'ing this file requires that you have AMS-LaTeX 2.0 installed % as well as the rest of the prerequisites for REVTeX 4.1 % % It also requires running BibTeX. The commands are as follows: % % 1) latex aipsamp % 2) bibtex aipsamp % 3) latex aipsamp % 4) latex aipsamp % % Use this file as a source of example code for your aip document. % Use the file aiptemplate.tex as a template for your document. \documentclass[% aip, %jmp,% %bmf,% sd,% %rsi,% amsmath,amssymb, %preprint,% reprint,% %author-year,% author-numerical,% twoside ]{revtex4-1} \usepackage{graphicx}% Include figure files \usepackage{dcolumn}% Align table columns on decimal point \usepackage{bm}% bold math \usepackage[mathlines]{lineno}% Enable numbering of text and display math %\linenumbers\relax % Commence numbering lines \usepackage{hyperref} \usepackage{fancyhdr} \usepackage{appendix} \usepackage{ifthen} \pagestyle{fancy} \fancyhf{} %\lhead{\textit{Sample manuscript short title}} \fancyfoot[RO]{\textit{\small{McMaster Journal of Engineering Physics}, 2017,\bf{} \thepage}} \fancyfoot[LE]{\textit{\small{McMaster Journal of Engineering Physics}, 2017,\bf{} \thepage}} \renewcommand{\footrulewidth}{0.4pt} %\fancyfoot{\ifthenelse{\value{page}=1}{\small{\textit{McMaster Journal of Engineering Physics}, 2016, \bf{[vol]}, \thepage}}} \begin{document} \preprint{AIP/123-QED} \title[\textit{}]{Alternative Feedback Control Methods To Standard PID\textit{}} \author{Desmond Flowers} %only include a), b) etc if there are footnotes on the authors (see section III.3 Footnotes, and Notes and References at end \affiliation{% McMaster University, 1280 Main St. West, Hamilton, ON, Canada %\\This line break forced% with \\ }% \date{\textbf{\today}}% It is always \today, today, % but any date may be explicitly specified \begin{abstract} Feedback controllers work by stabilizing a system through one or several feedback parameters, which includes the classically employed PID controller used both throughout academia and industry. For this reason it is useful to understand how feedback controllers can be used under harsh conditions, where spikes in the readings are likely to occur due to one or many disturbances to the system [e.g.?]. A line following robot is considered under conditions of widely changing UV lighting against IR sensors, and speeds of 0 to 8 m/s, greatly challenging otherwise controllable parameters [how so?], and necessitating the need for frequent recallibration. This of itself requires thoughtful security and/or safety precautions so not to put the system at risk of damage. This paper contrasts standard PID approach with other methods like fuzzy logic, fractional order (FO) parameters, normalization based on H2 and H infinity optimization and particle swarm estimation method. [please elaborate]. [please come back to this]% Valid PACS numbers may be entered using the \verb+\pacs{#1}+ command. \end{abstract} \pacs{Valid PACS appear here}% PACS, the Physics and Astronomy % Classification Scheme. \keywords{Suggested keywords}%Use showkeys class option if keyword %display desired \maketitle \thispagestyle{fancy} \section{\label{sec:level1}Introduction} %Guessed PID constants from Ziegler–Nichols method [how so? poorly worded]. This was unsuccessful as the car would need to be retuned for different speeds and under different sunlight conditions where sensors weren't able to discern information, a requirement making the model undesirable to customers who may neither care nor understand how to properly troubleshoot. It was also diffuclt to determine the ultimate gain through trial and error as it put the car at risk of damage [adjust sentances]. Determining a reliable feedback controller is hugely important to the electronics and automation industry and heavily considered by students and intructors throughout academia. There are also extensive studies completed in this area. Standard proportional-integral-derivative (PID) controllers operate by convolving an input signal by an appropriate transfer function so to stabilize the otherwise dynamic system. This involves determining adequate parameters to stabilize the system using any one to all three error terms, including immediate errors (proportional term), historic errors (integral term) and anticipated errors (derivative term). Alternative algorithms include fuzzy and fractional order PIDs (FO-PID) which build upon the parameters of the standard PID feedback system so that it better handles harsh conditions, and other optimization methods that may operate outside the constraints of classical systems (elaborate). This paper is divided into three sections before the final remarks, including background information on alternative stabilization methods and how they may challenge the experimental results [what], a breakdown of the experiment including the acquired dataset and analysis [do something here], and discussion on the relationship between the discussed methods. This paper aims to substantiate why certain methods are prefered to others. Improved understanding of PID design impacts both education system (students and instructors), and companies that are using PID centered technology [examples?]