Industrial Control Engineering

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Energy Management of Three Wheeler Hybrid Electric Vehicle by Using Model Predictive Controller
(Addis Ababa University, 2024-08) Dereje Abera; Dereje Shiferaw (PhD)
In response to environmental issues and declining global crude oil reserves researchers and automobile manufacturers are exploring novel vehicle technologies. Hybrid Electric Vehicles (HEVs) reduce fuel usage and greenhouse gas emissions. The transportation sector consumes around 66% of global oil consumption, with small passenger cars and trucks accounting for 50%. For meeting future energy demands and reducing pollution a power-split hybrid electric car is a possible solution . It combines features of both conventional and electric vehicles. Energy can be managed optimally because it comes from two subsystems: the engine and the battery. This thesis presents a model predictive control approach with constraint handling that outperforms previous strategies for efficient energy management of hybrid electric vehicles. A comprehensive mathematical model of a three wheeler Auto Rickshaw power split hybrid electric car is created, including the engine, planetary gear, motor/generator, and battery. The presented model utilizes an interior-point optimizer-based nonlinear predictive control approach with operational limitations and a cost function. The goal is to reduce fuel usage and keep the battery’s charge within predefined limitations. The generated model was simulated in MATLAB, including motor, generator, engine speed, and battery SoC. The proposed MPC results for the HWFET(modified) and EUDC(modified) cycles show specific fuel consumption of 0.5162 and 0.5817 liters/100 km, respectively. The ADVISOR 2003 rule-based method yields 1.00 and 1.1 liters/100 km for the HWFET(modified) and EUDC(modified) cycles, respectively, supporting these findings.The suggested MPC improves specific fuel consumption by 48.39% and 47.12% in HWFET(modified) and EUDC(modified) drive cycles, respectively. So MPC based three wheeler power split HEVs would play a significant role to significantly lower fuel consumption and environmental pollution and foreign currency to import fuel.
System Optimization and Design of Hybrid Mini Grid System Consisting of Diesel-Solar and Storage Units Using Bacteria-Foraging Algorithm: A Case Study Bohe Village-Somali Region
(Addis Ababa University, 2025-02) Tizita Tesfaye; Dereje Shiferaw (PhD)
Providing a reliable and sustainable energy supply in remote and underserved regions is a signi cant challenge. This study presents the system optimization and optimal control design of a hybrid mini-grid system, combining diesel generators, solar photovoltaic (PV) panels, and battery storage units to meet the energy demands of Bohe Village in the Somali Region of Ethiopia. The optimization is achieved using the Bacteria-Foraging Algorithm (BFA), an advanced technique inspired by the foraging behavior of E. coli bacteria, ensuring the hybrid system's e cient operation in MATLAB. The research starts by analyzing the daily load pro le of Bohe Village, highlighting peak loads and total energy requirements. The hybrid system is then designed, incorporating suitable capacities for the diesel generators, PV panels, and battery storage. The BFA is applied to optimize controller gains for the inverter, boost converter, battery charging/discharging, and energy management system (EMS). The EMS dynamically selects and allocates energy sources, ensuring optimal load distribution to meet demand while preventing overloads. The results show that the hybrid mini-grid system is well-suited for Bohe Village's energy needs. The integration of solar PV and battery storage enhances the system's sustainability and resilience against fuel price volatility and supply disruptions. This case study provides valuable insights and a framework for optimizing hybrid energy systems in similar remote locations. Ultimately, the use of BFA for hybrid mini-grid design o ers a promising solution to energy challenges faced by remote communities, contributing to energy access and sustainable development.
Design And Simulation of An Energy-Efficient Model-Predictive Cruise Controller for Three Wheel Electric Vehicle
(Addis Ababa University, 2025-03) Mistere Getaneh; Dereje Shiferaw
As three-wheel electric vehicles (EVs) gain popularity in developing countries due to their affordability and environmental benefits, challenges related to energy efficiency and utilization remain critical. This research aims to enhance the energy efficiency and dynamic performance of these vehicles through advanced control techniques, specifically Model Predictive Control (MPC). The study identifies a significant research gap in the design of MPC controllers for three-wheel EVs, particularly due to the complexities of nonlinear vehicle dynamics and parameter uncertainties. To address this, we propose an energy-efficient MPC cruise controller for a Bajaj three-wheel EV, utilizing comprehensive mathematical models of vehicle dynamics, energy consumption, the Brushless Direct Current (BLDC) motor, and the inverter. Simulations conducted in MATLAB/Simulink demonstrate the effectiveness of the proposed controller in accurately tracking both constant and varying speed references. The results highlight the controller's ability to maintain a target speed of 35 km/h with minimal deviation, effectively responding to dynamic conditions. For instance, the vehicle accelerates from 0 to 25 km/h in just 10 seconds, reaches 35 km/h by 15 seconds, and decelerates smoothly to 25 km/h at the 30-second mark. This research not only contributes to the optimization of three-wheel EV performance but also lays the groundwork for future advancements in energy-efficient vehicle control systems.
