Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/3161
Title: Machine Learning Based Self-Balancing and Motion Control of the Underactuated Mobile Inverted Pendulum With Variable Load
Authors: Ünlütürk, Ali
Aydoğdu, Ömer
Keywords: Fuzzy logic
Payloads
Robot sensing systems
Mobile robots
Artificial neural networks
Motion control
Machine learning
Machine learning
adaptive fuzzy logic control
balance robot
sensor fusion
Design
Issue Date: 2022
Publisher: Ieee-Inst Electrical Electronics Engineers Inc
Abstract: In this paper, a novel Machine Learning (ML) based Adaptive Fuzzy Logic-Proportional Integral (AFL-PI) controller was developed for the self-balancing and precision motion control of a two wheeled Underactuated-Mobile Inverted Pendulum (U-MIP) under variable payloads. One of the external disturbances in balance and motion control of the U-MIP is the amount of payload it carries on. To investigate the effectiveness of the proposed controller, a load bar was mounted on top of the U-MIP. The weights of 55gr each can be attached to this bar for variable payloads. The weights on the bar were labeled as three different classes: Low Load (LL), Normal Load (NL) and Heavy Load (HL). Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) models were tested to obtain the highest payload class estimation. The highest load classification accuracy was achieved with ANN. Therefore, the ANN model was applied on the U-MIP. The balance performance of the U-MIP was compared by applying the classical FL-PI and ANN based AFL-PI controller on the robot. In order to compare the body tilt angle performance of the U-MIP, the optimal FL-PI parameter in LL was applied for NL and HL conditions without changing. Then, the proposed ANN based AFL-PI controller was implemented on U-MIP. With the proposed novel controller, the body tilt angle variation of the U-MIP was improved by %29.42 for NL and %55.62 for HL compared to the classical FL-PI controller. The validity of the proposed controller was proved by real experiments.
URI: https://doi.org/10.1109/ACCESS.2022.3210540
https://doi.org/10.1109/ACCESS.2022.3210540
https://hdl.handle.net/20.500.13091/3161
ISSN: 2169-3536
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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