Continuous‐time model predictive control of a permanent magnet synchronous motor drive with disturbance decoupling. Issue 5 (1st May 2017)

• Record Type: Journal Article
• Authors: Errouissi, Rachid; Al‐Durra, Ahmed; Muyeen, S.M.; Leng, Siyu
• Is Part Of: IET electric power applications. Volume 11:Issue 5(2017)
• Page Start: 697
• Page End: 706
• Publication Date: 2017-05-01
• Subjects: predictive control -- permanent magnet motors -- synchronous motor drives -- torque -- machine control; continuous‐time model predictive control -- permanent magnet synchronous motor drive -- disturbance decoupling -- CTMPC -- PMSM drive -- Taylor series expansion -- non‐linear model -- model uncertainties -- load torque -- predicted speed tracking error -- cascaded control scheme -- nonlinear disturbance observer -- parameter variation; Electric power -- Periodicals; Electric power systems -- Periodicals; 621.305
• DOI: 10.1049/iet-epa.2016.0499
• Languages: English
• ISSNs: 1751-8660

Abstract

Abstract : The design and the experimental validation of a continuous‐time model predictive control (CTMPC) for a permanent magnet synchronous motor (PMSM) drive with disturbance decoupling is discussed. The CTMPC approach uses Taylor series expansion to derive a closed‐form solution to the problem of model predictive control even though the system behaviour is described by a non‐linear model. This type of controller requires an exact knowledge of the system model to guarantee an accurate prediction of the system behaviour, while the PMSM is usually subjected to model uncertainties and external disturbances such as the load torque. Moreover, in the proposed approach, the predicted speed tracking error is directly used to determine the required voltage command without the need for a cascaded control scheme. As a result, the load torque is seen as unmatched disturbance which makes exact disturbance decoupling more challenging. To overcome such a problem, a non‐linear disturbance observer is designed and combined with the CTMPC method to enhance the prediction accuracy under parameter variation and unknown load torque. The feasibility of the proposed approach is experimentally investigated, and good transient and steady‐state performances are obtained.