Browsing by Subject "Electric control equipment"

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    Thermal investigation of a thermoelectric cooler based on Arduino and PID control approach
    (Elsevier Ltd, 2022) Kherkhar A.; Chiba Y.; Tlemçani A.; Mamur H.
    In this study, an experimental and numerical approach is used in order to evaluate the thermoelectric cooler (TEC) control performance and efficiency. For this purpose, the refrigeration system is designed by using the semi-conductor material operating under Peltier effect, and Arduino device. The efficiency of the system is investigated through the performance coefficient and temperature span for carrier fluid between the hot and cold exchanger by using the prototype developed recently at Medea University. In addition, the proportional-integral-derivative (PID) is used in order to maintain temperature control and heat transfer of the system TEC in a closed-loop through the driving circuit, which is specially designed for the TEC can conveniently adjust the input current, which passes through the refrigerator so as to fully make use of quick cooling power advantages. The main obtained results including, the maximum coefficient of performance registered is 0.73 to 0.1 with a temperature span about of 51 °C, by inputting current of 5 A within a control temperature range 0-30 °C, while targeting a temperature of 5 °C at room temperature for the proposed control system had a control time of 21 s, with only a discrepancy of ±0.1 °C. The experimental results confirm that during the time interval 0-20 min, the inside temperature of thermoelectric refrigerator has been decreased rate of 1.5 °C/min. It was shown through the different simulation results with PID controller by taking kp = 0.9, ki = 0.15, and kd = 0, that the cooling temperature decreases over time to 5 °C, which means that these systems work in time-dependent conditions. The proposed controller is able to reach an error of 0.1 °C with minimal overshoot under than 20 s. © 2022 The Authors.
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    Altitude control of quadcopter with symbolic limited optimal discrete control
    (Springer Science and Business Media Deutschland GmbH, 2024) Özbaltan M.; Çaşka S.
    In recent years, quadcopter UAVs have been extensively utilized. Controlling quadcopters is a major concern, and researchers are actively studying it. In this study, altitude control of a quadcopter UAV is achieved using the symbolic limited optimal discrete controller synthesis technique. The resulting controller is compared with the adaptive PID control method, where the PID controller’s parameters are determined using the Dragonfly algorithm. The findings show the superior performance of our approach. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.
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    Design of a Novel PID Controller Based on Machine Learning Algorithm for a Micro-Thermoelectric Cooler of the Polymerase Chain Reaction Device
    (Institute of Electrical and Electronics Engineers Inc., 2024) Arikusu Y.S.; Bayhan N.
    The combined use of Extreme Gradient Boosting (XGBoost) algorithm, one of the machine learning (ML) methods, and a generalization of Hermite-Biehler theorem to obtain a novel PID controller that will ensure robustly stable and optimized operation of a micro thermoelectric cooler (Micro-TEC), which is the main part of a Polymerase chain reaction (PCR) device, is a unique approach of our study compared to previous studies. Therefore, we first established a mathematical model of the micro-TEC by making real-time measurements and then, a new data set was created to find the optimum parameter values of PID controller, and finally, XGBoost Hyperparameters with GridSearchCV was used for the first time to predict PID controller parameters. The XGBoost algorithm achieved 97% training success and 91% test success in estimating the parameters of the PID controller. Moreover, the novel controller developed using the XGBoost algorithm in this study has an impressive speed of 3 seconds. Additionally, our proposed method was compared with various metaheuristic optimization algorithms in terms of error percentage. The error percentages of XGBoost, the equilibrium optimization, the particle swarm optimization and the artificial bee colony optimization algorithms were found to be 0.4%, 1.1%, 3.7% and 11.1%, respectively. It is observed the settling times of micro-TEC with ML-PID controller for all five PCR cycles are 4.86, 44, 83.4, 123 and 162.5 seconds, respectively, and the overshoot values are below 5%. The proposed method gave the smallest settling time, error and overshoot percentages compared to these metaheuristic optimization algorithms. © 2013 IEEE.