Department of Electrical Engineering

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Has files: YesSubject: search.filters.subject.Dr. Mirza Tariq Hamayun

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    Adaptive Nonlinear Gain Approximation of a Robust Nonlinear Controller to Handle Uncertain Disturbances for UAV Systems Using Neural Networks
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Waqas Ahmad; CIIT/FA23-REE-003/LHR; Dr. Mirza Tariq Hamayun; LHR TP 10045
    The quadrotor unmanned aerial vehicle (UAV) has been a part of scientific studies owing to its applications in military, agriculture, infrastructure and construction, and environmental monitoring and conservation. However, its control design is challenging due to its nonlinear underactuated dynamics, external disturbances, parametric uncertainties, and the aerodynamic variations encountered during flight operations. Sliding mode control (SMC) is the most widely used control technique for handling nonlinear dynamics in the presence of external disturbances and modeling uncertainties. However, conventional SMC requires a priori knowledge of the upper bound on the overall disturbance at all times. Usually, these upper bounds on disturbances are derived from past data, environmental conditions, and empirical modeling; therefore, a conservative bound is used in SMC design, leading to high control effort and draining more energy from the system. To address this issue, adaptive sliding mode control (ASMC) strategies based on a neural network (NN) are presented in this dissertation. Neural networks are recognized as the leading approach to intelligent computation and are highly effective in handling nonlinearity, fault tolerance, adaptation, and continuous online learning. In this research, NN-based ASMC strategies are presented for a quadrotor UAV to improve flight performance by online estimation of adaptive modulation gain in response to variations in disturbance magnitude. Different neural network schemes are employed, including feed-forward neural networks (FNNs) using backpropagation (BP) and Levenberg-Marquardt (LM) algorithms for optimization, and a radial basis function neural network (RBFNN) with a Gaussian activation function, to adaptively estimate the modulation gains. Furthermore, the stability analysis of the proposed controllers is proven using Lyapunov theory.
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    Output Observer-based Sensor Fault Detection in Twin Rotor Multiple Input Multiple Output System
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Muhammad Salik Bilal; FA20-REE-010; Dr. Mirza Tariq Hamayun; LHR TP 9368
    Complex air vehicles are challenging to analyze due to their highly nonlinear behavior and considerable cross-coupling interaction. The control of a Twin Rotor Multiple Input Multiple Output System (TRMS) is difficult due to significant cross-coupling and nonlinear dynamics that resemble to helicopters. A beam that can freely rotate in both the vertical and horizontal planes on its base makes up the TRMS. The main rotor and the tail rotor are its two rotors. Both of these rotors are driven by DC motors. Gyroscopic disturbances and sensor faults in the rotor motors during rotation can affect the TRMS's stability and input tracking. In this work, an optimal controller is designed to ensure stability and reference tracking. Furthermore, an observer is designed to estimate the system outputs for sensor fault detection in TRMS subjected to deterministic disturbance and norm-bounded uncertainty in system matrix (A) using Linear Matrix Inequalities (LMIs) technique for TRMS. The effectiveness of reference tracking and estimation of the system outputs for sensor fault detection has been investigated through simulation environment.
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    Design and Implementation of a Hybrid Supervisory Controller for the Speed Control of PMSM under Load Variations
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Tayyab; SP21-REE-005; Dr. Mirza Tariq Hamayun; LHR TP 8474
    The control of speed in Permanent Magnet Synchronous Motors (PMSM) is a crucial aspect in various industrial domains, including electric vehicles, automation, and sustainable energy infrastructures. The maintenance of consistent speed control in PMSM encounters difficulties when faced with fluctuations in load. This research aims to demonstrate the implementation of a Proportional-Integral (PI) controller, Artificial Neural Networks (ANNs), and a hybrid controller which is a combination of PI and ANN in the speed regulation of PMSM while considering the variations in load. The hybrid controller that merges the benefits of a PI controller and an ANN controller is utilized to address the challenge of load variation. The PI controller is a reliable and consistent method of speed control, while the ANN contributes to adaptive control and improved performance in the presence of dynamic loads. The hybrid controller operates through the iterative adjustment of control inputs in response to motor feedback. The proportional and integral gains of the PI controller are tuned using ANN. The ANN learns the complex link between input parameters (e.g., motor speed, load torque) and the needed control action through a learning process. The controller's PI component delivers control signals proportionate to the difference between the reference and real motor speeds, resulting in fast responsiveness and stability. This allows the controller to adjust to changing load conditions and optimize the motor's reaction to keep the target speed. The controllers have been implemented in MATLAB/SIMULINK and it has been observed that the PI controller produced the mean square error (MSE) and root mean square error (RMSE) of 0.389 and 0.6236 respectively. The ANN model produced the MSE and RMSE of 0.021155 and 0.1454 respectively. Then the MSE and RMSE produced by PI-ANN are observed as 0.000458 and 0.02144 respectively. The results demonstrate that the PI-ANN controller produced the least MSE and RMSE followed by ANN and PI. There are various advantages of using a PI-ANN hybrid controller for PMSM speed control on load variation. It improves system adaptability by allowing the controller to modify control parameters in response to real time load variances.