Kontrol Kecepatan Motor DC Dengan Estimator Kecepatan Extended Kalman Filter Dan Kontroler Proportional Integral Berbasis Particle Swarm Optimization

Asmara, Kiet Pascal (2023) Kontrol Kecepatan Motor DC Dengan Estimator Kecepatan Extended Kalman Filter Dan Kontroler Proportional Integral Berbasis Particle Swarm Optimization. Other thesis, Institut Teknologi Sepuluh Nopember.

Text 07111940000110-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 October 2025. Download (2MB) | Request a copy

Abstract

Motor DC merupakan salah satu motor yang paling banyak digunakan dalam industri maupun keseharian. Untuk berbagai aplikasi dari motor DC, pengendalian kecepatan dan torsi secara presisi diperlukan, menggunakan sensor kecepatan seperti tachogenerator untuk mengetahui nilai kecepatan dari motor. Tetapi, khususnya dalam aplikasi industri, sensor tersebut cukup mahal dan juga meningkatkan kemungkinan munculnya gangguan dari kegagalan sensor. Maka, dikembangkan teknik kendali sensorless yang dapat mengestimasi kecepatan motor dengan sinyal listrik, tanpa sensor kecepatan. Pada penelitian ini, akan digunakan estimator Extended Kalman Filter (EKF) dan kontroler Proportional Integral (PI) berbasis Particle Swarm Optimization (PSO). EKF akan mengestimasi kecepatan rotor menggunakan pengukuran tegangan dan arus dari sensor listrik. Lalu, input arus jangkar akan dikendalikan oleh kontroler PI yang dioptimisasikan dengan algoritma PSO. Algoritma PSO akan mencari nilai parameter PI optimal dengan mengoptimisasikan nilai error dan respon transien dalam fungsi objektif. Algoritma PSO tersebut menghasilkan parameter PI optimal bernilai (3.9406, 20.6850) yang menghasilkan overshoot sebesar 8.83%, settling time sebesar 0.678s, rise time sebesar 0.0312s, dan ITAE sebesar 1.1667. EKF menghasilkan kecepatan estimasi dengan RMSE sebesar 1.538, yang lebih kecil dibandingkan kecepatan aktual yang memiliki RMSE sebesar 2.045.
================================================================================================================================
DC motors are one of the most widely used motors in industry and everyday life. For many applications of DC motors, precise control of speed and torque is required, using a speed sensor such as a tachogenerator to determine the speed value of the motor. However, especially in industrial applications, these sensors are quite expensive and also increase the possibility of interference from sensor failures. Thus, a sensorless control technique was developed which can estimate the motor speed with an electrical signal, without a speed sensor. In this study, the Extended Kalman Filter (EKF) estimator and Proportional Integral (PI) controller based on Particle Swarm Optimization (PSO) will be used. EKF will estimate the rotor speed using voltage and current measurements from electrical sensors. Then, the armature current input will be controlled by the PI controller which is optimized with the PSO algorithm. The PSO algorithm will find the optimal PI parameter value by optimizing the error value and transient response in the objective function. The PSO algorithm produces optimal PI parameters worth (3.9406, 20.6850) which results in an overshoot of 8.82%, a settling time of 0.6783s, a rise time of 0.0312s, and an ITAE of 1.1667. EKF produces an estimated speed with an RMSE of 1.538, which is smaller than the actual speed which has an RMSE of 2.045.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Motor DC, PSO, EKF, Fungsi objektif, DC Motor, Objective Function
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2681.B47 Electric motors, Direct current.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Kiet Pascal Asmara
Date Deposited:	26 Sep 2023 02:39
Last Modified:	26 Sep 2023 02:39
URI:	http://repository.its.ac.id/id/eprint/104110

Actions (login required)

View Item