Article Per Year (5 Year)
p-Index From 2020 - 2025
0.882
P-Index
This Author published in this journals
All Journal Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems Journal of Fuzzy Systems and Control (JFSC) Control Systems and Optimization Letters
Khotakham, Wanida
Unknown Affiliation
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Mechanical Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Comparative Analysis of Sensor Fusion for Angle Estimation Using Kalman and Complementary Filters Chotikunnan, Phichitphon; Khotakham, Wanida; Ma'arif, Alfian; Nirapai, Anuchit; Javana, Kanyanat; Pisa, Pawichaya; Thajai, Phanassanun; Keawkao, Supachai; Roongprasert, Kittipan; Chotikunnan, Rawiphon; Imura, Pariwat; Thongpance, Nuntachai
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1674

Abstract

In engineering, especially for robots, navigation, and biomedical uses, accurate angle estimation is absolutely crucial. Using data from the IMU6050 sensor, which combines accelerometer and gyroscope readings, this work contrasts two sensor fusion methods: the Kalman filter and the complementary filter. The aim of the research is to find the most efficient filtering method for preserving accuracy and resilience throughout several motion contexts, including low-noise (standard rotation) and high-noise (external disturbances). With an eye toward improving sensor accuracy in dynamic applications, the study contribution is a thorough investigation of filter performance under different noise levels. MATLAB quantified estimate accuracy using key metrics like root mean square error (RMSE) and mean absolute error (MAE). Under controlled noise levels, our approach included methodical error analysis of both filters. Results show that, especially under low-noise conditions, the Kalman filter beats the complementary filter in terms of lower MAE and RMSE; it also shows adaptability and robustness in high-noise environments with much fewer errors than accelerometer-only and complementary filter outputs. These results show the relevance of the Kalman filter in practical settings like robotic control, motion tracking, and possible biomedical equipment, including patient positioning systems and wheelchairs with balance control. Future studies might investigate the implementation of the Kalman filter in sophisticated systems requiring accuracy, such as telemedicine robots or autonomous navigation. This work develops sensor fusion techniques and offers understanding of consistent sensor data processing in several operating environments.
Genetic Algorithm-Optimized LQR for Enhanced Stability in Self-Balancing Wheelchair Systems Chotikunnan, Phichitphon; Khotakham, Wanida; Wongkamhang, Anantasak; Nirapai, Anuchit; Imura, Pariwat; Roongpraser, Kittipan; Chotikunnan, Rawiphon; Thongpance, Nuntachai
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.161

Abstract

Balancing systems, exemplified by electric wheelchairs, require accurate and effective functioning to maintain equilibrium across many situations. This research looks at how well a standard linear quadratic regulator (LQR) and its genetic algorithm (GA)-optimized version keep an electric wheelchair stable when it stands on its own. The aim of the optimization was to improve energy economy, robustness, and responsiveness through the refinement of control settings. Simulations were performed under two scenarios: stabilizing the system from a tilt and recovering from an external force. Both controllers stabilized the system; however, the GA-optimized LQR demonstrated considerable improvements in control efficiency, decreased stabilization time, and enhanced response fluidity. It exhibited improved resilience to external disturbances, as indicated by a decrease in oscillations and an increase in fluid displacement recovery. These enhancements highlight the LQR's versatility, resilience, and appropriateness for real-world applications, including Segways, balancing robots, and patient wheelchairs. This study highlights the ability of evolutionary algorithms to enhance the effectiveness of traditional control systems in dynamic and unpredictable settings.
Comparative Analysis of PID Tuning Methods for Speed Control in Mecanum-Wheel Electric Wheelchairs Thongpance, Nuntachai; Chotikunnan, Phichitphon; Wongkamhang, Anantasak; Chotikunnan, Rawiphon; Imura, Pariwat; Khotakham, Wanida; Nirapai, Anuchit; Roongprasert, Kittipan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13046

