Extended Approach of ANFIS in Cascade Control

Abstract

Abstract-this paper discusses extended approaches of Adaptive Network Based Fuzzy Inference System (ANFIS) using Self Tuning Regulator (STR) via Fuzzy logic to be implemented in cascade control. Plant used is Pressure RIG 38-714 which supports cascade configuration. The controlled variable in the outer loop is pressure, in the inner loop's flow. The proposed method's used to improve the performance of ANFIS which has been commonly implemented to accomplish control task. Comparison has been conducted between ANFIS using STR and without STR, from experiment could be concluded that controller ANFIS using STR acquired better performance than only used ANFIS. It's derived from the transient response of those. For ANFIS using STR is obtained rise time and settling time are 9 ms and 12 ms respectively. In the other hand, ANFIS without STR resulted 21 ms and 29 ms for rise time and settling time respectively.

Perancangan Sistem Kendali Cascade pada Deaerator Berbasis Adaptive Neuro Fuzzy Inference System (ANFIS)

“…Keuntungan pada arsitektur ini adalah ketika terdeteksi gangguan pada siklus dalam, maka akan segera terdeteksi dan dilakukan penyesuaian. Dikarenakan proses pada siklus dalam lebih cepat dari variabel utama, maka struktur ini juga mampu mencegah terganggunya stabilitas pada proses yang berjalan [3]. Penggunaan ANFIS pada penelitian ini bertujuan untuk memberikan pengendalian yang lebih baik dan mampu beradaptasi harapan dapat menghasilkan sebuah sistem pengendalian yang memiliki waktu kestabilan lebih baik dan kemampuan beradaptasi dengan menggunakan pengendalian modern [4,5].…”

“…Pengendali ini umumnya digunakan untuk mendapatkan respon yang lebih baik dibandingkan dengan pengendali satu siklus. Pengendali cascade memiliki banyak keuntungan dibandingkan pengendali konvensional, khususnya untuk menghilangkan gangguan [3]. Pada tahap selanjutnya dilakukan penentuan MF yang digunakan sebagai bentuk aplikasi TSK if -then rules yang digunakan pada penelitian ini.…”

ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

“…In the area of control engineering, the fuzzy logic controller with many varieties and structures has been practically developed in the many areas such as control, forecasting, identification, and etc. The strongest thing of fuzzy logic"s can adopt intuition of human feeling mapped in the membership function of fuzzy logic [17].…”

Adaptive Speed Control System Based on Interval Type-2 Fuzzy Logic

“…The problem can be alleviated through a Proportional Integral (PI) controller, which is simple to implement and common in BLDC motor control. Robust PID/PI controllers for minimum overshoot response of BLDC drives have been introduced into various applications [13][14][15]. This paper presents the design of Interval Fuzzy Type-2 (IT2FLC).…”