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All Journal Perfecting a Video Game with Game Metrics International Journal of Robotics and Control Systems
Mohd Ariffanan Mohd Basri
Universiti Teknologi Malaysia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Finite Element Simulation of Microfluidic Biochip for High Throughput Hydrodynamic Single Cell Trapping Amelia Ahmad Khalili; Mohd Ariffanan Mohd Basri; Mohd Azhar Abdul Razak
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.9023

Abstract

In this paper, a microfluidic device capable of trapping a single cell in a high throughput manner and at high trapping efficiency is designed simply through a concept of hydrodynamic manipulation. The microfluidic device is designed with a series of trap and bypass microchannel structures for trapping individual cells without the need for microwell, robotic equipment, external electric force or surface modification. In order to investigate the single cell trapping efficiency, a finite element model of the proposed design has been developed using ABAQUS-FEA software. Based on the simulation, the geometrical parameters and fluid velocity which affect the single cell trapping are extensively optimized. After optimization of the trap and bypass microchannel structures via simulations, a single cell can be trapped at a desired location efficiently.
Self-Tuning PID Controller for Quadcopter using Fuzzy Logic A'dilah Baharuddin; Mohd Ariffanan Mohd Basri
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.v3i4.1127

Abstract

Tracking has become a necessary feature of a drone. This is due to the demand for drones, especially quadcopters, to be used for activities such as surveillance, monitoring, and filming. It is crucial to ensure the quadcopters perform the tracking with stable flight. Despite the advantages of having VTOL ability and great maneuverability, quadcopters require an effective controller to overcome their under-actuation and instability behavior. Even though a PID controller is commonly used and promising with its simple mechanism, it requires very proper tuning to ensure the stability of the system is not affected. In this paper, a simple Fuzzy algorithm is proposed to be incorporated into a PID controller to form a self-tuning Fuzzy PID controller. The Fuzzy logic controller works as the self-adjuster to the PID parameters. A mathematical model of the DJI Tello quadcopter is derived with position and attitude control loops that are designed to track a variety of trajectories with stable flight. The proposed method uses a simple architecture where the ranges of PID parameters are used as scaling factors for Fuzzy controller outputs. The results of the simulations show the tracking error performance metrics, which are IAE, ISE, and RMSE, are smaller compared to the values of the PID controller. Beyond its impact on quadcopter control, the proposed self-tuning approach holds promise for broader applications in nonlinear systems.
Radial Basis Function Network Based Self-Adaptive PID Controller for Quadcopter: Through Diverse Conditions Nur Hayati Sahrir; Mohd Ariffanan Mohd Basri
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1261

Abstract

A quadcopter is an underactuated and nonlinear system which requires a robust controller to aid in maneuvering the quadcopter during flight. A Proportional-Integral-Derivative (PID) controller is easy and suitable to implement, and its efficiency is proved in quadcopter control. However, a PID controller with fixed parameters is inadequate enough to control a quadcopter system with different inputs or perturbations. This paper proposes the development of a self-adaptive PID controller assisted by Radial Basis Function (RBF) Network, to improve the function of the PID controller and help a quadcopter to better adapt towards different inputs and situations, independently.  This work contributes to introducing RBF-PID controller to adaptively fly the underactuated quadcopter through different trajectory and perturbations using simulation. By using the hidden Gaussian function to train the current input, estimate the suitable output and update the Jacobian Information during system control, the PID gains can change adaptively during flight, additionally with the help of Gradient Descent Method (GDM). The proposed method is compared to the traditional PID controller tuned using the PID Tuner App in Simulink. Different inputs are given to test the altitude, attitudes, and position tracking such as step, multistep, sine wave, circular and lemniscate trajectory. The simulated results proved the robustness of RBF-PID in enhancing the disturbance rejection capacity by 13% to 25% in the presence of perturbations (sine wave and wind gust) compared to PID controller. The proposed controller can ensure quadcopter’s flight stability through perturbations that is within the quadcopter’s limitations.
Intelligent PID Controller Based on Neural Network for AI-Driven Control Quadcopter UAV Nur Hayati Sahrir; Mohd Ariffanan Mohd Basri
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.v4i2.1374

Abstract

Unmanned Aerial Vehicle (UAV), specifically a quadcopter is publicly popular which it provides services in different applications such as aerial delivery, aerial photography, military, weather forecasting and more examples to date. A Proportional-Integral-Derivative (PID) controller is one of the control techniques that can provide stabilization and reliable trajectory tracking. However, proper PID gains are needed to ensure a stable flight and it should be hybridized or improved to increase the robustness, reliability, and stabilization during flight. In this paper, an intelligent PID controller using neural network is proposed based on Levenberg-Marquardt feedforward neural network training method. The PID gains are initialized using different ranges according to the optimal gains generated by Particle Swarm Optimization, and this contributes towards a good training performance using Mean Square Error (MSE) evaluation. The trained network takes desired output and references as input data to calculate the required combination of PID gains as the output. The including of the response characteristics as the input data for the network, together with reference, error, and control input is the significance of the work. The performance of this work is presented using MSE performances, attitudes and altitude stabilization, and trajectory tracking reliability through error index performances. The simulation results graphically prove that the proposed controller provides better stability with reduced overshoot and settling times. Disturbance rejection is also enhanced by 1.7% compared to manual tuned PID controller. The reliability of the proposed controller highlights avenues for further exploration in AI-driven control strategies for quadcopter systems.
Co-Authors A'dilah Baharuddin Amelia Ahmad Khalili Mohd Azhar Abdul Razak Nur Hayati Sahrir