Study on Application of AI Controllers for Speed Control of Motors

Building high-performance motor drives is vital for industrial uses. A high-performance motor drive system must be quick in terms of dynamic speed order monitoring and load regulation. The motor controllers can provide system protection by regulating or limiting torque, protecting against overloads, and safeguarding against mistakes. Many motor controllers include logic for managing applications as well as additional features like data recording and data collecting. The purpose is to study various tuning techniques for motor speed controllers using Artificial intelligence. The controllers such as proportional-integral (PI) controller and proportional integral derivative (PID) controller have been considered here. Most frequently used Artificial intelligence (AI) methods such as Artificial Neural Networks (ANN), Fuzzy logic controller (FLC), Genetic Algorithm (GA), Bat Algorithm, Adaptive Tabu Search (ATS), Ant Colony Optimization (ACO), Ziegler and Nichols (ZN) Algorithm are considered by their decision-making capability. In the proposed work a rigorous literature review is done for analyzing the performance of AI-based controllers. This will help other researchers to understand various aspects of the said controllers, particularly the technology involved. In-depth review and application of AI controllers are highlighted using various charts and graphs for easy understanding. Keywords: AI motor controllers, Fuzzy Logic, PID controller, PI controller, Genetic algorithm, PSO algorithm, Ziegler and Nichols algorithm, Bat algorithm, Adaptive Tabu search algorithm