Paper Info
Title
Fast Model Predictive Control Based On Linear Input/Output Models And Bounded-Variable Least Squares
Abstract
This paper introduces a fast and simple model predictive control (MPC) approach for multivariable discrete-time linear systems described by input/output models subject to bound constraints on inputs and outputs. The proposed method employs a relaxation of the dynamic equality constraints by means of a quadratic penalty function so that the resulting real-time optimization becomes a (sparse), always feasible, bounded-variable least-squares (BVLS) problem. Criteria for guaranteeing closed-loop stability in spite of relaxing the dynamic equality constraints are provided. The approach is not only very simple to formulate, but also leads to a fast way of both constructing and solving the MPC problem in real time, a feature that is especially attractive when the linear model changes on line, such as when the model is obtained by linearizing a nonlinear model, by evaluating a linear parameter-varying model, or by recursive system identification. A comparison with the conventional state-space based MPC approach is shown in an example, demonstrating the effectiveness of the proposed method.
Year
Venue
Field
2017
2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC)
Least squares,Mathematical optimization,Multivariable calculus,Linear system,Computer science,Linear model,Control theory,Model predictive control,Input/output,System identification,Penalty method
DocType
ISSN
Citations 
Conference
0743-1546
0
PageRank 
References 
Authors
0.34
1
2
Name
Order
Citations
PageRank
Nilay Saraf100.34
Alberto Bemporad24353568.62