Department of Electrical Engineering
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Item 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 10045The 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.Item Implementation of Machine Learning Models for Predicting Hydrogen Production from Renewable Energy(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Adnan Ayub; CIIT/FA23-REE-002/LHR; Dr. Muhammad Yaqoob Javed; LHR TP 10044The world continues to be largely reliant on fossil fuels, such as coal, oil and natural gas, which are major contributors of greenhouse gases, air pollution and climate change caused by the release of gases like CO2, NO o and SO2. Even though renewable energy sources such as solar and wind provide a cleaner substitute, their nature as intermittent and weather-dependent sources become a big problem in terms of large scale and long-term energy storage making traditional battery systems economically impractical. Consequently, the utilization of the surplus renewable energy through the process of water electrolysis to create green hydrogen has become a viable and alternative way of storing energy in the long term. This work suggests a two-stage machine learning-based predictive model of hydrogen production when using renewable energy, based on real-world working data of a 40.5 MW grid-connected photovoltaic (PV) power facility. The initial step involves predicting photovoltaic power output from meteorological variables using multiple regression and deep learning models, such as Support Vector Regression (SVR), Random Forest, Decision Tree, and Gated Recurrent Units (GRU). The second stage is to incorporate the predicted solar energy into a hybrid electrochemical model of hydrogen production, and the same machine learning models serve as data-driven correction models to more effectively predict hydrogen yield. It is a two-stage method that integrates both physical modelling and machine learning to model nonlinear system behaviour and real-world losses of operation. The findings show that SVR was always better in both phases than the other models with an R 2 value of 0.965 in photovoltaic power prediction and 0.968 in hydrogen production prediction. The given framework minimized the mistake in the production of hydrogen annually to about 3 percent, which is much better than theoretical models and deep learning alternatives. Moreover, a Sobol based global sensitivity analysis revealed that Global Horizontal Irradiance (GHI) made the greatest contribution to the uncertainty in the hydrogen production process, then AC power output and temperature at the module. The outcomes of these studies support the premise that the two stages proposed framework is a viable, precise, and large-scale solution to real-time forecasting, optimization of the system, and successful grid integration of green hydrogen system.Item Energy Aware Path Planning and Guidance for Non- Holonomic Robots in a Manufacturing Workshop(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Rimsha Rehman; CIIT/FA23-REE-005/LHR; Dr. Mujtaba Jaffery; LHR TP 10046Automated Guided Vehicles (AGVs) are now a core part of modern manufacturing workshops, enabling fast, reliable, and flexible material movement while reducing dependence on manual handling. Despite their importance, most existing path planning approaches still focus mainly on minimizing distance or travel time. These strategies overlook a crucial factor, energy consumption, which directly affects operational cost, battery health, sustainability goals, and overall system efficiency. Addressing this limitation, this thesis presents an Energy Aware Path Planning (EAPP) framework specifically designed for single-load, non-holonomic AGVs used in structured workshop environments. The proposed framework models the workshop layout as an undirected graph, where nodes represent workstation points or intersections and edges represent feasible routes that comply with AGV kinematic and turning constraints. Unlike traditional planners, our approach integrates a physics-based energy model into the A* algorithm, allowing each edge to be evaluated not only by its geometric length but also by its expected energy usage, considering acceleration, deceleration, rolling resistance, turning angles, and standby power. This enables the AGV to prioritize paths with fewer turns, even when they are slightly longer, ultimately reducing total energy consumption. The method is implemented and validated using MATLAB simulations, where both distance-based A* and the proposed energy-aware A* are compared under realistic AGV parameters. The results show that the EAPP framework significantly lowers energy consumption and maintains competitive travel times, while still producing feasible and safe navigation paths within constrained workshop networks. By directly embedding energy considerations into the path planning process, this work contributes to more sustainable intralogistics and provides a practical, scalable solution suitable for real-time AGV navigation and industrial deployment.Item Enhancing Security and privacy of machine learning based outdoor Air pollution prediction system using IOTA B(COMSATS University Islamabad Lahore Campus, 2020) Muneeba Malik,; SP20-REE-004; Dr. Abbas Javed Assistant Professor; LHR TP 7893Internet of Things (IoT) is a combination of devices, which are network-enabled and work for one shared cause. IoT has transformed several fields and continue to do so, for instance, agriculture, smart housing, development and planning, security systems, and communication networks. The security of IoT networks and devices remains compromised and has not been particularly worked upon. It poses many threats and challenges like jamming networks, insecure physical interface, sleep-deprivation attack and high-level attacks like Sybil attacks, authentication and communication, buffer reservation attacks. To deal with the issue of security, Blockchain Technology have been using advanced security algorithms for hashing and different resources like miners to compute the proof of work. It implements centralized consensus to verify and add more block to the chain acting as an authentication scheme. Over the course of last decade, the decentralized applications and their performance have been under observation while work on them is continuously in progress. Distributed Ledger Technology (DLT) has emerged as an advanced system to record and confirm transactions and authenticate the network to add another node to it. DLT have been used to verify, secure, improve the data that is produced and is sent over any network in its domain. As IoT continues to make progress, it continues to encounter verification challenges. To counter one these issues, a DLT called IOTA Tangle has exclusively been designed for IoT – machine to machine communication. This project aims to work on enhancing security and privacy concerns of machine learning based air pollution prediction system using IOTA Tangle as its resource. IOTA light node and full node will be developed to upload the sensor data securely on Tangle. In this work, performance of the network will be evaluated in terms of end-to-end node delay, power consumption and accuracy of air quality index (AQI).Item Modeling, Controlling Of Automatic Washing Machine(COMSATS University Islambad Lahore Campus, 2020) Salman Chaudhary , Syed Ahsan Raza Shamsi,; SP15-BTE-006 , SP15-BTE-044; Dr. Muhammad Yaqoob Javed, Assistant Profesor [Supervisor]; LHR TP 5908This project presents a design of automatic washing machine which is controlled using programmable logic controller (PLC). PLC is used to control the system which have specific functions. Basic PLC functioning like timing, sequence, controlling and relaying were implemented. The hardware contains one agitator and shell tub. A motor is used to run the agitator in either direction required as per sequence programming. solenoid valves are used for water inlet and drain out. Operation of these devices is completely automated using PLCItem Neural Network Based Gesture Identification System(COMSATS University Islamabad, Lahore Campus Library Information Services, CUI Lahore, 2020) By: Zoolnurain , Anas Idrees , Ali Raza, Contributor; FA15-EEE-033 , FA15-EEE-032 , FA15-EEE-002; Muhammad Usman Rafique,; LHR TP 5864Physically disabled individuals like deaf, mute and patients suffering from various disabilities require an effective communication device to make them independent. Traditionally flex sensors-based gloves have been used to identify the gestures of Sign languages. Current work is limited to only one glove used for capturing the gesture. ,Gesture recognition in the 3D environment has been a challenging task. The problem can be solved using Machine Learning techniques to separate the true gesture from the false gesture. Therefore, the aim of this project is to develop a portable universal communication device to assist patients with disabilities and provide them with better standards of living. One of the goals of this project is to implement and compare the performance of Neural networks to identify the true gesture. The system will use sign language (gestures identification from flex sensors) to communicate with people around them. The data will then be analysed in Matlab based machine learning environment to identify the performance of Machine learning algorithms. This proposal will also look into the feasibility of implementing machine learning algorithms in Android phones for gesture recognition.