Kendali PI-D dan Kontrol Fuzzy pada Plant Level Air Terdistribusi

Abstract

ABSTRAKPenelitian ini bertujuan menginvestigasi algoritma kendali pada plant level air yang terdistribusi pada dua tangki. Terdapat dua metode yang digunakan yakni kendali PI-D yang ditempatkan pada masing-masing unit pengendali lokal (LCU) dan kendali Fuzzy sebagai unit pengendali utama (MCU) dimana performa sistem dapat di-monitoring pada Human Machine Interface (HMI). Kendali PI-D (tipe B) dipilih untuk mengantisipasi persoalan setpoint kick yang sering muncul ketika digunakan kendali PID konvensional. Hasil penelitian menunjukkan respons kendali PI-D dapat mengatasi setpoint kick ditandai dengan perubahan smooth ketika setpoint berubah. Respons kendali PI-D pada HMI menunjukkan hasil yang baik direpresentasikan melalui error steady state dapat bernilai 0, overshoot 0% dan settling time 0,01 detik. Disamping itu, kontrol Fuzzy yang digunakan pada MCU dapat menghasilkan nilai setpoint (SV) yang tepat untuk masing-masing LCU, sehingga diperoleh respons kendali di atas.Kata kunci: LCU, MCU, PI-D, Level Air, Logika Fuzzy ABSTRACTThis study aims to investigate the control algorithm on the plant water level distributed in two tanks. There are two methods used, namely PI-D control placed on each local control units (LCU) and Fuzzy control as the Main Control Unit (MCU) and synchronization for Human Machine Interface (HMI) and monitoring. The PID control (type-B) was chosen to anticipate the setpoint kick problem that often arises when conventional PID controls are used. The results showed that the PI-D control response was able to overcome the setpoint kick which represented by smooth acting of actuator response . Response of the PI-D control on the HMI shows a good response by a steady state error of 0, an overshoot of 0%, and a settling time of 0.01 seconds. Besides that, Fuzzy logic used in the MCU can produce the right setpoint (SV) value for each LCU, so that the control response above is obtained.Keywords: LCU, MCU, PI-D, Water Level, Fuzzy Logic