A Riccati-Based Interior Point Method for Efficient Model Predictive Control of SISO Systems
(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.
(Less)
- author
- Hagdrup, Morten
LU
; Johansson, Rolf
LU
and Bagterp Jørgensen, John
- organization
- publishing date
- 2017-07-01
- 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
- 2025-04-04 14:39:22
@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}}, keywords = {{Artificial Pancreas; closed-loop control; constrained optimization; interior point methods; linear systems; Predictive control; quadratic programming; Riccati iteration}}, 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}}, }