Abstract
This paper presents an adaptive speed controller based on artificial intelligent technique to improvethe performance of classical Direct Torque Control (DTC) for Permanent Magnet Synchronous Motor (PMSM) drives. The proposed method applies back propagation (BP) based neural network (NN) to tune the parameters of classical proportional-integral (PI) speed controller. Comparisons between conventional PI speed controller and proposed method are carried out by Simulation.Simulation results demonstrate that conventional DTC system based on the proposed NN speed controller can achieve higher performance with fast speed response, small overshoot and robustness.
Highlights
Permanent magnet synchronous motors (PMSM) have become more popular in high-performance industrial drives
Neural network, which is capable of approaching to any nonlinear systems with uncertainties, is applied to tune PI parameters [13].Effective direct torque control (DTC) control for PMSM drives can be obtained by optimizing PI controller parameters using neural network (NN) with nonlinear self-adaptive ability
This paper proposes an adaptive speed controller based on back propagation (BP) neural network, which is verified by simulation with higher performances
Summary
Permanent magnet synchronous motors (PMSM) have become more popular in high-performance industrial drives. In [10], artificial neural network (ANN) based on Kalman filter is used as speed controller It can get better simulation results than conventional PI speed controller, it is relatively more complex by combining ANN and Kalman filter. Neural network, which is capable of approaching to any nonlinear systems with uncertainties, is applied to tune PI parameters [13].Effective DTC control for PMSM drives can be obtained by optimizing PI controller parameters using NN with nonlinear self-adaptive ability.
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