Fuzzy neural network PI/PD-like controller using extended Kalman filter for motion controls industry

The study of control systems and associated research advances the state of knowledge for electrical engineers. This study proposes an online trained Fuzzy Neural Network PI/PD controller for speed trajectory tracking of a brushless drive system. The Fuzzy Neural Network (FNN) structure is composed of two parallel fuzzy-neural PI/PD-like fuzzy controllers. Each of the fuzzy-neural PI/PD controllers is a four layer control network. Extended Kalman Filter (EKF) adaptively trains each FNN parameters set online. The online learning mechanism modifies the weights and the membership functions of the parallel FNN PI/PD-like fuzzy controllers to adaptively control the rotor speed of the drive system. Thus, the proposed architecture-based EKF presents an alternative to control schemes employed so far. The entire system is designed and implemented in the laboratory using a hardware setup. The real-time laboratory implementation is based on a dSPACE DS1104 DSP and MATLAB/Simulink environment. Experimental results have shown that the proposed controller adaptively and robustly responds to a wide range of operating conditions. Comparison results demonstrate performance improvement of the FNN PI/PD-like fuzzy controller in comparison to a traditional PID control system using the same hardware and testing scheme.