Intelligent Online Auto-Tuning Technique for IPMSM Servo Drive

A real-time moment of inertia identification technique using wavelet fuzzy neural network (WFNN) for an interior permanent magnet synchronous motor (IPMSM) servo drive is proposed in this study. The estimated moment of inertia will be used in the online design of an integral-proportional (IP) speed controller to achieve the gains auto-tuning of the IPMSM servo drive. In this study, first, the dynamic analysis of a field-oriented control (FOC) IPMSM servo drive system with an IP speed controller is studied. Then, a heuristic approach using the WFNN is proposed for the real-time identification of the moment of inertia of the IPMSM servo drive system. Moreover, the network structure and the convergence analysis of the WFNN are introduced. Furthermore, an IPMSM servo drive based on a high performance digital signal processor (DSP) is developed. Finally, from the experimental results, the gains of the IP speed controller can be effectively tuned online at different operating conditions.