Integral Terminal Sliding Mode with Automatic Gain Tuning Fuzzy Logic Controller to Regulate Evaporator Superheat Temperature in HVAC Systems
(Addis Ababa University, 2024-10) Alemayehu Feyisa; Dereje Shiferaw (PhD)
HVAC systems are inherently complex, and this complexity, along with various disturbances, makes it challenging to control the system effectively. Achieving a balance between meeting strict comfort requirements and minimizing energy use for optimal performance is particularly difficult. This thesis introduces a combined control strategy: the Integrated Terminal Sliding Mode Controller with Automatic Gain Tuning and Fuzzy Logic (ITSMCFLAG). This controller regulates the superheat temperature at the evaporator outlet by adjusting the opening of the electronic expansion valve (EEV). The ITSMC-FLAG controller efficiently manages both the evaporator superheat and evaporating temperature by adjusting the expansion valve’s opening based on the superheat at the evaporator outlet and the inlet mass flow rates. It also takes into account the return room temperature and environmental uncertainties. This advanced approach not only enhances cooling performance and reduces energy consumption, but it also extends the lifespan of the compressor. By combining terminal sliding mode control with fuzzy logic, the ITSMC-FLAG controller improves HVAC performance through a dynamic fuzzy logic auto-gain mechanism that adjusts the system’s gains according to the current operating conditions. This ensures consistent and optimal performance, even with disturbances and fluctuations in system dynamics. Comprehensive MATLAB simulations demonstrate the significant potential of the proposed controller to improve cooling efficiency, reduce energy consumption, and extend compressor life.
Neural Network Based Lower Limb Prosthetic Control Using Super Twisting Sliding Mode Control
(Addis Ababa University, 2025-01) Adisu Tadese; Chala Merga (PhD)
A prosthetic is an artificial limb employed to substitute lost anatomical structures due to disease, injury, or trauma. In this paper, an approach for controlling the prosthetic leg using a surface Electromyography (sEMG) signal, an artificial neural network (ANN), and super twisting sliding mode control (ST-SMC) is presented. The triggering signal is extracted from surface electromyographic (sEMG) signals recorded from the nine muscles at the lower limb, which are grouped into three movement groups. Intense signal analysis that includes filtering, rectification, and linearization is done to obtain the reference sEMG to the NN from the raw sEMG because the raw sEMG data can’t be used as training data for NN. An artificial neural network (ANN) predicts the joint angle for walking, upstairs and downstairs using the processed sEMG signals of the muscles. The super-twisting sliding mode control (ST-SMC) is used to regulate the motion of the prosthetic joints in accordance with the specified reference trajectories. The kinematic model is formulated using forward and inverse kinematics principles, which determine the position and orientation of the prosthetic leg. The dynamic model is based on a fixed coordinate system of human lower leg modeling, formulated by the Euler-Lagrange principle. Rather than using a simple triple pendulum model, the proposed approach presents a more realistic model of the human lower leg. Matlab software’s signal analyzer, neural network fitting packages, and MATLAB/Simulink are used for signal analysis, neural network training, and model simulation. The dynamic system modeling and ST-SMC controller design are implemented. Parameter change analysis and disturbance analysis were done to show that the controller is robust against internal parameter changes and external environmental changes. From the simulation results, it is observed that training the neural network with processed data and implementing the ST-SMC results in increased regression value and decreased mean squared error (MSE) in trajectory tracking instead of using SMC.
