Ethiopia Institute of Technology- Mekelle
Permanent URI for this communityhttps://repository.mu.edu.et/handle/123456789/58
Browse
4 results
Search Results
Item Voltage Control of DC-DC Boost Converter Using Lyapunov Rule Based Model Reference Adaptive Controller(Mekelle University, 2024-10-24) : Mebrahtu NgusseThe switched mode DC-DC converters are the simplest power electronic circuits that facilitate the conversion of electrical voltage from one level to another through a switching process efficiently. DC-DC boost converters are utilized for step-up voltage in various applications. The output voltage of boost converter has, oscillation, overshoot, undershoot, and steady-state error. PID controllers have been usually applied to the converters because of their simplicity to obtain the desired voltage. But, PID controller could work well in one operating condition and cannot continuously adapt the changes in the process dynamic. To overcome this problem an advanced controller is required. The proposed Lyapunov Rule Based MRAC is adaptive and non-linear controller designed to overcome the uncertainties and nonlinearities for DC-DC boost Converter under Continuous Conduction Mode (CCM) operating condition. Using MATLAB/Simulink the performance of the proposed Lyapunov rule based MRAC is compared with that of the PID controller based on the dynamic response of the system. Using PID controller, overshoot and settling time have been improved by reducing from 67.5229% to 7.7717% and 0.6985 Sec to 0.277 Sec respectively. In the case of the proposed controller (Lyapunov rule based MRAC), overshoot, settling time, and undershoot have been improved by reducing from 67.5229% to 4.5993%, from 0.6985 Sec to 0.1458 Sec, and from 0.0036% to 0.0% respectively. To test the performance of the DC-DC boost converter, it is assumed that, the input voltage has been decreased and increased from its operating point by 25% and 41.67% respectively. Also the load resistance is assumed to be decreased and increased from its operating load resistance by 25% and 20% respectively. An external disturbance is applied to the system to check how the controller handles to uncertainties and PID controller has shown deviation from the desired value but, the controller MRAC maintained the desired value.Item Design of an Adaptive Neuro-Fuzzy Inference System Controller for Temperature and Concentration Control in a MIMO Continuous Stirred Tank Reactor (CSTR)(Mekelle University, 2025-04-07) Ybrah ZemchealThe Continuous Stirred Tank Reactor (CSTR) is a critical unit in chemical processing industries, where precise control of process variables is essential for optimal product quality and efficiency. Among the key variables, temperature and concentration are particularly important to regulate. However, chemical processes often exhibit nonlinear and multivariable behavior, making conventional controllers less effective in real-time operation (PID control in CSTR exhibits sluggish or oscillatory responses for feed concentration, slow response to variable water flow, Poor robustness for uncertain parameters (e.g., reaction rates, heat transfer coefficients), perform poorly due to cross-coupling effects, less nonlinearity handling due to reaction kinetics. Challenges such as dynamic process variations, reactant nonlinearities, fluctuating environmental conditions, and diverse disturbances further complicate control. Additionally, most industrial chemical processes are multi-input multi-output (MIMO) systems, necessitating advanced control strategies and decoupling techniques. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed as an advanced control approach to enhance system performance and accuracy compared to conventional controllers. ANFIS integrates the structured knowledge representation of fuzzy logic with the adaptive learning capabilities of neural networks, offering improved control for complex systems. The performance of ANFIS is evaluated against a traditional PID controller through offline simulations using MATLAB/Simulink. The results demonstrate that ANFIS outperforms PID control in key performance metrics, including overshoot, rise time, settling time, and system stability. Furthermore, ANFIS exhibits superior disturbance rejection capabilities, making it a more robust solution for CSTR control in industrial applications.Item Load Frequency Control of Small Hydro Power Plant Based on Artificial Neural Network with Tuned PID Controller(Mekelle University, 2025-04-10) Rigbey HailetsionElectricity propels the advancement of society and economy, fueling sectors such as healthcare, education, and industries. Real-time adjustments in power generation are made by load frequency control to stabilize frequency and voltage, a critical factor for uninterrupted power supply. An innovative approach suggests the integration of artificial neural networks and PID controllers for enhancing the performance of small hydropower plants. The core objective of the research is to create an ANN combined with tuned PID for regulating load frequency in small hydro power plants. The thesis elaborates on the limitations of conventional PID controllers and the flexibility of the ANN-based strategy, outlining the process of plant modeling and controller configuration. Both Proportional-Integral-Derivative (PID) controllers and ANN with Tuned PID controllers are commonly employed techniques. While PID controllers offer stability, the ANN with Tuned PID controllers exhibit superior adaptability and quicker responses to dynamic variations, thereby enhancing the efficiency of the SHP. Using the ANN-tuned PID controller results in significant improvements in several areas. The settling time is notably enhanced, decreasing by 74.36% compared to the untuned PID controller and 50.88% compared to the tuned PID controller. The overshoot is greatly reduced, showing a decrease of 96.29% compared to the untuned controller and 90.26% compared to the tuned controller, indicating much better stability. Additionally, the peak time increases slightly by 2.78% compared to the untuned controller and 2.14% compared to the tuned controller, demonstrating minimal delay in reaching the maximum value. These changes highlight faster, more accurate, and stable system responses with advanced tuning techniques.Item COMPARATIVE PERFORMANCE ANALYSIS OF LQR, PID, FUZZYPSO AND PSO-PID CONTROLLERS ON QUARTER CAR ACTIVE SUSPENSION SYSTEM(Mekelle University, 2025-04-05) KOKEB GEBREMEDHINThe purpose of this study is to evaluate and compare the effectiveness of various control strategies for active suspension. MATLAB/SIMULINK software is used for both the controller design and the quarter vehicle model. The control strategies PID, LQR, PSO-PID, and FUZZY-PSO are employed. The suspension travel response and sprung mass acceleration response two critical parameters for ride comfort and road handling are chosen, examined, and compared between the responses of the active suspension system and the passive suspension system in order to assess the effectiveness of the vehicle's suspension system. The two Key Performance Indicators (𝐾𝑃𝐼𝑀𝐴𝑋 𝑎𝑛𝑑 𝐾𝑃𝐼𝑀𝐴𝑋) are chosen to demonstrate a decrease in peak overshoot and a decrease in oscillation for the parameters chosen for the active and passive suspension system comparison. From this research, in summary, the PSO-PID and FUZZY-PSO controllers that were created exhibit exceptional performance in enhancing ride quality, hence increasing passenger safety and vehicle handling in the presence of two bumpy road disturbances. When it comes to decreasing to sprung mass acceleration and suspension travel, the PSO-tuned fuzzy logic controller performs the best, followed by PSO-PID, LQR, and PID controllers.