Identify PMSM´s Parameters by Single-Layer Neural Networks with Gradient Descent

In order to identify parameters of permanent magnet synchronous motor(PMSM) on-line, single-layer neural networks (SLNN) with gradient descent is proposed. SLNN can study and adapt itself by change its weigh values while PMSM is running. The output of PMSM´s status variants is a function about the estimated parameters, including stator resistance, d-q axial inductance, rotor flux and moment of inertia, which are included in SLNN´s weight vector, so the estimated parameters can be iterated in SLNN after computing their gradients. Changing the learning rate of SLNN makes it available to choose the emphasis on estimated accuracy or on convergence rate. The servo PI parameters are adjusted according to the identified values. The experimental results and simulations have illustrated its simplicity, validity and efficiency.