Journal article
Fast self-triggered MPC for constrained linear systems with additive disturbances
- Abstract:
- This paper proposes a robust self-triggered model predictive control (MPC) algorithm for a class of constrained linear systems subject to bounded additive disturbances, in which the inter-sampling time is determined by a fast convergence self-triggered mechanism. The main idea of the self-triggered mechanism is to select a sampling interval so that a rapid decrease in the predicted costs associated with optimal predicted control inputs is guaranteed. This allows for a reduction in the required computation without compromising performance. By using a constraint tightening technique and exploring the nature of the open-loop control between sampling instants, a set of minimally conservative constraints is imposed on nominal states to ensure robust constraint satisfaction. A multi-step openloop MPC optimization problem is formulated, which ensures recursive feasibility for all possible realisations of the disturbance. The closed-loop system is guaranteed to satisfy a mean-square stability condition. To further reduce the computational load, when states reach a predetermined neighbourhood of the origin, the control law of the robust self-triggered MPC algorithm switches to a self-triggered local controller. A compact set in the state space is shown to be robustly asymptotically stabilized. Numerical comparisons are provided to demonstrate the effectiveness of the proposed strategies.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, 2.1MB, Terms of use)
-
- Publisher copy:
- 10.1109/TAC.2020.3022734
Authors
- Publisher:
- Institute of Electrical and Electronics Engineers
- Journal:
- IEEE Transactions on Automatic Control More from this journal
- Volume:
- 66
- Issue:
- 8
- Pages:
- 3624-3637
- Publication date:
- 2020-09-08
- Acceptance date:
- 2020-09-05
- DOI:
- ISSN:
-
0018-9286
- Language:
-
English
- Keywords:
- Pubs id:
-
1130892
- Local pid:
-
pubs:1130892
- Deposit date:
-
2020-09-07
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2020
- Rights statement:
- © IEEE 2020.
- Notes:
- This is the accepted manuscript version of the article. The final version is available from IEEE at: https://doi.org/10.1109/TAC.2020.3022734
If you are the owner of this record, you can report an update to it here: Report update to this record