.png){kind=icon}
Return
A Distributionally Robust Optimization-Based Stochastic Self-Triggered Model Predictive Control
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2025.3649661.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this article, we propose a self-triggered distributionally robust model predictive control algorithm for linear discrete systems with state chance constraints and unbounded stochastic disturbances. Assuming that only the first and second moments of the disturbance are accessible, we transform the objective function into a compact quadratic form and reformulate the state chance constraints into linear inequalities, which is more tractable when solving. In order to reduce communication and sampling times of the system, we propose a self-triggered update scheme, in which the state sampling and the control input sequence are updated when the control performance predicted based on the current sampling exceeds that of the periodic sampling scheme. We demonstrate that the optimization problem in the proposed self-triggered model predictive control (MPC) method is recursively feasible and stable. Numerical simulation results verify the effectiveness of the proposed algorithm.
Keywords:
Chance constraints
distributionally robust optimization (DRO)
self-triggered model predictive control
Journal
I
IF:
0
Papers:
240
Citations:
0
Organization
Cited Papers
Event-triggered and self-triggered control for linear systems based on reachable sets.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSaveShareSave
ShareSaveShareSaveShareSaveSelf-Triggered Output Feedback Control of Linear Plants in the Presence of Unknown Disturbances
PREAI
ShareSaveShareSave
View more