Abstract

This study compares two PID controller tuning methods, particle swarm optimization (PSO) and Cohen-Coon, employed for speed control of an omnidirectional Mecanum-wheel electric wheelchair. Mecanum wheels improve maneuverability on powered mobility platforms; yet, controlling these systems is difficult due to nonlinearities and directional coupling effects. This work investigates the effectiveness of PSO as a sophisticated alternative to traditional PID tuning methods, effectively tackling this issue. This study evaluates P, PI, PD, and PID controllers tuned by both Cohen-Coon and PSO methods, applied to a DC motor system simulating real-world wheelchair actuation. Step response-based system identification models the motor using MATLAB/Simulink. Simulations of a 12V DC motor are examined using controlled-step time-domain inputs. Every controller configuration is subjected to evaluation for overshoot, root mean square error (RMSE), rise time, and settling time. The PSO-tuned PID controller exhibited enhanced performance, characterized by a rise time of 2.06 s, a settling time of 2.37 s, an overshoot of 0.78%, and an RMSE of 4.59, far surpassing the Cohen-Coon variant, which had a settling time of 6.12 s and an overshoot of 20.14%. The results indicate that PSO enhances both transient and steady-state performance in intricate and disturbance-sensitive systems, including Mecanum wheelchairs. Despite PSO's increased computing complexity during offline tuning and the necessity for meticulous parameter selection, its advantages can be precomputed and effectively utilized in real-time embedded systems. This study highlights the importance of safety, dependability, and responsiveness, illustrating that PSO is a scalable and efficient method for improving assistive robotic systems.
Noise-Reduced 3D Organ Modeling from CT Images Using Median Filtering for Anatomical Preservation in Medical 3D Printing Chotikunnan, Phichitphon; Chotikunnan, Rawiphon; Puttasakul, Tasawan; Khotakham, Wanida; Imura, Pariwat; Prinyakupt, Jaroonrut; Thongpance, Nuntachai; Srisiriwat, Anuchart
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26665

Abstract

This study offers a systematic approach to improving the reconstruction of three-dimensional anatomical models from CT imaging data. The main difficulty tackled is the maintenance of internal bone features during denoising, essential for producing clinically relevant models. A nonlinear filtering strategy was implemented, utilizing a 3×3 median filter alongside manual refinement to eliminate salt-and-pepper noise while preserving anatomical information. The study presents a reproducible image-processing pipeline that improves structural clarity and enables material-efficient 3D printing while preserving internal bone integrity. A publicly available dataset including 813 anonymized chest CT scans (512×512 pixels, 16-bit grayscale) from Zenodo was employed. Preprocessing included grayscale normalization, brightness adjustment, and the application of median filters with kernel sizes from 3×3 to 9×9, followed by artifact removal using FlashPrint software before STL conversion. The 3×3 median filter achieved an excellent balance between noise reduction and anatomical clarity, outperforming mean filtering and larger kernels in maintaining edge detail. Although statistical evaluation was not conducted, visual analysis validated an 18.07 percent decrease in print time and a 17.88 percent reduction in filament consumption. The technology exhibited actual efficacy in generating high-quality anatomical models. Future endeavors will incorporate automated segmentation and sophisticated denoising methodologies to enhance applicability in surgical simulation, clinical education, and personalized healthcare planning.
Comparative Analysis of PID-Driven Data-Based and PSO-Tuned Fuzzy Membership Functions for Robotic Manipulator Control Chotikunnan, Phichitphon; Khotakham, Wanida; Imura, Pariwat; Chotikunnan, Rawiphon; Wongkamhang, Anantasak; Thongpance, Nuntachai
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i3.335

Abstract

Robotic manipulators require control systems that are both responsive and precise in order to ensure accurate tracking and stability in dynamic environments. Conventional fuzzy logic controllers that are based on proportional integral derivative (PID) methods frequently encounter difficulties in achieving fast response, minimal steady-state error, and low overshoot. This study presents a comparative evaluation of a PID-driven data-based fuzzy logic controller and a particle swarm optimization (PSO) tuned fuzzy logic controller for a three-axis robotic manipulator implemented in Simulink. Both controllers used Gaussian membership functions within a Mamdani inference structure. The PSO algorithm was employed to optimize fuzzy input-scaling gains using a composite performance index that incorporated absolute error, control effort, overshoot penalty, and steady-state error. The simulation results indicate that the PSO-tuned controller consistently outperformed the benchmark. On the R-axis, it shortened rise and settling times and reduced overshoot, mean absolute error (MAE), and root mean square error (RMSE). On the T-axis, response speed and error values improved, although overshoot increased, indicating a trade-off between speed and stability. On the Z-axis, the PSO controller achieved a substantial decrease in overshoot, lower error metrics, and faster stabilization. Overall, the PSO-based tuning process preserved steady-state stability while improving transient performance on all axes. These findings show that metaheuristic optimization is an effective and practical method for enhancing fuzzy logic controllers in robotic manipulators. This approach has potential applications in precision manufacturing, service automation, and surgical robotics.
Co-Authors Alfian Ma’arif Chotikunnan, Phichitphon Chotikunnan, Rawiphon Imura, Pariwat Javana, Kanyanat Keawkao, Supachai Nirapai, Anuchit Pisa, Pawichaya Prinyakupt, Jaroonrut Puttasakul, Tasawan Roongpraser, Kittipan Roongprasert, Kittipan Srisiriwat, Anuchart Thajai, Phanassanun Thongpance, Nuntachai Wongkamhang, Anantasak