Proportional Integral Derivative (PID) controllers are used in general to control a system, for example a DC motor system. The difficulty of using the controller is parameter tuning, because the tuning parameters still use the trial and error method to find the PID parameter constants, namely Proportional Gain (KP), Integral Gain (KI) and Derivative Gain (KD). In this case, the genetic algorithm method is used which can give better results in each iteration. Genetic algorithms are one of the smart methods inspired by the process of natural selection, the process that causes biological evolution, this concept is applied to tuning PID parameters. This research uses the Matlab simulation method and applies the simulation results to the DC motor hardware using the Arduino Uno. The genetic algorithm method gives a system that has a better steady time and a smaller maximum spike than the Trial and Error method. The test process produced the two best data with an overshoot value = 2, settling time = 13.5 and rise time of 2.7872 and the PID parameter value for mutation of 1 was KP = 3.7500; KI = 1.3184 and KD = 0.2051. Then the value of the best PID parameter on Crossover is 0.4, which is KP = 4.2090; KI = 1.2012 and KD = 0.2539 with an overshoot value = 2, settling time = 18 and rise time = 2.6462.
Saat ini, pengendali Proportional Integral Derivative (PID) digunakan secara umum untuk mendapatkan solusi optimum. Solusi dikatakan optimum apabila output di kehidupan nyata sesuai dengan output yang telah ditentukan. Oleh karena itu, pengendali adalah suatu hal yang dibutuhkan. Tantangan dalam menggunakan pengendali adalah tuning parameter untuk mencari konstanta parameter PID seperti Proporsional Gain (KP), Waktu Integral (KI) dan Waktu Derivatif (KD). Untuk memaksimalkan kinerja motor DC, pengaturan pengendali PID yang tepat merupakan hal yang sangat penting. Desain pengendali PID sebagai pengendali motor DC sudah sering dilakukan. Penggunaan pengendali PID membutuhkan pengaturan parameter yang tepat untuk mendapatkan kinerja yang optimal pada motor. Metode yang umum dalam menentukan parameter pengendali PID adalah trial and error. Namun hasil yang didapat tidak membuat pengendali PID optimal dan justru akan merusak sistem. Oleh karena itu, penelitian ini menggunakan salah satu metode penalaan parameter PID dengan menggunakan metode cerdas berbasis Genetic Algorithm (Algoritma Genetik) untuk mengoptimasi dan menentukan parameter yang tepat dari PID. Algoritma genetik adalah salah satu algoritma yang menggunakan genetika sebagai model algoritmanya. Algoritma genetik terinspirasi dari meniru proses seleksi alam, yaitu proses yang menyebabkan evolusi biologis. Konsep inilah yang diadaptasi dan diterapkan dengan baik untuk menala parameter PID. Penggunaan metode algoritma genetik dapat memberikan hasil yang lebih baik pada setiap iterasinya. Hasil penelitian menunjukkan bahwa overshoot yang dihasilkan karena adanya respon kecepatan setelah penambahan PID adalah kurang dari 10%. Currently, Proportional Integral Derivative (PID) controllers are generally used to obtain the optimum solution. The solution is said to be optimum if the output in real life matches the output determined. Therefore, the controller is needed. The challenge in using the controller is tuning parameters to find constants of PID parameters such as Proportional Gain (KP), Integral Time (KI) and Derivative Time (KD). In order to maximize the performance of a DC motor, proper PID controller settings are crucial. The design of PID controllers as DC motor controllers has often been done. The use of a PID controller requires setting the right parameters to get optimal performance on the motor. The common method for determining PID controller parameters is trial and error. However, the results obtained do not make the PID controller optimal and will actually damage the system. Therefore, this study uses one of the PID parameter tuning methods by using an intelligent method based on Genetic Algorithm to optimize and determine the appropriate parameters of PID. Genetic algorithm is an algorithm that uses genetics as a model algorithm. Genetic algorithms are inspired by imitating the process of natural selection, the process that causes biological evolution. This concept is well adapted and applied for tuning PID parameters. The use of genetic algorithm methods can give better results in each iteration. The results showed that the resulting overshoot due to the speed response after the addition of PID was less than 10%.
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