Details
| Original language | English |
|---|---|
| Article number | 9044326 |
| Pages (from-to) | 794-801 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Automatic Control |
| Volume | 66 |
| Issue number | 2 |
| Publication status | Published - 23 Mar 2020 |
Abstract
In this article, we present a nonlinear robust model predictive control (MPC) framework for general (state and input dependent) disturbances. This approach uses an online constructed tube in order to tighten the nominal (state and input) constraints. To facilitate an efficient online implementation, the shape of the tube is based on an offline computed incremental Lyapunov function with a corresponding (nonlinear) incrementally stabilizing feedback. Crucially, the online optimization only implicitly includes these nonlinear functions in terms of scalar bounds, which enables an efficient implementation. Furthermore, to account for an efficient evaluation of the worst case disturbance, a simple function is constructed offline that upper bounds the possible disturbance realizations in a neighborhood of a given point of the open-loop trajectory. The resulting MPC scheme ensures robust constraint satisfaction and practical asymptotic stability with a moderate increase in the online computational demand compared to a nominal MPC. We demonstrate the applicability of the proposed framework in comparison to state-of-the-art robust MPC approaches with a nonlinear benchmark example.
Keywords
- constrained control, Nonlinear model predictive control (MPC), robust MPC, uncertain systems
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Science Applications
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In: IEEE Transactions on Automatic Control, Vol. 66, No. 2, 9044326, 23.03.2020, p. 794-801.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A computationally efficient robust model predictive control framework for uncertain nonlinear systems
AU - Koehler, Johannes
AU - Soloperto, Raffaele
AU - Müller, Matthias A.
AU - Allgöwer, Frank
N1 - Funding information: Manuscript received October 22, 2019; revised February 25, 2020; accepted March 15, 2020. Date of publication March 23, 2020; date of current version January 28, 2021. This work was supported by the German Research Foundation under Grant GRK 2198/1-277536708, Grant AL 316/12-2, and Grant MU 3929/1-2-279734922. The work of Raffaele Soloperto was supported by the International Max Planck Research School for Intelligent Systems. Recommended by Associate Editor E. C. Kerrigan. (Corresponding author: Johannes Köhler.) Johannes Köhler, Raffaele Soloperto, and Frank Allgöwer are with the Institute for Systems Theory and Automatic Control, University of Stuttgart, 70550 Stuttgart, Germany (e-mail: johannes.koehler@ist.uni-stuttgart.de; raffaele.soloperto@ist.uni-stuttgart.de; mueller@irt.uni-hannover.de).
PY - 2020/3/23
Y1 - 2020/3/23
N2 - In this article, we present a nonlinear robust model predictive control (MPC) framework for general (state and input dependent) disturbances. This approach uses an online constructed tube in order to tighten the nominal (state and input) constraints. To facilitate an efficient online implementation, the shape of the tube is based on an offline computed incremental Lyapunov function with a corresponding (nonlinear) incrementally stabilizing feedback. Crucially, the online optimization only implicitly includes these nonlinear functions in terms of scalar bounds, which enables an efficient implementation. Furthermore, to account for an efficient evaluation of the worst case disturbance, a simple function is constructed offline that upper bounds the possible disturbance realizations in a neighborhood of a given point of the open-loop trajectory. The resulting MPC scheme ensures robust constraint satisfaction and practical asymptotic stability with a moderate increase in the online computational demand compared to a nominal MPC. We demonstrate the applicability of the proposed framework in comparison to state-of-the-art robust MPC approaches with a nonlinear benchmark example.
AB - In this article, we present a nonlinear robust model predictive control (MPC) framework for general (state and input dependent) disturbances. This approach uses an online constructed tube in order to tighten the nominal (state and input) constraints. To facilitate an efficient online implementation, the shape of the tube is based on an offline computed incremental Lyapunov function with a corresponding (nonlinear) incrementally stabilizing feedback. Crucially, the online optimization only implicitly includes these nonlinear functions in terms of scalar bounds, which enables an efficient implementation. Furthermore, to account for an efficient evaluation of the worst case disturbance, a simple function is constructed offline that upper bounds the possible disturbance realizations in a neighborhood of a given point of the open-loop trajectory. The resulting MPC scheme ensures robust constraint satisfaction and practical asymptotic stability with a moderate increase in the online computational demand compared to a nominal MPC. We demonstrate the applicability of the proposed framework in comparison to state-of-the-art robust MPC approaches with a nonlinear benchmark example.
KW - constrained control
KW - Nonlinear model predictive control (MPC)
KW - robust MPC
KW - uncertain systems
UR - http://www.scopus.com/inward/record.url?scp=85100388305&partnerID=8YFLogxK
U2 - 10.1109/TAC.2020.2982585
DO - 10.1109/TAC.2020.2982585
M3 - Article
VL - 66
SP - 794
EP - 801
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
SN - 0018-9286
IS - 2
M1 - 9044326
ER -