Industrial Control Engineering

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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.
Adaptive Super Twisting Sliding Mode Controller Design of Quadcopter for Wheat Disease Detection
(Addis Ababa University, 2023-11) Nardos Belay; Lebsework Negash (PhD)
Brown wheat rust is a fungal disease that can cause huge destruction in wheat production and quality. Collecting accurate large scale crop data and detecting these diseases based on certain standards through visual inspection is labor intensive, time consuming, and prone to human error. This paper focuses on the design of adaptive super twisting sliding mode controller of a quadcopter for detection of brown wheat rust disease. First, the dynamics of the system was understood then the Newton-quaternion approach was used to model the dynamic system and verified in simulink. Then, the adaptive super twisting sliding mode controller was developed for attitude and position trajectory tracking of a quadrotor. Controller design involves tuning the parameters of the supertwising sliding mode controller using adaptation laws. Comparison of conventional sliding mode controller with the adaptive super twisting sliding mode controller was analyzed. The effectiveness of the proposed control scheme has been verified by developing simulation results for quadcopter in MATLAB/SIMULINK software. The results show high tracking accuracy, chattering reduction, and disturbance rejection capability of the proposed controller. For the task of brown wheat rust detection, transfer learning technique was applied using the state of the art ResNet152v2 model to perform feature extraction for the convolutional neural network architecture. The trained model achieved an accuracy level of 93.28% in the training phase and 92% in the test set.
sEMG based Trajectory Control of Artificial Hand Mechanism using ANN and SMC
(Addis Ababa University, 2023-02) Amanual Tesfaye; Dereje Shiferaw (PhD)
In this thesis, an approach for controlling an artificial hand mechanism using surface Electromyography (sEMG) signal, artificial neural network(ANN) and sliding mode control (SMC) is presented. An artificial hand mechanism also known as a prosthetic hand or bionic hand, is device designed to replace the function of a missing or non-functioning hand. The 3D model of five finger robotic hand was designed using SolidWorks software and then exported to MATLAB/Simulink. This model was then used to extract the kinematic and dynamic properties of the robotic hand for which controller design can be performed and tested. The kinematic model was used to calculate the position and orientation of the hand, while the dynamic model was used to determine the forces and torques required to move the hand fingers to achieve the desired gesture. To use this robotic hand as a replacement for an actual hand lost due to a variety of reasons, integration of this electromechanical system and human biological system is required. To this end, surface Electromyography (sEMG) was used to detect hand muscle activities which were interpreted into six specific hand movements (gestures). By sensing the activities of two muscle groups of the user’s arm, the fingers in the robotic hand was controlled to follow specific trajectories. The classification of the sEMG signals into one of the six gestures was done using Long Short-Term Memory (LSTM) neural network that was trained from a dataset collected from five people. To improve the performance of the LSTM during classification, feature extraction operation of the sEMG signal was performed during training and classification. Feature extraction methods are applied to identify relevant patterns and characteristics from the raw sEMG signals. These characteristics can subsequently be used to categorize and distinguish between various hand movements, enabling the control of the hand using sEMG signals. As a result, the neural network’s accuracy rose from 58.64% when utilizing raw data to 99.3% when using extracted features. Once the required hand gesture was identified from the sEMG signal, interpretation of the gesture into individual fingers joint angles was done using cubicpolynomial path-planning algorithm. These joint angle trajectories were used to command the robotic hand using Sliding-Mode Control (SMC) controller. The reason behind using SMC was its robustness in handling external disturbance, parameter uncertainty and unmodelled dynamics, which are inherent in robotic systems. To test the performance of the controller, parametric uncertainty was added to the system dynamics during simulation. The results show that the SMC controller effectively tracked the desired trajectory in the presence of ±30% link mass variation from their nominal values.
