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A Riccati-Based Interior Point Method for Efficient Model Predictive Control of SISO Systems

Hagdrup, Morten LU ; Johansson, Rolf LU and Bagterp Jørgensen, John (2017) In IFAC-PapersOnLine 50(1). p.10672-10678
Abstract

This paper presents an algorithm for Model Predictive Control of SISO systems. Based on a quadratic objective in addition to (hard) input constraints it features soft upper as well as lower constraints on the output and an input rate-of-change penalty term. It keeps the deterministic and stochastic model parts separate. The controller is designed based on the deterministic model, while the Kalman filter results from the stochastic part. The controller is implemented as a primal-dual interior point (IP) method using Riccati recursion and the computational savings possible for SISO systems. In particular the computational complexity scales linearly with the control horizon. No warm-start strategies are considered. Numerical examples are... (More)

This paper presents an algorithm for Model Predictive Control of SISO systems. Based on a quadratic objective in addition to (hard) input constraints it features soft upper as well as lower constraints on the output and an input rate-of-change penalty term. It keeps the deterministic and stochastic model parts separate. The controller is designed based on the deterministic model, while the Kalman filter results from the stochastic part. The controller is implemented as a primal-dual interior point (IP) method using Riccati recursion and the computational savings possible for SISO systems. In particular the computational complexity scales linearly with the control horizon. No warm-start strategies are considered. Numerical examples are included illustrating applications to Artificial Pancreas technology. We provide typical execution times for a single iteration of the IP algorithm and the number of iterations required for convergence in different situations.

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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial Pancreas, closed-loop control, constrained optimization, interior point methods, linear systems, Predictive control, quadratic programming, Riccati iteration
in
IFAC-PapersOnLine
volume
50
issue
1
pages
7 pages
publisher
IFAC Secretariat
external identifiers
  • scopus:85031795538
ISSN
2405-8963
DOI
10.1016/j.ifacol.2017.08.2184
language
English
LU publication?
yes
id
ba767669-ed15-45e9-afce-9543d951d2f1
date added to LUP
2017-10-31 08:05:02
date last changed
2020-12-22 03:35:12
@article{ba767669-ed15-45e9-afce-9543d951d2f1,
  abstract     = {<p>This paper presents an algorithm for Model Predictive Control of SISO systems. Based on a quadratic objective in addition to (hard) input constraints it features soft upper as well as lower constraints on the output and an input rate-of-change penalty term. It keeps the deterministic and stochastic model parts separate. The controller is designed based on the deterministic model, while the Kalman filter results from the stochastic part. The controller is implemented as a primal-dual interior point (IP) method using Riccati recursion and the computational savings possible for SISO systems. In particular the computational complexity scales linearly with the control horizon. No warm-start strategies are considered. Numerical examples are included illustrating applications to Artificial Pancreas technology. We provide typical execution times for a single iteration of the IP algorithm and the number of iterations required for convergence in different situations.</p>},
  author       = {Hagdrup, Morten and Johansson, Rolf and Bagterp Jørgensen, John},
  issn         = {2405-8963},
  language     = {eng},
  month        = {07},
  number       = {1},
  pages        = {10672--10678},
  publisher    = {IFAC Secretariat},
  series       = {IFAC-PapersOnLine},
  title        = {A Riccati-Based Interior Point Method for Efficient Model Predictive Control of SISO Systems},
  url          = {http://dx.doi.org/10.1016/j.ifacol.2017.08.2184},
  doi          = {10.1016/j.ifacol.2017.08.2184},
  volume       = {50},
  year         = {2017},
}