The fuzzy neural network control with adaptive algorithm for a PM synchronous motor drive

In this study a fuzzy neural network (FNN) control system with adaptive algorithm is proposed to control permanent magnet synchronous motor (PMSM) drive system. First, the DSP field-oriented mechanism is applied to formulate the dynamic equation of the PMSM servo drive. Then, the adaptive FNN control system is proposed to control the rotor of the PMSM servo drive for the tracking of periodic reference inputs. In the adaptive FNN control system, the FNN controller is used to mimic an optimal control law, and the compensated controller with adaptive algorithm is proposed to compensate the difference between the optimal control law and the FNN controller. Moreover, an on-line parameter training methodology, which is derived using the Lyapunov stability theorem and the backpropagation method, is proposed to increase the learning capability of the FNN. The effectiveness of the proposed control schemes is verified by experimental results.