Target Tracking Of Quad-Rotor UAV Using Adaptive Sliding Mode Controller Based on Real Time Image Image-Processing
(Addis Ababa University, 2023-03) Henok Guta; Lebsework Negash (PhD)
Surveillance plays a crucial role in various military and civilian operations, including search and rescue missions. In recent times, unmanned aerial vehicles (UAVs) have gained significant popularity and are considered the ideal resources for such applications. The quad-rotor offers distinct advantages over fixed-wing light vehicles, including costeffectiveness, compact size, and the ability to hover, as well as perform vertical takeoff and landing. However, to ensure reliable performance in various tasks, it is crucial to have a specialized controller that can effectively account for the quad-rotor's nonlinear dynamics, underactuated characteristics, and uncertainties related to parameters and external disturbances. To address these challenges and enhance controller robustness, a slide mode control technique is employed, which offers advantages over traditional PID and other nonlinear controllers. This control design also incorporates image processing capabilities to enable real-time identification and tracking of user-defined targets, providing efficient and accurate performance. This thesis aims to develop real-time target identification and tracking system based on image processing. The system utilizes fast decision-making capabilities and robust flight control techniques to ensure optimal trajectory for a quad-rotor. The image processing component relies on an onboard camera and employs the You Only Look Once (YOLO) algorithm to identify and estimate the continuous motion coordinates of the target. The identification process primarily relies on user-defined criteria such as shape, size, and color. The graphics processor of the embedded software is responsible for accurately calculating the target dynamics relative to a common reference frame of Earth's geographic coordinates. Furthermore, the system selects efficient maneuvers based on time and energy considerations, leveraging the capabilities of the YOLO algorithm. In the presence of parametric uncertainties and external disturbances, the controller effectively minimizes tracking errors within a short time frame, ensuring obstacle clearance and reducing redundancy costs. Through simulation results, the designed controller demonstrates minimal altitude and attitude tracking errors, achieving precise identification and tracking of a userdefined ground target.
AI-Based Mobile Robot for Agricultural Application using Sliding Mode Controller
(Addis Ababa University, 2023-05) Zewdu Jemema; Dereje Shiferaw (PhD)
Recent advancements in agricultural robotic systems have greatly enhanced their functionality, usability, and integration into various tasks, particularly in the field of agriculture. The primary goal of designing agricultural robots is to enhance efficiency, save time, and decrease production costs by incorporating controllers, sensors, actuators, and communication systems. These robots have versatile applications and are widely embraced in the agricultural sector in industrialized countries. Extensive research has been dedicated to developing mobile robot platforms tailored for agricultural tasks, including plant health monitoring, pesticide spraying, fruit picking, and harvesting, with the aim of supporting farmers in developing nations like Ethiopia, where approximately 67% of the population is involved in agriculture. The thesis specifically targets fruit harvesting and focuses on the challenging task of modeling, designing, and simulating a mobile manipulator with advanced capabilities for agricultural businesses, making it one of the most difficult undertakings in this field. The study encompasses the presentation of the mobile manipulator’s 3D design, kinematics, and dynamics. In addition, AI techniques are employed to analyze fruit images, facilitating the accurate detection and determination of fruits. Based on the results, the effectiveness of the training technique has been assessed using an RMSE value of 0.19 and a loss value of 3.6e-02. An SMC utilizes the input generated from the image to govern the mobile manipulator’s position. The system’s stability and robustness have been assessed by considering uncertainties and variations in mass. When comparing the performance of a designed controller with a PID controller in the presence of uncertainty and parameter variation, it was found that SMC outperformed. According to the evaluation using the ITAE, SMC proves to be more effective, demonstrating a 75% improvement compared to the PID controller. Overall, this research contributes to the development of a robust and intelligent mobile manipulator for fruit harvesting in the agricultural sector, with potential applications to support farmers in countries like Ethiopia.
Wind Farm Layout Optimization Using Genetic Algorithm and Pitch Angle Control
(Addis Ababa University, 2021-11) Meskerem, Fanta; Dereje, Shiferaw (Phd0
In this study, MATLAB is used to develop wind farm layout optimization and pitch angle control methods for the wind energy system. The upstream wind turbine reduces the wind speed that passes through the downwind turbine when numerous wind turbines are arranged in close proximity and at random in a wind farm. The wake effect is the name for this phenomena. This effect has an impact on the wind farm's energy production. As a result, while planning and constructing effective wind farms, an optimum layout that takes into account the wake effect is critical. This research proposes a genetic algorithm-based wind farm layout optimization methodology for optimal power output while minimizing wake loss. As a result, power output has increased from 94.25 percent to 96 percent. SIMULINK findings from prior studies for 26, 30, and 32 turbines in a 2000m2 farm area are used to validate this. The change in wind speed is the other most significant difficulty in wind energy systems. The power level rises above the authorized safe level when the wind speed exceeds the rated value of the wind turbine. As a result, the wind turbine rotor is subjected to a highly nonlinear aerodynamic load. This load causes blade fatigue and vibration, resulting in rotor blade damage. To overcome the load and manage the quantity of aerodynamic collected power, an Adaptive Fuzzy PID pitch angle controller is developed in this article. Furthermore, it improved the transient stability of the wind energy system. When compared to the PID controller, simulations show that the suggested controller is superior in terms of feasibility, overshoot, and settling time.
