Current control designed with model based predictive control for six-phase motor drives. (March 2020) - Ayala, Magno; Doval-Gandoy, Jesus; Rodas, Jorge; Gonzalez, Osvaldo; Gregor, Raul
Abstract: Finite control set model-based predictive control techniques are distinguished by being an interesting alternative to traditional field oriented control techniques for multiphase drives due to their fast dynamic response and flexibility in the introduction of constraints. However, those predictive control techniques have some drawbacks regulating the ( x − y ) current components which can cause machine losses as well as a high computational burden. This paper presents a comparative study of an enhanced predictive current control technique with a conventional predictive control technique and two hybrid predictive control techniques applied to an asymmetrical six-phase induction motor drive in terms of current tracking, total harmonic distortion of stator currents and computational burden. Experimental results are reported to demonstrate the benefits of the different current control techniques by using the mean squared error and total harmonic distortion of stator currents as quality figures of merit and the number of floating point operations to measure the computational burden of each predictive control, thus concluding the advantages and limitations of each technique at transient and steady regimes. Highlights: Model predictive control with second horizon prediction and restrained vectors. Kalman filter used to estimate the rotor currents. Multiphase machine advantages such as major power splitting and lower torque ripple.