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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) International Journal of Robotics and Control Systems
Ahmed J. Abougarair
University of Tripoli
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Performance of Anti-Lock Braking Systems Based on Adaptive and Intelligent Control Methodologies Ahmed J. Abougarair; Nasar Aldian A. Shashoa; Mohamed K. Aburakhis
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3794

Abstract

Automobiles of today must constantly change their speeds in reaction to changing road and traffic circumstances as the pace and density of road traffic increases. In sophisticated automobiles, the Anti-lock Braking System (ABS) is a vehicle safety system that enhances the vehicle's stability and steering capabilities by varying the torque to maintain the slip ratio at a safe level. This paper analyzes the performance of classical control, model reference adaptive control (MRAC), and intelligent control for controlling the (ABS). The ABS controller's goal is to keep the wheel slip ratio, which includes nonlinearities, parametric uncertainties, and disturbances as close to an optimal slip value as possible. This will decrease the stopping distance and guarantee safe vehicle operation during braking. A Bang-bang controller, PID, PID based Model Reference Adaptive Control (PID-MRAD), Fuzzy Logic Control (FLC), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controller are used to control the vehicle model. The car was tested on a dry asphalt and ice road with only straight-line braking. Based on slip ratio, vehicle speed, angular velocity, and stopping time, comparisons are performed between all control strategies. To analyze braking characteristics, the simulation changes the road surface condition, vehicle weight, and control methods. The simulation results revealed that our objectives were met. The simulation results clearly show that the ANFIS provides more flexibility and improves system-tracking precision in control action compared to the Bang-bang, PID, PID-MRAC, and FLC.
Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique Ahmed J. Abougarair; Shada E. Elwefati
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.v3i4.1151

Abstract

Developing effective strategies to contain the spread of infectious diseases, particularly in the case of rapidly evolving outbreaks like COVID-19, remains a pressing challenge. The Susceptible-Infected-Recovery (SIR) model, a fundamental tool in epidemiology, offers insights into disease dynamics. The SIR system exhibits complex nonlinear relationships between the input variables (e.g., population, infection rate, recovery rate) and the output variables (e.g., the number of infected individuals over time). We employ Recurrent Neural Networks (RNNs) to model the SIR system due to their ability to capture sequential dependencies and handle time-series data effectively. RNNs, with their ability to model nonlinear functions, can capture these intricate relationships, enabling accurate predictions and understanding of the dynamics of the system. Additionally, we apply the Pontryagin Minimum Principle (PMP) based different control strategies to formulate an optimal control approach aimed at maximizing the recovery rate while minimizing the number of affected individuals and achieving a balance between minimizing costs and satisfying constraints. This can include optimizing vaccination strategies, quarantine measures, treatment allocation, and resource allocation. The findings of this research indicate that the proposed modeling and control approach shows potential for a comprehensive analysis of viral spread, providing valuable insights and strategies for disease management on a global level. By integrating epidemiological modeling with intelligent control techniques, we contribute to the ongoing efforts aimed at combating infectious diseases on a larger scale.
Co-Authors Mohamed K. Aburakhis Nasar Aldian A. Shashoa Shada E. Elwefati