Backstepping Fuzzy Sliding Mode Controller for Trajectory Tracking of Mobile Manipulator
(Addis Ababa University, 2024-04) Geta Menyechel; Dereje Shiferaw (PhD)
A Mobile Manipulator (MM) is essentially a robotic arm attached to a mobile platform, which could be designed for space, ground, aerial, or underwater environments. The mobile platform expands the reach of the manipulator, allowing it to access a larger workspace. This increased mobility enhances the ability to position the manipulator in various configurations, leading to more efficient task execution. Mobile Manipulators has complex system structure, highly coupling dynamics between mobile base and mounted manipulator arm, holonomic and nonholonomic kinematics constraints and highly nonlinear characters substantially increase the difficulty in designing a controller for the wheeled mobile manipulator. Designing a robust controller for mobile manipulator with the aim of simultaneous control of the velocity of the mobile platform and the motion of the end-effector is the aim of this thesis work. By employing the concepts of kinematic backstepping control and fuzzy sliding mode torque control, a two-step control approach is introduced for the nonholonomic mobile manipulator. In the first step, the kinematic velocity control is designed to ensure that all desired trajectories are achieved. In the second step, a fuzzy sliding mode torque controller, based on the dynamics of the mobile manipulator, is designed to ensure that the mobile platform’s velocity and the end-effector’s position converge to the reference trajectories generated in the first step. The proposed method stability is proved using Lyapunov theory, and its convergence is mathematically guaranteed. Comparision between BSMC and the proposed BFSMC is conducted in terms of tracking performance in the face of both disturbance and parameter variation and the proposed BFSMC has shown better performance in tracking the given trajectory by rejecting the external disturbances and tolerating the parametric uncertainties results in performance improvement of 31.6%. The effectiveness of the suggested control approach is confirmed through the creation of simulation outcomes using MATLAB/SIMULINK software.
Design of Integrated Guidance and Control for a Missile using Sliding Mode Control Technique
(Addis Ababa University, 2019-10) Addisalem Hailegnaw; Dereje Shiferaw (PhD)
Guided missiles with respect to their capability to engage high speed, highly agile targets and capability to achieve precision end-game trajectory requires extremely accurate performance of all component systems. In order to develop systems that meet the required accuracies, integrated guidance and control (IGC) systems are currently being studied. These IGC architectures infuse the guidance and con- trol systems into a single framework thereby promoting natural synergy among the missile subsystems. Compared with designing the guidance subsystem and control subsystem of missile separately, integrated guidance and control has many superior- ities, such as improving performance, reducing implementation cost and maximizing the adjustability of missile. Integrated guidance and control uses the target states relative to the missile to directly generate fin deflections that will result in target interception. In addition to achieving target interception, the integrated guidance and control has the responsibility for ensuring the internal stability of the missile dynamics. In this thesis Integrated guidance and control for a missile is developed using sliding mode control, taking a more natural guidance parameters which are predicted impact point heading errors as sliding surfaces. Mathematical model of a generic missile is derived by using the equations of motion, and missile/target engagement kinematics equations of the collision course are developed. Theoretical analysis shows the effectiveness of Integrated sliding mode guidance and control and simulations are carried out to show two dimensional and three dimensional missile target engagement scenarios using proportional navigation guidance law, and missile airframe is simulated to show the response to fin deflection so as to integrate the guidance law and control law later.
Particle Swarm Optimization (PSO) tuned Linear Quadratic Gaussian (LQG) Controller Design for Surface to Air Missile Guidance System
(Addis Ababa University, 2018-06) Getasew Mekonnen; Dereje Shiferaw (PhD)
Missile guidance system is a well-known nonlinear control engineering area of research. Many technologies have been developed to improve control performance, robustness and to overcome environmental disturbances. This thesis employs a particle swarm optimization (PSO) algorithm to solve the weighting matrices selection problem of linear quadratic Gaussian (LQG) for controlling surface to air Missile guidance system. One of the major challenges in the design of LQG for real time applications is the optimal choice of the state and input weighting matrices (Q and R) respectively, which play a vital role in determining the performance and optimality of the controller. Commonly, trial and error approach is employed for selecting the weighting matrices, which not only burdens the design but also results in non-optimal response. Hence, to choose the elements of Q and R matrices optimally, a PSO algorithm is formulated and applied in the design of linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) for control of surface to air Missile. It is also intended to produce better robustness with the help of particle swarm optimization (PSO) algorithm as an optimization tool. Indeed, the system’s mathematical model has been developed and also the properties of the uncontrolled system have been analyzed. The model developed shows that the Missile system considered is a 2x2 MIMO (multiple input multiple output) system. Since the system is MIMO, the interaction of the inputs with the outputs has been analyzed using relative gain array (RGA) analysis and frequency domain analysis of the system transfer functions. Also, the condition number of the missile system is calculated and it has small value i.e. “1” which implies there is no control problem for the plant. Then, optimal state feedback controllers have been developed. Here, LQR and LQG controllers are developed. The performance of the controllers designed by manual tuning and PSO-tuning has been analyzed and compared. Besides the performance, the robustness of the controllers developed has been analyzed. The robustness analysis is done by evaluating the singular values of the loop gains using singular value decomposition (SVD) at a certain frequency and for a specified frequency range. Finally, comparative analysis between the designed controllers is carried out. The proposed PSO tuned design methodology has resulted good Performance i.e. The PSO tuned LQR and LQG controller improved the steady state error and peak response from (0.1043 to 0), (1.043 to 1) and (0.1723 to 0), (1.1723 to 1) m/s2 respectively in Y direction. In addition, the PSO tuned design has also resulted improvements in robustness of the control systems i.e. The PSO and manually tuned LQG PM is 65.0010and 30.31220 respectively. Indeed, the loop transfer recovery (LTR) approach is employed at the input to recover the robustness of the manual linear quadratic Gaussian (LQG) controller, which resulted in the improvement of the robustness at the input i.e. the singular value increases or altered from -17.345dB to -5.625dB. The thesis has also suggested further research work in the control of Missile system.
