Hardware/Software Implementation of Fuzzy-Neural-Network Self-Learning Control Methods for Brushless DC Motor Drives

Abstract

This paper presents a study of different fuzzy neural network (FNN) learning control methods for brushless dc (BLDC) motor drives. The FNN combines fuzzy logic (FL) with the learning capabilities of an artificial neural network. The study designs an FNN structure and defines four different training algorithms for the FNN, namely, backpropagation (BP), extended Kalman filter (EKF), genetic (GEN), and particle swarm optimization (PSO). These algorithms are examined in the simple application of pattern matching an input set to an output set and determine the strengths and weaknesses of each algorithm. Tests of each learning algorithm by a pattern matching benchmark are achieved via dSPACE DSP MATLAB/Simulink environment and allows for the capability for adaptive self-tuning of the weights and memberships of the input parameters. Thus, this adds a self-learning capability to the initial fuzzy design for operational adaptively and implements the solution on real hardware using a BLDC motor drive system. The success of the adaptive FNN-controlled BLDC motor drive system is verified by experimental results. Testing results show that the EKF method is the superior method of the four for this specific application. The BP method was also somewhat successful, nearly matching the pattern but not to the accuracy of the EKF. The GEN and PSO methods did not demonstrate success. Demonstrating the proposed self-learning FNN control on real hardware realizes the solution.