Fuzzy Gain Scheduling of PID (FGS-PID) for Speed Control Three Phase Induction Motor Based on Indirect Field Oriented Control (IFOC)

Abstract

This paper propose about using PID control system based on Kp, Ki, and Kd parameter determination with scheduling process from fuzzy logic. Control system is used to arrange speed of three phase induction motor using IFOC method. This method can be minimized the main problem from speed control of induction motor which is a transient condition. The robustness validation from this system use testing process of dynamic speed which is compared with the other control system to know the system performance in transient condition such as (rise time, overshoot, undershoot and settling time). The result shows using the proposed system has better performance responses which is requiring 0.001 seconds time in transient condition up to steady state condition without overshoot and undershoot problem.

Penala Parameter Pid Otomatis Pada Pengatur Kecepatan Motor Induksi Tiga Fasa

“…kontroler PID bertugas mengoptimalkan peran kontrol vektor. Salah satu kelemahan dari kontroler PID adalah sulitnya menangani sistem nonlinear, karena performanya akan semakin menurun sementara motor induksi merupakan sebuah sistem nonlinear [7]. Kelemahan tersebut dapat diatasi dengan menala ulang (tune up) setiap ada perubahan kondisi sehingga performa pengatur kecepatan motor induksi tiga fasa berbasis kontrol vektor tetap terjaga optimal.…”

“…Sehingga jika dipandang dari sumbu yang berputar ini, vektor akan menjadi arus DC (idq) pada sumbu direct dan quadrature. Langkah ini menggunakan transformasi Park melalui (6) dan (7).…”

Position Control of 1-DOF High-Precision Rotary Table using Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller

“…In this research, the membership functions used were triangles shape. Each input is mapped according to the classification chosen with linguistic variables (near, medium, far) that were used to describe each input condition [24] . The membership sets of error (e) and derivative error (de) were shown in Figure 2.…”

Applying a neural tuner of the PI­controller parameters to control gas heating furnaces

“…Such a possibility is provided by intelligent adaptive control methods, which make it possible, to a certain degree, to simulate human behavior, in particular, expert systems (ES) [8], fuzzy logic [9,10], neural networks [11][12][13]. One of the disadvantages of expert systems methods [8] is the absence of possibility of rapid learning, due to the high complexity …”

“…Among the studies that investigate a fuzzy logic apparatus in order to configure settings of the controller, we can highlight those that address configuration of parameters of the controller [9,10]. The main drawback of the proposed solution is the implementation of method with specific values of coefficients of normalization for the inputs and outputs of the tuner.…”