Composite Adaptive Inverse Controller Design for Permanent Magnet Synchronous Motor

Permanent magnet synchronous motor (PMSM) servo system is a nonlinear, multi-variables strong coupling system. To improve the performance of the PMSM system, a composite adaptive inverse control strategy is proposed. This control strategy adopt improved radial basis function (RBF) neural network and FIR filter as nonlinear filter. The proposed filter is used to identify the system, inverse system and design the adaptive inverse controller. Meanwhile the chaos multi-population particle swarm optimization (CMPSO) algorithm is proposed to training the parameters of the nonlinear filter offline. And then an improved variable step size LMS (IVSLMS) algorithm is used to optimize the parameters online. These algorithm improves the convergence speed and accuracy, further improves the control performance of adaptive inverse control. The results of simulation and experiment indicate that the PMSM servo system has good dynamic, static performance and robustness by using proposed hybrid adaptive inverse control strategy.

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W celu poprawy parametrów silnika synchronicznego z magnesami trwałymi PMSM zaproponowano kompozytowa adaptacyjną strategię sterowania. Strategia wykorzystuje sieć neuronową i nieliniowy filtr SOI.