Teaching fuzzy logic control based on a robotic implementation

Payman Shakouri , Olga Duran , Andrzej Ordys , Gordana Collier

Abstract

Advanced control concepts present a teaching challenge - even at master level students benefit from these concepts being implemented and demonstrated on real hardware, rather than simply modeling the plant, applying control strategy and tuning. This paper provides reference materials (both theoretical and test results), to be used in control teaching and assessment using a laboratory experiment, with a realtime single board computer based robotic vehicle (National Instruments Robotics Starter Kit). This paper explores the practical implementation of the ACC system through use of a real-time single board computer based robotic vehicle (National Instruments Robotics Starter Kit). The ACC algorithm based on fuzzy PID control is deployed on a field programmable gate array (FPGA), included in the robot's architecture. This robotic vehicle is programmed using a graphical programming language (LabVIEW). A Kalman filter is used to estimate the unmeasured parameters while implementing the control algorithm in the hardware (the real robot). The results obtained are compared for the simulation model and the real robot, respectively. The experiment demonstrates clear correlation between theoretical expectations and real-life system performance and at the same time offers a novel idea how to deliver this advanced control concept in an applied and visual manner.
Author Payman Shakouri - [Kingston University]
Payman Shakouri,,
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, Olga Duran - [Kingston University]
Olga Duran,,
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, Andrzej Ordys (FM / IACR) - [Kingston University]
Andrzej Ordys,,
- The Institute of Automatic Control and Robotics
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, Gordana Collier - [Kingston University]
Gordana Collier,,
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Journal seriesIFAC-PapersOnLine, ISSN 2405-8963
Issue year2013
Vol46
No17
Pages192-197
Publication size in sheets0.5
Keywords in Englishadaptive cruise control system, fuzzy control, PID controller, Kalman filter, robot control, Research Informed Teaching, state estimation
ASJC Classification2207 Control and Systems Engineering
DOIDOI:10.3182/20130828-3-UK-2039.00047
URL https://www.sciencedirect.com/science/article/pii/S1474667015340994
Languageen angielski
Score (nominal)5
Score sourcejournalList
ScoreMinisterial score = 0.0, 14-05-2020, ArticleFromJournal
Ministerial score (2013-2016) = 5.0, 14-05-2020, ArticleFromJournal
Publication indicators Scopus Citations = 4; GS Citations = 3.0; Scopus SNIP (Source Normalised Impact per Paper): 2013 = 0.413
Citation count*4 (2020-09-13)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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