Experimental Investigation and Optimization of Wear Property of Al-TiB2-Gr Hybrid Composite
(Addis Ababa University, 2024-06) Daniel Mekasha; Desalegn Wogaso (PhD)
In recent days, monolithic metals and conventional alloys are being replaced by aluminum metal matrix composites in many applications in the field of automotive and aerospace due to a high strength-to-weight ratio, fracture limit, toughness, wear, and sustaining properties at higher temperatures. This investigation thus deals with the fabrication of aluminum metal matrix hybrid composites by stir casting technique with reinforcement of TiB2 and Gr in varying percentages through the stir casting process. Reinforcements are added in 10% TiB2 and 0%, 2%, 4%, 6%, and 8% in volume percentage of graphite particulate into Al 6061 for composite preparation. The wear and hardness properties of the hybrid composite material were studied and a microstructural examination was presented. The wear performance of the specimens was measured using the pin-on-disc technique and optimization of process parameters was conducted according to the DOE and optimized by Taguchi analysis. The results showed that the reinforcements twinned and measuring only a few μm in size, were embedded in the aluminum matrix, having an equiaxed grain structure. By the addition of 2% of graphite reinforcement, the microhardness value was drastically improved by 45.6%, from 23.2 to 33.8HV. From Sample 2 to Sample 6, the hardness increased gradually. The Maximum hardness value for this experiment, 43.43HV, was obtained in 10% reinforcement composition to give a total of 86.59% improvement in hardness. The COF from the pin-on-disk test also showed a 20.52% decrement, from 0.54 to 0.38um, at 10% composition of Gr particle because of its lubricating characteristics. The wear rate of composites reinforced with 8% graphite and 10% graphite declined by 83.18% and 85.77%, respectively, showing that the wear rate also decreases as graphite composition increases. The optimum parametric condition for COF and wear rate was achieved when the combination of factors is at a Load of 30N, at a Speed of 200RPM, and for 10 minutes with confirmation of test also supporting the conclusion with a 10.11% and 7.37%, respectively, deviation from the predicted value.
Joint Position Control of aWalking Humanoid Robot with MPC Controller
(Addis Ababa University, 2024-06) Afomiya Megersa; Dereje Shiferaw
With much of the industrial processes in Ethiopia relying on human labor, people are obliged to perform hazardous and monotonous tasks, such as lifting heavy objects, working in contaminated environments, and engaging in repetitive activities. Unfortunately, this places human lives at risk and exposes their health to potential harm. Moreover, these conditions also negatively impact the industry itself, leading to decreased production quantity and quality. The main objective of this research is to model and simulate a humanoid robot with each leg having 4 degrees of freedom (DOF) and each arm having 4 DOF, enabling it to carry heavy loads and move to different locations. To control the joint position of the robot, an MPC has been implemented and a comparison with a LQR has been done to evaluate the performance. Particle swarm optimization technique has been utilized to tune parameters of the controller yielding better performance. The approach first started with a thorough understanding of problem followed by a 3D modeling of a humanoid robot model in Solidwork. Further with the exported model in Simulink, different sets of angles were assigned to the robot joints so as to imitate human motion. To provide transition from a departure to destination, a sets of way points have been defined in MATLAB and the controller’s ability to transit the dynamic system in a stable manner have been tested. The pure pursuit controller has been implemented to track the path, allowing the humanoid robot to navigate through the defined set of points. The implemented MPC has been found to provide efficient trajectory tracking as compared to LQR. The comparison have been further analyzed by calculating the peak error value, where the MPC Controller provided better tracking performance with peak error values of 0.13 in shoulder joint and 0.18 in both hip and knee joint as compared to error values of 0.38 and 0.58 obtained while implementing LQR.
