EKF-Based PI-/PD-Like Fuzzy-Neural-Network Controller for Brushless Drives

This paper presents the development of a fuzzy-neural-network (FNN) proportional–integral (PI)-/proportional–derivative (PD)-like controller with online learning for speed trajectory tracking of a brushless drive system. The design implements the novel use of the extended Kalman filter (EKF) to train FNN structures as part of the PI-/PD-like fuzzy design. The FNN structure has two parallel FNN PI-/PD-like controllers, each with four internal layers. EKF trains each FNN by modifying the weights and the membership function parameters. Thus, the proposed EKF-based architecture presents an alternative to control schemes employed so far. The objective is to replace the conventional PI–derivative (PID) controller with the proposed FNN PI-/PD-like controller with EKF learning mechanism. Comparisons of the algorithm performances provide evidence of improvement of the FNN PI-/PD-like controller over PID control. A test bench enables design implementation in the laboratory on hardware using a dSPACE DS1104 DSP and MATLAB/Simulink environment. Experimental testing results show that the proposed controller learns and robustly responds to a wide range of operating conditions in real time.