M.Phil / MS
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/42
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
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Item 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 9368Complex 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.Item Optimizing PV Performance in Shaded Areas with Distributed (MLPE) Systems(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Muhammad Aqsam Raza; FA20-REE-012; Dr. Yaqoob Javed; LHR TP 9371Solar energy, especially photovoltaic solar energy, enhances renewable energy technologies. However, solar PV system performance may be affected by the changing climate, temperatures highlighted, and changes in irradiance. In the context of such issues, there are two considerations for configuring the PV system: centralized and distributed. The centralized one has quick power tracking with faster convergence speeds. At the same time, it works great when the irradiance is uniform, giving very high energy; however, PS may result in a massive loss of power. On the contrary, for the distributed system, there is monitoring for current and voltage at each panel, producing copious data that work fine in the case of shade because of applied MLPE converters. Thesis presents a modular-based DFO DC-DC converter with several novel features, including the accurate monitoring of modules, an accessible troubleshooting facility, and a rapid shutdown if there is a fire danger. The following section compares the developed MPPT DFO algorithm robustness with that of other state-of-the-art techniques, such as CS, FFO, PSO Inc. and P&O. The findings indicate that the novel approach is dynamic in nature and produces lower harmonics and better performance in tracking MPPT. In this regard, the effectiveness of both the MLPE and centralized systems is tested on the Helioscope software using different inverter brands: SMA, Tigo, Enphase, Solar Edge and Huawei. The results prove that MLPE is a better option in the case of shading region for attaining maximum power point almost 30% to 35% more power is generated through a MLPE based system.Item Fuzzy Model Predictive Controller for Attitude Control of Satellite(COMSATS University Islamabad Lahore Campus, 2023) Rabiya Noor,; SP20-REE-008; ,Dr. Mujtaba Hussain Jaffery Assistant Profesor; LHR TP 7894The technological advancement in satellite has tremendously improved in recent years. Generally, satellite applications have been classified into four categories i.e., navigation, communication, weather and earth observation missions. The successful execution of these applications heavily relies on the performance of attitude determination and control subsystem of satellite. Therefore, researchers have been continuously working to improve the satellite attitude regulation under challenging space environment by applying various control strategies i.e., PD, PID, LQR, MPC. MPC is a famous control technique, as it can handle the operational constraints of the system effectively. In satellite, actuator torque has limited capability and it can result in the saturation problem. MPC can regulate the satellite attitude without violating the actuator constraints hence ensuring system stability. However, the mathematical model of satellite is highly nonlinear and for nadir pointing satellite, it can be treated as simple linear system for a constrained operating range. Although, it impacts the accuracy of the nonlinear model when a model-based controller is used. This results in a compromise between simplicity and accuracy that is faced by every control engineer. Therefore, in this work a systematic approach to improve the attitude accuracy has been explored while deriving linear subsystems of the nonlinear satellite model for the controller design. In this approach, the mathematical model of the plant is represented by various linear subsystems in terms of Takagi-Sugeno Fuzzy rules which are independent of each other and a control law is designed for each rule using MPC. The output of each MPC is accumulated based on Parallel Distribution technique hence providing command to the actuator. Eventually, the torque produced by the actuator is applied on nonlinear plant. Comparison of the response specifications (transient and steady state response) between PID, MPC and Fuzzy-MPC controllers under different cases such as external disturbance, actuator constraints, large reference angles and initial angles has been discussed. The simulation results indicated that proposed Fuzzy-MPC exhibits consistent and improved performance in all four cases in comparison to other controllers. The pointing accuracy is successfully achieved while catering to the challenge of actuator constraints under external disturbances of LEO.Item Nowcasting of RSSL in wireless communication channel over the sea using machine learning algorithms(COMSATS University Islamabad Lahore Campus, 0023) Farwa Jafar,; FA20-REE-005; Dr. Khurram Zaidi; LHR TP 7896The presence of naturally occurring evaporation duct (ED) phenomenon is very high in the tropical/equatorial regions of the world. Although, refractivity estimation of EM and Radio waves in ED is well studied in the literature, still, the signal propagation through ED over-the-horizon needs to be thoroughly researched to help determine the received-signal-strength-level (RSSL) for a reliable wireless communication link. In order to accurately predict RSSL in ED, we have acquired RSSL (avg.) per-minute data for three months over-the-horizon distance of 50 km (Tx-Rx) from onshore-to-offshore Oil & Gas Platform. This data was collected using fixed antenna heights in ED. Applying deep learning algorithms on real-time RSSL data, we have nowcasted the future RSSL values for next 5 seconds timescale in this thesis. A thorough comparison is made between the CNN and LSTM deep learning methods for real-time series prediction analysis. These deep learning networks are linked with numerous convolution layers to grasp the nonlinear mapping between measured and future RSSL values. Coding and Simulation work is performed in Python 3.9 environment and results are generated in Kaggle Notebook. CNN and LSTM networks have never been used earlier for predicting “signal strength” over-the-horizon and over-the-sea under ED environment. The contribution of this research is to bridge this gap and examine the accuracy of LSTM and CNN for nowcasting RSSL data. According to what we've discovered, both of these neural network models are capable of achieving adequate to high prediction power given that the "datasets" are suitably big. Both methods, when taken as a whole, are reliable with regard to their hyperparameters. However, with increasing number of training courses, LSTM didn’t improve its performance, whereas CNNs performed correspondingly more accurate each time. For 3rd training, CNN has given the most optimal fitting of training data as compare to test data. The RMSE achieved for CNN after third training was 4.47 which is the least of all simulations. Hence, CNNs proved to be superior, since they operate one order of magnitude quicker than LSTM. We proposed that the early predictive capability, speed, and resilience of CNN open its door to nowcasting’s future.