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All Journal Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems
Pariwat Imura
Rangsit University
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Comparative Study of Takagi-Sugeno-Kang and Madani Algorithms in Type-1 and Interval Type-2 Fuzzy Control for Self-Balancing Wheelchairs Manutsawee Kiew-ong-art; Phichitphon Chotikunnan; Anantasak Wongkamhang; Rawiphon Chotikunnan; Anuchit Nirapai; Pariwat Imura; Manas Sangworasil; Nuntachai Thongpance; Anuchart Srisiriwat
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1154

Abstract

This study examines the effectiveness of four different fuzzy logic controllers in self-balancing wheelchairs. The controllers under consideration are Type-1 Takagi-Sugeno-Kang (TSK) FLC, Interval Type-2 TSK FLC, Type-1 Mamdani FLC, and Interval Type-2 Mamdani FLC. A MATLAB-based simulation environment serves for the evaluation, focusing on key performance indicators like percentage overshoot, rise time, settling time, and displacement. Two testing methodologies were designed to simulate both ideal conditions and real-world hardware limitations. The simulations reveal distinct advantages for each controller type. For example, Type-1 TSK excels in minimizing overshoot but requires higher force. Interval Type-2 TSK shows the quickest settling times but needs the most force. Type-1 Mamdani has the fastest rise time with the lowest force requirement but experiences a higher percentage of overshoot. Interval Type-2 Mamdani offers balanced performance across all metrics. When a 2.7 N control input cap is imposed, Type-2 controllers prove notably more efficient in minimizing overshoot. These results offer valuable insights for future design and real-world application of self-balancing wheelchairs. Further studies are recommended for the empirical testing and refinement of these controllers, especially since the initial findings were limited to four-wheeled self-balancing robotic wheelchairs.
Exploring ResNet-18 Estimation Design through Multiple Implementation Iterations and Techniques in Legacy Databases Nuntachai Thongpance; Pareena Dangyai; Kittipan Roongprasert; Anantasak Wongkamhang; Ratchanee Saosuwan; Rawiphon Chotikunnan; Pariwat Imura; Anuchit Nirapai; Phichitphon Chotikunnan; Manas Sangworasil; Anuchart Srisiriwat
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.19589

Abstract

In a rapidly evolving landscape where automated systems and database applications are increasingly crucial, there is a pressing need for precise and efficient object recognition methods. This study contributes to this burgeoning field by examining the ResNet-18 architecture, a proven deep learning model, in the context of fruit image classification. The research employs an elaborate experimental setup featuring a diverse fruit dataset that includes Rambutan, Mango, Santol, Mangosteen, and Guava. The efficacy of single versus multiple ResNet-18 models is compared, shedding light on their relative classification accuracy. A unique aspect of this study is the establishment of a 90% decision threshold, introduced to mitigate the risk of incorrect classification. Our statistical analysis reveals a significant performance advantage of multiple ResNet-18 models over single models, with an average improvement margin of 15%. This finding substantiates the study’s central hypothesis. The implemented 90% decision threshold is determined to play a pivotal role in augmenting the system’s overall accuracy by minimizing false positives. However, it’s worth noting that the increased computational complexity associated with deploying multiple models necessitates further scrutiny. In sum, this study provides a nuanced evaluation of single and multiple ResNet-18 models in the realm of fruit image classification, emphasizing their utility in practical, real-world applications. The research opens avenues for future exploration by refining these methodologies and investigating their applicability to broader object recognition tasks.
Co-Authors Anantasak Wongkamhang Anuchart Srisiriwat Anuchit Nirapai Kittipan Roongprasert Manas Sangworasil Manutsawee Kiew-ong-art Nuntachai Thongpance Pareena Dangyai Phichitphon Chotikunnan Ratchanee Saosuwan Rawiphon Chotikunnan