| Abstract |
A very simple control approach using neural network for the robust position control of a Permanent
Magnet Synchronous Motor (PMSM) is presented. The linear quadratic controller plus feedforward
neural network is employed to obtain the robust PMSM system approximately linearized using fieldorientation
method for an AC servo. The neural network is trained in on-line phases and a feedforward
recall and error back-propagation training compose this neural network. Since the total numbers of
nodes are only eight, this system easily is realized by the general microprocessor. During the normal
operation, the input-output response is sampled and the weighting value is trained multi-times by error
back-propagation method at each sample period to accommodate the possible variations in the
parameters or load torque. And the state space analysis is performed to obtain the state feedback gains
systematically. In addition, the robustness is also obtained without affecting overall system response.
A floating-point Digital Signal Processor DS1102 Board (TMS320C31) realizes this method. This
board provides a free 6.2'' length Industry Standard Architecture (ISA) slot with 16-bits connector. The
basic DSP software is used to write C-program, which is compiled by using ANSI-C style function
prototypes. |
