Neuro-vector-based electrical machine driver combining a neural plant identifier and a conventional vector controller
Kurosh Madani, Gilles Mercier, Mohammad Dinarvand, Jean-Charles Depecker · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
One of the most important problems, for a machine control process is the system identification. To identify varying parameters which are dependent from other system's parameters (speed, voltage and currents, etc.), one must have an adaptive control system. Synchronous machines conventional vector control's implementation using PID controllers have been recently proposed presenting the best actual solution. It supposes an appropriated model of the plant. But real plant's parameters vary and the P.I.D. controller is not suitable because of the parameters variation and non-linearity introduced by the machine's physical structure. In this paper, we present an on-line dynamic adaptive neural based vector control system identifying the motor's parameters of a synchronous machine. We present and discuss a DSP based real- time implementation of our adaptive neuro-controller. Simulation and experimental results validating our approach have been reported.