Abstract
A new Fourier Series Learning Controller (FSLC) for velocity control on a Permanent Magnet Synchronous Motor (PMSM) is proposed and implemented. An analysis of the error convergence for the FSLC is presented, and the update law for the Fourier series coefficients is specified. The field-oriented control method is used as a basic element to implement three different controllers for a PMSM. The performance of the FSLC is compared with two control methods, a classical PI (Proportional Integral) controller and an artificial neural network controller. The periodic nature of torque ripple in PMSMs is considered as a periodic disturbance, which must be compensated by the controller. With the FSLC implementation, a substantial reduction of the velocity ripple is obtained. Furthermore, a higher speed of learning is achieved with the FSLC in comparison with the artificial neural network.
Highlights
The permanent magnet synchronous motors market is growing due to their competitive cost, efficiency and reliability
The new controller was used for the velocity control on a Permanent Magnet Synchronous Motor (PMSM), which reduces the torque ripple by 70% with respect to the results obtained with a PI as the velocity controller
The performance of the Fourier series learning controller was compared with the performance of other two controllers, the conventional PI controller and an artificial neural network
Summary
The permanent magnet synchronous motors market is growing due to their competitive cost, efficiency and reliability. The use of a frequency-domain iterative learning scheme for periodic quadrocopter flight, parameterizing the non-causal tracking error compensation as Fourier series, was presented by [21] Uncertainties, such as measurement noise, inaccuracies of the approximate transfer function or model uncertainty, can be accounted for in the learning step size, and the optimal step size for each frequency component can be computed from the statistical properties thereof [21]. The problem of modeling and tracking control for a high speed ball screw drive, which includes time-varying parametric uncertainties and disturbances, can be solved when a function approximation technique based on Fourier series expansion is applied to estimate the total uncertainties containing inertia uncertainties, stiffness uncertainties and external disturbances [27]. It is verified for periodic disturbances, such as torque ripple in PMSMs, that Fourier series can eliminate these disturbances by providing the correct harmonics with a suitable amplitude
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