Adaptive Radial Basis Function Neural Network Based Hierarchical Sliding Mode Controller for 2-Dimensional Double Pendulum Overhead Crane
(Addis Ababa University, 2024-01) Wosene Yirga; Dereje Shiferaw (PhD)
Several control methods for an overhead crane modeled as a double pendulum with constant cable length have been published in various studies. Most of the proposed control methods were open-loop and linear control methods or nonlinear control methods that fully depended on the system model.However, the dynamic of an overhead crane is a complex nonlinear function of uncertain or unknown parameters, which reduces the performance of such control methods. In this thesis, an adaptive radial basis function neural network-based hierarchical sliding mode controller (ARBFNNHSMC) is designed to control a 2-dimensional overhead modeled as a double pendulum system with variable cable length using the Lagrange equation of motion. To reduce the chattering effect of the sliding mode controller as well as increase its robustness, ARBFNN is designed to estimate unknown or uncertain nonlinear functions in the system. The overall control law, which contains only some parts of the crane model, is designed, and the adaptation law is derived from the Lyapunov stability condition to update the weight of the network based on observed errors. The proposed control strategy and derived model are verified using MATLAB/Simulink software.For the same controller parameters,500% changes in model parameters are taken, and trolley displacement settling time and rising time for HSMC are 12.3 seconds and 6.95 seconds, respectively. On the other hand, the maximum hook’s and payload’s swing angles are around 1.34 deg and 1.9 deg for HSMC, and it is around 1.04 deg and 1.64 deg for ARBFNN-HSMC. The residual hook’s and payload’s swing angles are 0.0137 deg and -0.0319 deg, respectively, in the case of HSMC and -0.0011 deg and -0.0022 deg for ARBFNN-HSMC. This numerical result shows that ARBFNN-HSMC has better performance than HSMC for large parameter variations. In addition, the controller output of ARBFNN-HSMC is smoother than that of HSMC, as evidenced by the result.
Boiler Drum Water Level Control Using Fuzzy Sliding Mode Controller
(Addis Ababa University, 2022-08) Jemila Wudu; Dereje Shiferaw (PhD)
Boiling water to create steam is a crucial step in the process industries. An important part of this process boiler drum water level control. For a variety of reasons, it's crucial to keep the drum's water level at the proper level. When the water level is too high, the steam purification equipment floods, which allows water and contaminants to leak into the steam system. The effectiveness of the treatment and recirculation function is decreased by a too-low water level. It is typically challenging to control the drum water level of the boiler due to the system's significant disturbance (like steam disturbance), nonlinearity in mathematical modeling, strong coupling between input and output parameters, and multivariable features. To overcome this problem a sliding mode controller with fuzzy logic controller (FSMC) is proposed. Also PID and SMC applied to the system in contrast. Since the drum water level controller's task is to level the boiler drum at startup point which is 10 perunit and keep it there at steady steam load. The results prove that sliding mode controller with fuzzy leads to better performance in overshoot, settling time and chattering effect elimination than PID and SMC controller. There is no overshoot in FSMC or 0 overshoot, has quick settling time which is 41.9228sec and no chattering effect. However, sliding mode controller exhibit fast rise time which is 13.1080sec than PID and FSMC which is 30.9 and 15.1947sec respectively. Also exhibit high chattering effect. This conduct is improper for a mechanical force or other physical indication. Additionally, in this work it is proven that when the amount of steam mass flow rate disturbance increase, achieving desired trajectory using PID controller decrease. But a desired trajectory can be achieved using SMC and FSMC whenever the steam mass flow rate disturbance increases. The water level control system is verified and simulated using MATLAB software.
Fractional Order Sliding Mode Controller Design for Fixed-wing Unmanned Aerial Vehicles Trajectory Tracking
(Addis Ababa University, 2024-01) Enanwo Wondem; Lebsework Negash (PhD)
This thesis focuses on the tracking of trajectories for fixed-wing unmanned aerial vehicles. (FWUAVs) using Fractional Order Sliding Mode Controller (FOSMC) . FWUAVs are widely utilized in both military and civilian sectors due to their ability to perform risky or inaccessible operations. However, controlling FWUAVs is challenging due to their nonlinear and coupled nature. The mathematical model of FWUAVs is complex, incorporating physical laws, Newton and Euler formulations, and coordinate systems with transformation matrices. To solve this complexity, a FOSMC ,which combines the robustness of conventional Sliding Mode Controller (SMC) with flexible fractional calculus, is applied. Particle Swarm Optimization (PSO) is used for tuning the control gains of FOSMC. The Integral Time Absolute Error (ITAE) is employed as a performance metric, aiming to minimize both settling time and overshoot. External disturbances and parameter variation is added to evaluate the performance of the controller. Comparison is done between Linear Quadratic Regulator (LQR), Fractional Order PID (FOPID), SMC and FOSMC for pitch angle using step input. FOSMC performs better than LQR, FOPID and SMC in terms of tracking accuracy, speed of response and overshoot. Open loop model verification and overall control system of FWUAV is done by MATLAB/Simulink software.
