Adaptive Linear Quadratic Gaussian Speed Control of Induction Motor Using Fuzzy Logic

Abstract

An induction motor's speed can be managed in a variety of ways using a Variable Frequency Drive (VFD). In this study, the speed control of an induction motor will be controlled by applying Indirect Field Oriented Control (IFOC) combined with Linear Quadratic Gaussian (LQG). Conventional LQG control is a linear controller; therefore, if the system's dynamic is high and over the linear boundary, the LQG performance will not be optimal. Therefore, Adaptive LQG (ALQG) is proposed. Fuzzy logic is used as an adaptive algorithm with low complexity and ease of implementation. The significance of this study lies in its endeavor to tackle the challenges associated with nonlinearities and high dynamics in induction motor control. The average performance of speed variation and load variation tests proves that ALQG is superior in terms of settling time and undershooting than PID and LQG. PID has the highest overshoot with the smallest Integral Absolute Error (IAE). In comparison, ALQG is superior to conventional LQG in terms of IAE with 3.59% lower.

GWO-PID of Two-phase Hybrid Stepping Motor for Robotic Grinding Force

“…Electric motors need control units to improve their performance. Among the commonly used control units that have proven their efficiency with linear systems is the traditional controller [11]- [15]. While nonlinear systems need expert units such as neural networks and fuzzy logic or smart ones such as the optimal advance of a genetic algorithm or types.…”