Fault tolerant speed regulation of Induction Motor using Artifical Neural Network

Abstract

This paper successfully develops and investigates the implementation of Artificial Neural Network (ANN) based robust speed control strategy for Indirect Vector Control (IVC) three phase Induction Motor (1M) drive. The 1M is modeled in terms of dq in s y nchronousl y rotating reference frame for IVC in Matlab/Simulink. The main purpose of the proposed design is to accomplish robustness for load disturbances, speed variation, parameter uncertainties, and electrical faults. The performance of aforesaid control technique is compared with that of conventional tuned PI control scheme. Simulation results of the ANN guarantee effectiveness and robustness regarding overshoot, undershoot, rise time, fall time and chattering for different operating condition in comparison to traditional PI control scheme.

A Neural Network-Based Model Reference Control Architecture for Oscillation Damping in Interconnected Power System

“…Hence, it becomes more intelligent in predicting the uncertainties. Due to the learning capability of NN, it has a wide range of applications in automation and optimization [20], image processing [21,22], speech recognition [23,24], control [25,26], modelling, and time series prediction [27,28].…”

“…Equation (24) and the control structure , and of Equation (25) is used to design a linear controller model to track the reference point. The polynomial B is neglected to make Equation (26) equal to the characteristic equation of the system. Hence, B is replaced with another term, i.e., ( ) = ( ).…”

“…Hence, B is replaced with another term, i.e., ( ) = ( ). Equation (26) will transform into the following equation: In Equation (19), y p (t) and u(t) represents the deviation of process from its operating points. The parameters a i and b i are dependent on the operating condition, while q −1 represents the backward shift operator.…”