Fast self-triggered MPC for constrained linear systems with additive disturbances
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...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers
2020
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| _version_ | 1826278610385764352 |
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| author | Dai, L Cannon, M Yang, F Yan, S |
| author_facet | Dai, L Cannon, M Yang, F Yan, S |
| author_sort | Dai, L |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T23:46:30Z |
| format | Journal article |
| id | oxford-uuid:711939d6-07bc-46d7-9005-b7d8d279fe4c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:46:30Z |
| publishDate | 2020 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:711939d6-07bc-46d7-9005-b7d8d279fe4c2022-03-26T19:41:23ZFast self-triggered MPC for constrained linear systems with additive disturbancesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:711939d6-07bc-46d7-9005-b7d8d279fe4cEnglishSymplectic ElementsInstitute of Electrical and Electronics Engineers2020Dai, LCannon, MYang, FYan, SThis 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. |
| spellingShingle | Dai, L Cannon, M Yang, F Yan, S Fast self-triggered MPC for constrained linear systems with additive disturbances |
| title | Fast self-triggered MPC for constrained linear systems with additive disturbances |
| title_full | Fast self-triggered MPC for constrained linear systems with additive disturbances |
| title_fullStr | Fast self-triggered MPC for constrained linear systems with additive disturbances |
| title_full_unstemmed | Fast self-triggered MPC for constrained linear systems with additive disturbances |
| title_short | Fast self-triggered MPC for constrained linear systems with additive disturbances |
| title_sort | fast self triggered mpc for constrained linear systems with additive disturbances |
| work_keys_str_mv | AT dail fastselftriggeredmpcforconstrainedlinearsystemswithadditivedisturbances AT cannonm fastselftriggeredmpcforconstrainedlinearsystemswithadditivedisturbances AT yangf fastselftriggeredmpcforconstrainedlinearsystemswithadditivedisturbances AT yans fastselftriggeredmpcforconstrainedlinearsystemswithadditivedisturbances |