IMPROVEMENT OF BLDC MOTOR PERFORMANCE THROUGH INTELLIGENT CONTROLLERS

Abstract

This dissertation deals with, “Improvement of BLDC motor performance through intelligent controllers”. Implementation of different control strategies for Permanent Magnet Brushless DC Motor in different modes of operation is carried out through MATLAB/simulation. The controlled electric motors play a vital role in the industrial automation. It is well known that electrical motors consume a significant percentage of electrical energy and even small improvement in operating efficiency could result in large reduction in consumption of energy. Therefore new techniques are required to extract ultimate performance from these drives. There has also been tremendous research for providing suitable speed controller for PMBLDC motor. Many control strategies have been proposed in literature. The main drawback of fixed gain controllers is that their performance deteriorates as a result of changes in motor parameters & its operating conditions. In recent times hybrid controllers such as Fuzzy Logic and Adaptive neuro-fuzzy (ANFIS) have emerged as one of the most attractive non-linear controller for application in the industrial processes giving robust performance in the face of parameter variation and load disturbance effects. The main objective is to compensate for overshoots and oscillation in the response of the PMBLDC motor for wide speed range of opearation. The performance is defined in terms of accuracy, smooth operation and simplicity. The controller performance is defined in terms of rise time, Settling time, overshoot, undershoot and behavior with non-linarites. In this thesis, the PMBLDCM drive is modeled and simulated in MATLAB/SIMULINK environment. The controller such as Proportional Integral controller, Fuzzy logic controller, Adaptive- Neuro controller (ANFIS), series hybrid controller(known as Fuzzy precompensated PI controller) and self-tuning PI controller(Fuzzy tuned PI controller) are implemented for speed controller in the MATLAB/SIMULINK environment and drive performance using these controllers is observed and compared. The performance comparison is done in terms of several performance measures such as settling time, peak time, rise time, overshoot, undershoot, and load variations and stable performance under all operating conditions. iv Every controller has their own merits and demerits. It is observed that PI controller would be a good choice for simplicity and ease of application. PI controllers are observed to have no steady state error but are slow in response. The Fuzzy logic controller offer good performance in the presence of nonlinearity but Fuzzy logic controller has offset at steady state. Adaptive neuro fuzzy controller provides excellent transient response in terms of quickness of the response. Series hybrid and self- tuning PI controller offer good response in different operating conditions but main drawback is that, processing time of controllers are high.