Browsing by Author "Waqas Ahmad"

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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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    AQI Predictor
    (Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-11-20) Waqas Ahmad; FA16-BCS-047; Dr Usama Ijaz Bajwa; LHR TP 6129
    Our project, AQI Predictor, is an application to predict the quality of Air aka Air Quality Index (AQI) Level using images. The user can input the images by capturing directly from the smartphone’s camera or already captured ones from the gallery. The app uses a trained model that’s hosted on local server at the backend. The trained model is a Convolutional Neural Network that uses machine learning techniques to help us in predicting the Air Quality level (AQL) correctly. The model is trained on a relevant dataset in order to achieve the accuracy. Along with the app, a website will be created that will be synchronized with the app and the data will be collected. The data then will be used to generate a map of the areas the Air quality level has been calculated for. This will help people in deciding which areas have what kind of air quality. The quality of air will be predicted by the application providing us Air Quality Level which will further tell us more details such as if the air is harmful or good and how necessary precautions can be taken. These factors and results will help us and the authorities including the Government and relevant governmental and other bodies decide certain measures to make the air quality better.