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Neural Networks Identification and Control of Mobile Robot Using Adaptive Neuro Fuzzy Inference System Abougarair, Ahmed Jaber
IAES International Journal of Robotics and Automation (IJRA) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v9i4.pp%p

Abstract

This paper developed and investigates the performance of intelligent algorithms in order to stabilize the robot when it is tracking to the desired reference. One type of robot is a Two Wheeled Balancing Mobile Robot (TWBMR) that requires control for both balancing and maneuvering. Combination artificial intelligence, Neural Networks (NNs) and Fuzzy Logic Control (FLC) have been recognized as the main tools to improve the performance of coupling nonlinear robot system without using any mathematical model. The input-output data of TWBMR generated from closed loop control system is used to develop a neural network model. In this study, neural networks model can be trained offline and then transferred into a process where an adaptive online learning is carried out using Adaptive Network Based Fuzzy Inference System (ANFIS) to improve the system performance. The simulation results verify that the considered identification and control strategies can achieve favorable control performance.The ANFIS control design approach does not require an accurate model of the plant as classical controller. In addition, high-level knowledge of the system is not needed to build a set of rules as a fuzzy controller.
Sliding Mode Control Design for Magnetic Levitation System Ma'arif, Alfian; Vera, Marco Antonio Marquez; Mahmoud, Magdi Sadek; Umoh, Edwin; Abougarair, Ahmed Jaber; Rahmadhia, Safinta Nurindra
Journal of Robotics and Control (JRC) Vol 3, No 6 (2022): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v3i6.12389

Abstract

This paper presents a control system design for a magnetic levitation system (Maglev) or MLS using sliding mode control (SMC). The MLS problem of levitating the object in the air will be solved using the controller. Inductors used in MLS make the system have nonlinear characteristics. Thus, a nonlinear controller is the most suitable control design for MLS. SMC is one of the nonlinear controllers with good robustness and can handle any model mismatch. Based on simulation results with a step as input reference, MLS provided good system performances: 0.0991s rise time, 0.1712s settling time, and 0.0159 overshoot. Moreover, a prominent tracking control for sine wave reference was also shown. Although the augmented system had a chattering effect on the control signal, the chattering control signal did not affect MLS performances.
Deep Learning-Based Automated Approach for Classifying Bacterial Images Abougarair, Ahmed Jaber; Oun, Abdulhamid A.; Sawan, Salah I.; Ma’arif, Alfian
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1423

Abstract

Identifying and classifying bacterial species from microscopic images is crucial for medical applications like prevention, diagnosis, and treatment. However, because of their diversity and variability in appearance, manually classifying bacteria is difficult and time-consuming. This work suggests employing deep learning architecture to automatically categorize bacterial species in order to overcome these difficulties and raise the accuracy of bacterial species recognition. We have evaluated our suggested approach using the Digital Images of Bacteria Species (DIBaS), a publicly accessible resource of photographs of tiny bacteria.  This work uses a dataset that differs in terms of bacterial morphology, staining methods, and imaging circumstances. This paper aims to enhance the accuracy and reduce the computational requirements for Convolutional Neural Networks (CNN) based classification of bacterial species using GoogLeNet and AlexNet to train the models. This paper focuses on employing transfer learning to retrain pre-trained CNN models using a dataset consisting of 2000 images encompassing 12 distinct bacteria species known to be harmful to human health.  The concept of transfer learning was utilized to expedite the network's training process and enhance its categorization performance.  The results are promising, with the method achieving an accuracy of 98.7% precision, recall of 99.50%, and an F1-score of 99.45%   with classifier speed. Furthermore, the proposed bacteria classification approach demonstrated strong performance, irrespective of the size of the training data used.  This paper contributes by automating bacterial classification to facilitate faster and more accurate identification of bacterial species, which facilitates the treatment of infections and related diseases, in addition to monitoring public health, and promoting the wise use of antimicrobial drugs. To improve outcomes in the future, researchers can also integrate deep learning techniques with other machine learning methods.
Optimizing Light Intensity with PID Control Alfian, Eriko; Ma'arif, Alfian; Chotikunnan, Phichitphon; Abougarair, Ahmed Jaber
Control Systems and Optimization Letters Vol 1, No 3 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v1i3.38

Abstract

Lighting is a fundamental cornerstone within interior design, possessing the capability to metamorphose spaces and evoke emotional responses profoundly. This principle applies to residential, industrial, and office domains, where lighting nuances are meticulously adjusted to enhance comfort and practicality. However, adequate luminance frequently intersects with energy wastage, often attributed to negligent light management practices. Mitigating this issue necessitates integrating light intensity controls adept at adapting to ambient luminosity and room-specific parameters. A prospective avenue encompasses incorporating a Proportional Integral Derivative (PID) control system synergized with light sensors. This research Implementing a closed-loop architecture, PID control utilizes feedback mechanisms to improve the precision of instrumentation systems. The PID methodology, consisting of Proportional, Integral, and Derivative control modalities, produces stable responses, accelerates system reactions, and diminishes deviations and overshooting by predetermined setpoints. The proposed Light Intensity Control System underpinned by PID methodology manifests as an exhibition of compelling outcomes drawn from empirical trials. The judicious selection of optimal parameters, specifically Kp = 0.2, Ki = 0.1, and Kd = 0.1, yielded noteworthy test outcomes: an ascent time of 0.0848, an overshoot of 6.5900, a culmination period of 0.4800, a settling period of 2.3032, and a steady-state error of 0.0300. Within this system, the PID controller assumes a pivotal role, orchestrating the regulation and meticulous calibration of light intensity to harmonize with designated criteria, thus fostering an environment of augmented energy efficiency and adaptability in illumination.Lighting is a fundamental cornerstone within interior design, possessing the capability to metamorphose spaces and evoke emotional responses profoundly. This principle applies to residential, industrial, and office domains, where lighting nuances are meticulously adjusted to enhance comfort and practicality. However, adequate luminance frequently intersects with energy wastage, often attributed to negligent light management practices. Mitigating this issue necessitates integrating light intensity controls adept at adapting to ambient luminosity and room-specific parameters. A prospective avenue encompasses incorporating a Proportional Integral Derivative (PID) control system synergized with light sensors. This research Implementing a closed-loop architecture, PID control utilizes feedback mechanisms to improve the precision of instrumentation systems. The PID methodology, consisting of Proportional, Integral, and Derivative control modalities, produces stable responses, accelerates system reactions, and diminishes deviations and overshooting by predetermined setpoints. The proposed Light Intensity Control System underpinned by PID methodology manifests as an exhibition of compelling outcomes drawn from empirical trials. The judicious selection of optimal parameters, specifically Kp = 0.2, Ki = 0.1, and Kd = 0.1, yielded noteworthy test outcomes: an ascent time of 0.0848, an overshoot of 6.5900, a culmination period of 0.4800, a settling period of 2.3032, and a steady-state error of 0.0300. Within this system, the PID controller assumes a pivotal role, orchestrating the regulation and meticulous calibration of light intensity to harmonize with designated criteria, thus fostering an environment of augmented energy efficiency and adaptability in illumination.