. The ability to discern PID methods to achieve optimal performance heightens the ability for eliminating superfluous economic resources, including materials and equipment harsh on the environment [give examples], and/or training required for effective troubleshooting [more information]. In the education system, students and instructors can benefit from choosing to recentre their attention from wholly rediscovering this knowledge, to instead building upon it in spirit of challenging other areas of the curriculum [like what?]. Immediate benefits include empowering the design of existing technologies to heighten performance and reduce existing superfluous resources, and long term benefits including development of even better system stabilization strategies through ongoing application and research [ok] \section{\label{sec:Sections}Experiment} Tthe experiment was constructed from an RC drift car (Figure A) with a pre-accompanied drive and steering servo and operating speed of 0 to 8 m/s. The car included two IR LED sensor arrays and an ultrasonic sensor attached at its front end, both wired to a Raspberry Pi microntroller fixed at the top of the car and programmed in Python. The microcontroller processed the information from these sensors to direct where the car should move, or whether it should stop [clean this up]. The Zeigler-Nichols method was used to determine the parameters of the PID control, and a feedback control algorithm seen by the flowchart in Figure B [expand]. \section{\label{sec:Sections}Evaluation by Fuzzy Logic and Fractional PID} The fuzzy PID and fractional order PID (FO-PID) controllers were considered for their ability to uniquely evaluate control system parameters. The fuzzy method uses fuzzy logic to adapt the system to different environments, exchanging absolute truth and false statements by "partially true" and "partially false" statements [purhaps an image]. This can be achieved by computational methods, adapting the system to harsher conditions [relate to the experiment] FO-PID controller includes two additional parameters, which enable damping of either the derivative or integral terms of the PID, heightening reliability to harsher conditions [discern from prior method]. Both methods require deeper mathematical analysis so to achieve more reliable results. These are more appropriate in cases where PIDs are already very well understood and are otherwise already being used as they build off the standard PID. This isn't necessarily true for other feedback control methods, including H2 and H infinity control methods, and particle swarm optimization method which each constrain higher order conditions to solve nontrivial solutions [difference?]. \section{\label{sec:FigsTables}Results} \section{\label{sec:Sections}Discussion} Zeigler-Nichols method was performed in this experiment to determine first the ultimate gain, then the other terms dependent on this value. The technique is limited by the operator's ability to guess the appropriate proportional without clear basis. [expand on this]. \section{\label{sec:Sections}Conclusions and Summary} \section{\label{sec:Sections}References} \begin{acknowledgments} Typically, standard acknowledgments include financial support and technical assistance, and may include dedications, memorials, and awards. Check with the Editorial Office for suitability of an acknowledgment if there is any question. To indicate the author, use initials. For example, “B.A. wishes to thank A. Loudon for technical assistance. C.A. wishes to thank Anytown University for use of their equipment.” Note: the Acknowledgment section should be set as your last paragraph of text before the references. \end{acknowledgments} \appendix \section{Appendixes} Appendixes are not permitted in the \textit{McMaster Journal of Engineering Physics}. \subsubsection*{\label{sec:notes}Notes and References} %note that the section numbering is suppressed by the use of "*" before the name of the section \footnotesize \noindent $^{a)}$A. Author and B. Author contributed equally to this work.\\ $^{b)}$This research was performed while C. Author was at Anywhere National Laboratory, City, State, Postal code, Country.\\ *Author to whom correspondence should be addressed. Electronic mail: author@somewhere.org %\nocite{*} \bibliographystyle{unsrtnat} \bibliography{MJEP}% Produces the bibliography via BibTeX. \end{document} % % ****** End of file aipsamp.tex ******