Optimal Finite Control Set Model Predictive Control Strategies for Induction Motor Drives
type: CreativeWork
description: The finite control set model predictive control (FCS-MPC) for motor drives has been vigorously investigated during the past decade for control of current, torque, stator flux linkage, and other performances in a motor drive system. Model predictive torque control (MPTC) and flux control (MPFC) are two popular categories of FCS-MPC for motor drives. In FCS-MPTC, a finite number of possible voltage vectors are evaluated by a cost function in an iterative prediction loop. The cost function includes several control objectives, such as minimization of torque and flux errors, the neutral-point voltage of multi-level inverters and inverter switching frequency. The control algorithm determines the optimal voltage vector that minimizes the pre-defined cost function. Each variable included in the cost function has a weighting factor according to different magnitudes and units. Weighting factor tuning is a non-trivial and complicated task, particularly when the control algorithm has two control-objectives which are prime variables i.e., torque and flux. In such a case, the control algorithm may choose a global optimal solution for torque which will be a sub-optimal solution for flux. In general, a conventional FCS-MPTC algorithm has a large computational burden due to a large number of available voltage vectors as in multi-level inverters or discrete SVM MPC technique and B) the presence of weighting factors in the cost function with several control objectives. This thesis developed a two-stage optimization-based FCS-MPC algorithm for an induction motor drive that uses reduced voltage control sets (RVCS) to evaluate the pre-defined cost function in the prediction loop. The proposed algorithm lowers the computational burden of the controller in two cascaded stages for a three-level neutral-point clamped voltage source inverter (3L-NPC VSI) fed IM drive. The voltage vector selection in the first stage of the proposed two-stage algorithm is executed using two different approaches…
abstract: The finite control set model predictive control (FCS-MPC) for motor drives has been vigorously investigated during the past decade for control of current, torque, stator flux linkage, and other performances in a motor drive system. Model predictive torque control (MPTC) and flux control (MPFC) are two popular categories of FCS-MPC for motor drives. In FCS-MPTC, a finite number of possible voltage vectors are evaluated by a cost function in an iterative prediction loop. The cost function includes several control objectives, such as minimization of torque and flux errors, the neutral-point voltage of multi-level inverters and inverter switching frequency. The control algorithm determines the optimal voltage vector that minimizes the pre-defined cost function. Each variable included in the cost function has a weighting factor according to different magnitudes and units. Weighting factor tuning is a non-trivial and complicated task, particularly when the control algorithm has two control-objectives which are prime variables i.e., torque and flux. In such a case, the control algorithm may choose a global optimal solution for torque which will be a sub-optimal solution for flux. In general, a conventional FCS-MPTC algorithm has a large computational burden due to a large number of available voltage vectors as in multi-level inverters or discrete SVM MPC technique and B) the presence of weighting factors in the cost function with several control objectives. This thesis developed a two-stage optimization-based FCS-MPC algorithm for an induction motor drive that uses reduced voltage control sets (RVCS) to evaluate the pre-defined cost function in the prediction loop. The proposed algorithm lowers the computational burden of the controller in two cascaded stages for a three-level neutral-point clamped voltage source inverter (3L-NPC VSI) fed IM drive. The voltage vector selection in the first stage of the proposed two-stage algorithm is executed using two different approaches…
author: Osman, Ilham
language: EN
keywords: Cost function, Model predictive control, Induction motor drive, Inverters, Optimization, Computational burden, Sub-optimal, Flux control, Nonlinear control
license: https://purl.org/coar/access_right/c_abf2
license: https://creativecommons.org/licenses/by-nc-nd/3.0/au/
date published: 2020
date modified: 2026-07-24T05:33:06Z
provider: University of New South Wales
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: UNSW, Sydney

PropertyValue
property ID: Handle
value: http://hdl.handle.net/1959.4/67166

PropertyValue
property ID: DOI
value: 10.26190/unsworks/21884