DTC with Duty Ratio Control Using Fuzzy Logic

In this document, we present a Direct Torque Control (DTC) approach using duty ratio control governed by fuzzy logic. This method involves several sophisticated technical concepts that warrant detailed examination for comprehensive understanding. First, let's briefly introduce the fundamentals and applications of fuzzy logic. Fuzzy logic serves as a mathematical framework capable of handling uncertain information, extensively applied in control systems, artificial intelligence, and other domains. In duty ratio control applications, fuzzy logic enables superior regulation of motor speed and torque, thereby enhancing overall performance and efficiency. Within the DTC methodology, fuzzy logic determines optimal duty cycles to achieve optimal motor operation. The implementation typically involves: - Defining fuzzy sets for torque error and flux error as input variables - Establishing output membership functions for duty ratio adjustment - Creating rule bases that map input conditions to appropriate control actions - Implementing defuzzification techniques like centroid method to generate precise duty ratios Key algorithmic components include: 1. Error calculation between reference and actual torque/flux values 2. Fuzzification process converting crisp inputs to fuzzy sets 3. Inference engine applying IF-THEN rules from the knowledge base 4. Real-time duty cycle optimization through defuzzification Understanding fuzzy logic principles and their practical implementation is crucial for mastering this DTC approach, particularly for developing efficient motor control systems with reduced torque ripple and improved dynamic response. Code implementation would typically involve designing membership functions, rule evaluation algorithms, and PWM signal generation routines synchronized with motor electrical parameters.