TLS EXIN based neural sensorless control of a high dynamic PMSM

Sensorless vector control of the Permanent Magnet Synchronous Motors (PMSMs) has been a very challenging subject for many years. In general, the absence of the encoder in the drive permits to obtain high dynamical performance by exploiting increased reliability and also reduced cost. Among the different methodologies proposed in the literature, a model-based approach has been proposed here. In particular, the space-vector equations of the PMSM have been re-elaborated in a matrix form to permit the use of a Least Squares technique for the estimation of the speed of the PMSM. The problem has been then faced-up with the so-called TLS EXIN neuron, which is the only linear neural network able to solve the TLS problem on-line in a recursive form. Experimental tests have been performed on an experimental test set-up based on a fractional horsepower permanent magnet machine.