An Intelligent Decision Support System for Design of Brushless Direct Current Motors

Brushless DC (BLDC) motors are among the most widely used electrical motors. Design of a BLDC motor is the most fundamental problem when dealing with the BLDC motors. This thesis presents an intelligent decision support system that can be used to design BLDC motors. A hybrid approach, that includes an object oriented paradigm using frames and procedural attachments together with a rule based mechanism, is used to build the knowledge base of the proposed architecture. The design strategy is implemented using a rule-based successive iterative method. An evolutionary fuzzy system was used to derive the modification rules of the system. The antecedent and consequent of each fuzzy modification rule was encoded as the individual of an evolutionary system. The evolutionary system evolves the set of modification rules to find a set of optimized rules. The proposed system developed design which had superior efficiency, weight and motor constant compared to design developed using the conventional design method.