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A comparison between a traditional PID controller and an Artificial Neural Network controller in manipulating a robotic arm
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
En jämförelse mellan en traditionell PIDstyrenhet och en Artificiell Neural Nätverksstyrenhet för att styra en robotarm (Swedish)
Abstract [en]

Robotic and control industry implements different control technique to control the movement and the position of a robotic arm. PID controllers are the most used controllers in the robotics and control industry because of its simplicity and easy implementation. However, PIDs’ performance suffers under noisy environments. In this research, a controller based on Artificial Neural Networks (ANN) called the model reference controller is examined to replace traditional PID controllers to control the position of a robotic arm in a noisy environment. Simulations and implementations of both controllers were carried out in MATLAB. The training of the ANN was also done in MATLAB using the Supervised Learning (SL) model and Levenberg-Marquardt backpropagation algorithm. Results shows that the ANN implementation performs better than traditional PID controllers in noisy environments.

Abstract [sv]

Robotoch kontrollindustrin implementerar olika kontrolltekniker för att styra rörelsen och placeringen av en robotarm. PID-styrenheter är de mest använda kontrollerna inom roboten och kontrollindustrin på grund av dess enkelhet och lätt implementering. PID:s prestanda lider emellertid i bullriga miljöer. I denna undersökning undersöks en styrenhet baserad på Artificiell Neuralt Nätverk (ANN) som kallas modellreferenskontrollen för att ersätta traditionella PID-kontroller för att styra en robotarm i bullriga miljöer. Simuleringar och implementeringar av båda kontrollerna utfördes i MATLAB. Utbildningen av ANN:et gjordes också i MATLAB med hjälp av Supervised Learning (SL) -modellen och LevenbergMarquardt backpropagationsalgoritmen. Resultat visar att ANN-implementeringen fungerar bättre än traditionella PID-kontroller i bullriga miljöer.

Place, publisher, year, edition, pages
2019. , p. 29
Series
TRITA-EECS-EX ; 2019:398
Keywords [en]
Artificial Intelligence, Artificial Neural Network, Control System, PID Controller, Model Reference Controller, Robot arm
Keywords [sv]
Artificiell Intelligens, Artificiell Neuralt Nätverk, Kontroll System, PID-kontroller, Modellreferenskontroller, Robotarm
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-259365OAI: oai:DiVA.org:kth-259365DiVA, id: diva2:1351191
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Available from: 2019-09-13 Created: 2019-09-13 Last updated: 2019-09-13Bibliographically approved

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CiteExportLink to record
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