Robust Model Reference Adaptive Controller For Trajectory Tracking Of Fixed-Wing UAV
(Addis Ababa University, 2023-09) Tofik Kemal; Lebsewerk Negash (PhD)
In recent years, the application of UAVs has increased. Fixed-wing Unmanned Aerial Vehicle (UAV) is an airborne vehicle that is largely used for surveillance, reconnaissance, monitoring, and data collection or to patrol an area that is not safe for a human being. This thesis addresses Robust Model Reference Adaptive Control for trajectory tracking of fixedwing UAV. The fixed-wing UAV is under actuated system and due to this reason controlling all six degrees of freedom directly is impossible. To overcome this problem, the proposed control algorithm has two loops inner(attitude) and outer(position)loop. The outer loop provides the pitch and yaw angle reference trajectories for the inner loop. The inner loop controls attitude(roll, pitch, and yaw angle). First, fixed-wing UAV dynamic models are driven using the Newton-Euler approach, and the dynamic models are decoupled to reduce complexity. The decoupled dynamics have six second-order Single Input Multiple Output (SIMO) systems. Second, a conventional Model Reference Adaptive Control (MRAC) is designed. However, this controller causes instability in the presence of unmatched uncertainty. Third, Robust Model Reference Adaptive Control (RMRAC) is developed to prevent parameter drift in off-nominal scenarios. This thesis addresses different robust modification techniques like σ-modification, e-modification, and optimal modification techniques. These control algorithms are tested on different trajectories and a comparative analysis is made. Lyapunov direct method is used as a mathematical tool for design and stability analysis. The performance of the proposed control strategy is verified by developing simulation results in MATLAB/SIMULINK software. Finally, the developed Robust Model Reference Adaptive Controller is tested for parametric uncertainty and external disturbance. The simulation result shows that the proposed controller is able to track the desired trajectory in the presence of external disturbance(wind gust environment) and parametric variation.
Twisting Sliding Mode Control Design with Particle Swarm Optimization for Fixed Wing UAV
(Addis Ababa University, 2023-11) Haileleul Biazn; Lebsework Negash (PhD)
Control of xed wing unmanned aerial vehicles (FW-UAVs) is challenging due to their highly coupled, complex, nonlinear, and uncertain mathematical model, and underactuated dynamics. To overcome this di culty a nonlinear robust twisting sliding mode control (SMC) was designed for inertial positions, attitudes and airspeed control to track the desired trajectories. Fuzzy based switching for inertial positions, and airspeed control was applied to overcome the trade-o between chattering and robustness. Where as, quasi-static saturation switching was used for attitude control. Particle swarm optimization (PSO) was used for optimizing the gain parameters of the designed controllers. Di erent set of trajectories; bow-tie, helical, and real data minimum snap polynomial paths were prepared for guiding the FW-UAV. The required real data way-points were taken manually using 'Google Earth Pro' as latitudes and longitudes in units of degree. These data points converted into coordinates in earth-centered-earth- xed frame, and then to the north-east down frame. Using these sample points minimum snap polynomial trajectories developed through minimizing snap as a quadratic programming. The overall mathematical model has been prepared in MATLAB Simulink for simulation. Finally, the performance of controllers was evaluated against disturbances, model uncertainty, parameter variation, and including actuator dynamics. Integral-time-absoluteerror (ITAE) has been used as performance index and in all trajectories the controllers were robust within 93% accuracy about the nominal value. The control was achieved within practically accepted ranges of control e ort. Nominal de ection angles of aileron (14◦), elevator (34◦), and rudder (22◦) were required, which are in the working ranges of practical control, unity rad. The thrust force generated by the propeller has an average value of 133 N, which is nearly equivalent to the weight of UAV, 132.5 N. The propeller engine acceleration had maximum values of 10 m/s (376 revolutions per minute) and 30 m/s (1128 revolutions per minute) in helical and level ights respectively.