Machine Learning Interfaces for Optimal Design and Control of Solar Thermal Systems in Process Industry
(Addis Ababa University, 2022-06) Fitsum, Bekele; Bhandari, Ramchandra (Prof.); Mengesha, Mamo (PhD)
The trend of introducing solar thermal systems (STSs) in process industries has resulted in a new energy paradigm– an interactive platform where there are economic benefits and motivations to address sustainable development. On the other hand, this paradigm has also introduced fluctuations and uncertainties not previously seen on the energy system, and is challenging the industry. Accordingly, we are observing increasing need for robust design and control solutions that will facilitate the smooth operation and cost competitiveness of the industrial solar thermal system (ISTS). The possibility for developing such a solution exists, but only if the necessity to explicitly model, which might not work well, and also increase computational complexity in the ISTS, is removed. In this dissertation work, a machine learning (ML) approach is followed for design and control optimization of ISTSs, and is leveraged for two goals. First it is used as a multi-modelling tool for developing heterogeneous optimization interfaces, using stochastic and generic models. These interfaces are intended to be simple but are not simpler in order to simultaneously address both scalability-tractability tradeoffs and model inefficiencies of conventional methods. Afterwards, ML enabled linking up of these interfaces as building blocks for realizing a modular optimization framework, and of integrating different layers of functionalities. As a result, the ML approach allowed disaggregated modelling of several similar technologies (and processes) as well as parameterizing of their inputs and local condition differently. Using this method, it was also possible to represent distributed energy resources (DERs) and their additional capabilities of interactions. Furthermore, it allowed replication of simulation experiments with the same model and at varying scale levels. These are essential features that cannot be offered by conventional methods, and can be used to improve synergy and unlock the potentials of DERs in ISTSs. Following the ML approach, some important findings were made. Firstly, the solutions of the optimal design problems were scalable and tractable. This feature facilitates operation-based designs of STSs according to the specific requirements of process(s), heat distribution networks or existing thermal plant in industry. The approach also allowed the testing of an improved optimal control strategy, while at the same time, enabling controller tuning or model calibration. This capability is used to adapt an empirical solar radiation model, to serve as an efficient and low-cost sensor that can be integrated to ISTSs in real-time. However, due to the scope and limitation of the dissertation, these relevant findings provide mainly key design and control strategies and points of discussion instead of benchmarked results. Therefore, it is particularly desirable if further research could confirm these findings.
MRAC Design for A Surveillance UAV for the Detection of Water Hyacinth
(Addis Ababa University, 2021-11) Mihret, Kochito; Lebsework, Negash (PhD)
Water hyacinth, locally named as ’Enboch’, is an invasive aquatic weed posing a great threat to the worldwide aquatic ecosystem. Its existence has been reported to greatly diminish water surfaces’ ecological value causing extensive nutrient reduction. An intuitive, but much feasible and inexpensive solution relies on the early detection of its presence followed by an action. This paper focuses on the design of a controller for a quadrotor able to perform area surveillance specifically suited for the detection of the hyacinth plant. The control design is done by taking the multivariate and non-linear nature of the problem into full consideration. The developed model reference adaptive controller (MRAC) comprising of both a standalone baseline controller and an adaptive augmentation is found to be able to stabilize the system in nominal scenarios and also restores nominal design performance in the presence of disturbances and parametric uncertainties. For the task of water hyacinth detection, the technique of transfer learning have been applied using the state-of-the-art VGG-16 model to perform feature extraction for a CNN architecture. The problem has been formulated as a multi-class classification problem considering three other aquatic plants identified as most probable on the habitats of water hyacinth. The trained model obtained an accuracy level of 93.34% through the training phase, 94.25% on a validation set, and 93% on a testing set.
