Implementation of Brushless DC Motor Control Using Compound Compensation Fuzzy Neural Network and PID

In this paper, we present a novel control methodology that integrates compensation fuzzy neural networks with PID control to achieve advanced brushless DC motor regulation. This hybrid approach leverages the learning capabilities of fuzzy neural networks combined with the stability advantages of conventional PID control, resulting in enhanced overall system performance. The implementation involves designing a fuzzy neural network compensation module that dynamically adjusts PID parameters based on real-time system responses, typically implemented through gradient descent learning algorithms and membership function optimization. Through this compound control strategy applied to brushless DC motors, we achieve precise motion control while ensuring system stability and optimal performance across diverse operating conditions. Key implementation aspects include neural network training with backpropagation, PID gain scheduling, and real-time parameter adaptation using error feedback mechanisms.