Trajectory Tracking Control of ISR Quadrotor UAV using GA Optimized Fuzzy-PID Based Neural Network Controller
(Addis Ababa University, 2023-10) Nigatu Wanore; Lebsework Negash(PhD)
The ISR Quad-Rotor is an unmanned aerial vehicle (UAV) utilized for intelligence gathering, surveillance, and reconnaissance missions. This thesis focuses on modeling and controlling the quad-rotor to identify and track a Person of interest using an intelligent control technique. The initial step involves deriving a nonlinear mathematical model for the 6DOF (six degrees of freedom) Quadrotor UAV using the Newton-Euler formalism. To ensure trajectory tracking of the quad-rotor, a Fuzzy Proportional-Integral-Derivative (Fuzzy-PID) controller, tuned with a Genetic Algorithm (GA), was implemented to generate data for a neural network (NNFPID-GA). The fuzzy logic approach facilitates parameter adjustment based on predefined fuzzy rules, while the GA algorithm determines the scaling factors of the Fuzzy-PID controller. The proposed control system was designed based on input-output data from the GA Optimized Fuzzy-PID controller. A network was trained using the Levenberg-Marquardt backpropagation algorithm with the assistance of the MATLAB®NN Toolbox. MATLAB®simulations were conducted to validate the effectiveness of the proposed control algorithm. Additionally, a flight test were performed using the UAV Toolbox to assess the stable flight performance of a developed GA Optimized Fuzzy-PID based neural network(NNFPID-GA) controller for the Quadrotor UAV. To evaluate the performance of both controllers, a comparison study based on performance metrics was conducted. Even though both controllers offer faster response in terms of settling time and improved performance, with less overshoot and better robustness in handling parameter variation and disturbance rejection capability, the GA Optimized Fuzzy-PID based neural network controller(NNFPID-GA) outperforms the GA Optimized Fuzzy-PID controller(FPID-GA). Finally, in order to generate trajectories for the controller, a face recognition system using Python were implemented. The training results demonstrate an accuracy of 98.65%, indicating that the system can effectively distinguish between a wanted person (Person of interest) and other individuals.
Trajectory Tracking of Fixed-Wing UAV Using Fuzzy-Based Sliding Mode Controller
(Addis Ababa University, 2023-10) Feleke Tsegaye; Lebsework Negash (PhD)
The work in this thesis mainly focuses on trajectory tracking of fixed wing unmanned aerial vehicle (FWUAV) by using fuzzy based sliding mode controller (FSMC) for surveillance applications. Unmanned Aerial Vehicles (UAVs) are general-purpose aircraft built to fly autonomously. This technology is applied in a variety of sectors, including the military, to improve defense, surveillance, and logistics. The model of FWUAV is complex due to its high non-linearity and coupling effect. In this thesis, input decoupling is done through extracting the dominant inputs during the design of the controller and considering the remaining inputs as uncertainty. The proper and steady flight maneuvering of UAVs under uncertain and unstable circumstances is the most critical problem for researchers studying UAVs. A FSMC technique was suggested to tackle the complexity of FWUAV systems. The trajectory tracking control algorithm primarily uses the sliding-mode (SM) variable structure control method to address the system’s control issue. In the SM control, a fuzzy logic control (FLC) algorithm is utilized in place of the discontinuous phase of the SM controller to reduce the chattering impact. In the reaching and sliding stages of SM control, Lyapunov theory is used to assure finite-time convergence. A comparison between the conventional SM controller and the suggested controller is done in relation to the chattering effect as well as tracking performance. It is evident that the chattering is effectively reduced, the suggested controller provides a quick response with a minimum steady-state error, and the controller is robust in the face of unknown disturbances. The designed control strategy is simulated with the nonlinear model of FWUAV using the MATLAB® / Simulink® environments. The simulation result shows the suggested controller operates effectively, maintains an aircraft’s stability, and will hold the aircraft’s targeted flight path despite the presence of uncertainty and disturbances.