Model Predictive Control of Unmanned Aerial Vehicle for Locust Detection and Bio-pesticide Spraying
(Addis Ababa University, 2021-11) Eden, Getiye; Lebsewerk, Negash (PhD); Dereje, Shiferaw (PhD)
Swarm of Locust are very harmful for food security, quality and quantity of agriculture products. Ethiopia is one of the countries which is extensively affected by locust invasion. The locust swarms have destroyed large swaths of food and pasture in Ethiopia which lead to famine and displacing thousands of people from their home. Ethiopia battled the swarms by spraying pesticides from air using helicopters leased from FAO. With this consideration, precise locust detection and bio-pesticide spraying is signiﬁcant for preventing locust plagues. This thesis is going to focus on the design of Model Predictive Control of UAV for locust detection and bio-pesticide Spraying. To accomplish this design: First the dynamics of the system was understood then the mathematical model of the system was done and it was based on an agriculture spray drone (JMR-X1400). The Newton-Euler formalism was used to model the dynamic system and veriﬁed in Simulink. The ﬂight controller is designed and MPC is implemented for this thesis. For this non-linear dynamic system of a quad-copter NMPC (non-linear MPC) is chosen. Multiple shooting method is selected to transform the optimal control problem to nonlinear program (NLP). To solve the NLP, CasADi in MATLAB is used and the solver is Ipopt (Interior Point Optimizer). The NMPC was able to control the quad-copter, which means the quad-copter was able to follow the given reference trajectory with minimum control effort. Since the quad-copter is used to spray pesticide, there will be a change in mass when it sprays. For this reason the Recursive Least Square Estimation (RLSE) is used to estimate the mass change and the model can be updated using the estimation. The proposed method works adequately. The RLSE was able to estimate the mass change and the quad-copter was still able to track the reference. Manual monitoring is a labor-intensive job and expensive for large farms. To tackle this problem, image recognition have provided a promising solution for detecting pests. So for this thesis Image recognition system is developed to detect and recognize the Locust swarm. Since it is an Image classiﬁcation, CNN is chosen and the programming language is in python. After passing through different procedures the ﬁnal training accuracy of the machine is 95:19%.
Feedback Linearization with LQR Control Approach for Quadrotor Trajectory Tracking
(Addis Ababa University, 2021-11) Betsega, Yosef; Lebsework, Negash (PhD); Dereje, Shiferaw (PhD)
A quadrotor is a type of helicopter which is lifted and propelled by four rotor.Quadrotor’s are used for variety application due to reason of mechanically simple,small size and low inertia.But controlling quadrotor is a challenging task because of non linearity and underactuacted character of the dynamics. In this thesis work,the dynamic model of the quadrotor is derived,by using NewtonEuler formulation.Then a nonlinear control approach which is a feedback linearization technique is used . Feedback linearization technique is used to linearize both the translational and rotational dynamics.Here during linearization an auxiliary control inputs arises and those linear control inputs are found through linear control technique called linear quadratic regulator.Constant trajectory tracking and rectangular trajectory tracking is also obtained by controlling the attitude and the position simultaneously. The quadrotor dynamic model and the controller design is developed using MATLAB simulation and the dynamics model is tested with step input for each control inputs.The effectiveness of the proposed controller is tested with disturbance and without disturbance through MATLAB simulation.It is observed that,on the constant tracking simulation result the step input response have good settling time response.It achieve the desired 100m position on the settling time of x 19.42 sec, y 19.5 sec and z 11.4 sec .
MPC Based Attitude Control of Quadcopter
(Addis Ababa University, 2021-09) Banchiywab, Aschalew; Lebsewerk, Negash (PhD)
Quadcopter is a type of UAV having two pairs of counter rotating rotors. Its movement is controlled by adjusting the relative speed or relative trust and torque of each rotor which are spun by electric motors. However, quadcopter is nonlinear, underactuated MIMO system and also its inputs and outputs are constrained, which provide a suitable platform for control algorithm development and investigation of both stabilization and trajectory tracking control. Because quadcopter is severely under-actuated and coupled system, a cascade control approach is proposed and applied for trajectory tracking control. Three di erent MPC methods, namely Linear MPC, Feedback linearization based MPC and Nonlinear MPC are designed for attitude control. Moreover, the control of x, y, and z position is also addressed, by utilizing a conventional PID controller, to test the performance of the attitude controllers during trajectory tracking control. The nonlinear mathematical model of a quadcopter's dynamics, solved from Newton's and Euler's laws, is investigated by realizing the designed controllers using MATLAB/Simulink. The introduced attitude controllers are compared based on three performance evaluation factors, tracking accuracy, control e ort e ciency and output disturbance rejection capacity. The performance of the proposed control schemes are veri ed by comparative simulation results and Root Mean Square Error (RMSE) tracking accuracy performance measure. The simulation results shows Linear MPC o er poor performance even if it is simple to design and o er fast response compared with the other. Feedback linearization based MPC ensure the utilization of linear MPC for nonlinear plant, in this case for nonlinear quadcopter model. In addition, Feedback linearization based MPC and Linear MPC strategies couldn't withstand output disturbances. Moreover, Feedback linearization based MPC strategy is incapable to deal with input constraints. Conversely, nonlinear MPC o ers a good performance with constraints and output uncertainty. Even if it is computationally intensive.

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