Adaptive reference model predictive control with improved performance for voltage-source inverters
File(s)07864322.pdf (2.06 MB)
Accepted version
Author(s)
Yang, Y
Tan, SC
Hui, SYR
Type
Journal Article
Abstract
Power converters under the model predictive control (MPC) inherently suffer from nonignorable steady-state residuals in its control outputs when it exists a mismatch in the parameters between the actual system in control and the system's model adopted in the control. In this brief, an adaptive reference MPC (ARMPC) is proposed in response to this issue. Unlike those conventional derivatives of MPC, the ARMPC is designed to track the so-called virtual references instead of the actual references. The virtual references are generated by a flexibly modeled virtual multiple input multiple output system. Consequently, additional tuning is not required for different operating conditions. ARMPC has been applied to a single-phase full-bridge voltage-source inverter with both resistive and resistive-inductive loads. It is experimentally verified that the proposed ARMPC can significantly attenuate the steady-state offsets in the environment of model mismatch (which is an inherent problem of MPC without significantly sacrifice transient performance). Also, a demonstration that ARMPC renders a consistent attenuation of steady-state errors than the conventional MPC with integrator is provided. More importantly, ARMPC shows better transient performance than the MPC with integrator for some cases.
Date Issued
2018-03-01
Date Acceptance
2017-02-11
Citation
IEEE Transactions on Control Systems Technology, 2018, 26 (2), pp.724-731
ISSN
1063-6536
Start Page
724
End Page
731
Journal / Book Title
IEEE Transactions on Control Systems Technology
Volume
26
Issue
2
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects
0906 Electrical And Electronic Engineering
0102 Applied Mathematics
Industrial Engineering & Automation
Publication Status
Published
Date Publish Online
2017-02-24