MRAS Based Sensorless Speed Control of Permanent Magnet Synchronous Motor Using Fuzzy Logic-PI Controller
(Addis Ababa University, 2023-10-20) Yimam Yimer; Mengesha Mamo
The accurate control of Permanent Magnet Synchronous Motors (PMSMs) without the need for mechanical speed sensors is a significant challenge in various industrial applications. This thesis proposes a Model Reference Adaptive System (MRAS) based sensor less speed control technique for PMSMs using a fuzzy logic-PI controller. The MRAS estimator utilizes a reference and adjustable models to estimate the (rotor) speed of the motor. The fuzzy logic-PI controller combines the advantages of fuzzy logic control and proportional integral control to improve control performance and speed regulation. The study investigates the performance of Proportional Integral (PI) and Fuzzy Logic-Proportional Integral (FL-PI) controllers under various conditions, including load and no-load scenarios, parameter variations and the influence of disturbances like sudden load torque changes. The simulation results reveal that the fuzzy logic-PI controller reduced overshoot to 2.6% from 5% and eliminated steady state error under no-load conditions. It exhibited a 3% overshoot during sudden load changes, the PI controller’s 3.9%. the Fl-PI controller consistently outperforms the PI controller in terms of overshoot, settling time and steady state error across different operating conditions. Moreover, the Fl-PI controller demonstrates robustness to parameter variations and effectively mitigates the impact of disturbances on motor speed and current. These findings highlight the superior performance and robustness of the Fl-PI controller, making it a promising choice for speed control in PMSM applications.
Enhancing Trajectory Tracking Accuracy in Three Wheeled Mobile Robots using Backstepping Fuzzy Sliding Mode Control
(Addis Ababa University, 2023-10) Yebekal Adgo; Lebsework Negash (PhD)
The rise in robotics technology has led to increased interest in three-wheeled mobile robots (TWMRs) due to their agility and adaptability across various applications. However, effectively controlling TWMRs presents a significant challenge owing to their inherent nonholonomic constraint, which restricts their independent movement in all directions. Additionally, factors like sensor noise, nonlinear system dynamics, and uncertain system parameters add to the complexity controlling of TWMRs. This research endeavors to enhance the precision of trajectory tracking in TWMRs. Specifically, it employs Backstepping Fuzzy Sliding Mode Control (BFSMC) with parameters optimized through Particle Swarm Optimization (PSO), coupled with the Extended Kalman Filter (EKF) for state estimation. The study conducts a comprehensive performance comparison between BFSMC and BSMC across various trajectory patterns, revealing substantial improvements in trajectory tracking accuracy with BFSMC. BFSMC demonstrates improved performance compared to BSMC across various trajectory types, quantified by calculating the percentage improvement in trajectory tracking using Integral Absolute Error (IAE). Specifically, it achieves a 51.97% improvement for circular trajectories, an 82.09% improvement for infinity trajectories, and an 84.073% improvement for spiral trajectories.. Moreover, BFSMC demonstrates superior robustness in the presence of disturbances, noise, parameter variations, and unmodeled dynamics compared to BSMC. The integration of the Extended Kalman Filter further improve accuracy, particularly in noisy conditions. Simulation results conducted using MATLAB/Simulink software validate the effectiveness of this approach in achieving superior trajectory tracking accuracy in TWMRs.
Trajectory Tracking Control of Quadcopter using Fuzzy Super Twisting SMC with PID Surface for Wheat Yield Estimation
(Addis Ababa University, 2023-11) Elisabeth Andarge; Elisabeth Andarge (PhD)
Accurate yield estimation during the heading stage of wheat production is pivotal for efficient harvest planning and ensuring food security. However, traditional yield estimation technique is labor-intensive and potentially harmful to the crop. Alternatively, the use of satellite imagery for estimation is hindered by its poor resolution. Another approach involves employing low-altitude quadcopters to capture high-resolution images. Yet, controlling flight of these quadcopters presents challenges due to their nonlinear and underactuated characteristics. This thesis aims to address these challenges by designing a flight controller capable of controlling the quadcopter to track the desired trajectory using Fuzzy super twisting sliding mode controller with PID surface to capture images and estimate wheat yield. The process begins with modeling the quadcopter using the Newton Euler method, followed by designing a flight controller. This controller is divided into inner and outer loops capable of automatically adjusting its parameters, and the system is then simulated in MATLAB/Simulink. Wheat head images acquired from the quadcopter are trained using transfer learning in YOLOv8. Parameters such as kernel weight, the number of kernels per head, and sampled area are extracted from the field. The controller’s performance is rigorously assessed through various trajectory and disturbance scenarios, comparing it with sliding mode controller both with and without parameter variation. The results demonstrate the controller’s efficiency in guiding the quadcopter along predefined trajectories, robustly rejecting disturbances, and effectively handling parameter variations. In the realm of image processing, the system exhibited notable advancements in both training and validation accuracy. In summary, the proposed controller enhances robustness, the capacity to handle parameter variations, disturbance rejection, chattering minimization, and controller effort reduction. Utilizing the trained weight parameters, the system can accurately detect and count wheat heads, ultimately providing an estimation of wheat yield.

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