A hybrid method for online rotor parameters estimation

The precise knowledge of motor parameters is very important for a vector control system. However, motor parameters, especially rotor parameters, are varying during operation. So the rotor parameters need to be online identified and the controller gains should be modified in real-time. Based on the combination of least square (LS) method and non-dominated sorting genetic algorithms 2 (NSGA2), this paper proposes a hybrid method for online rotor parameters identification. The NSGA2 is applied for initial estimation, while LS is used after certain iterative steps, so that the hybrid method is able to take the advantages of both algorithms. The contrast tests show that the new approach converges faster than LS and performs better than NSGA2 in accuracy after longer iteration. The simulation results show that the vector control system, whose controllers are modified with the results of this method, can achieve a good performance of dynamic and steady state responses.