.png){kind=icon}
Return
Event-Triggered Model Predictive Control With a Statistical Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2019.2916626.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The event-triggered model predictive control (MPC) reduces energy consumption for updating control sequences while maintaining the originality of the MPC, which copes with hard constraints on dynamical systems. In the presence of large uncertainties, however, the standard event-triggered MPC may generate too frequent event occurrences. To compensate for unknown uncertainties, this paper applies a statistical learning to event-triggered MPC. The stability and the feasibility of the proposed control system are analyzed in regard to the statistical learning influences, such as the number of training samples, model complexity, and learning parameters. Accordingly, the event-triggering policy is established to guarantee the stability. We evaluate the proposed algorithm for the tracking problem of a nonholonomic model perturbed by uncertainties. In comparison with the standard event-triggered control scheme, the simulation results of the proposed method show better tracking performance with less frequent event triggers.
Keywords:
Uncertainty
Stability analysis
Control systems
Optimization
Training
Statistical learning
Predictive control
Empirical risk minimization (ERM)
event-triggered control
model predictive control (MPC)
statistical learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
Organization
Cited Papers
ShareSaveShareSavePharmacokinetics of subcutaneous recombinant human granulocyte colony- stimulating factor in children.png)
Blood
OAAI
BloodIF0
OAAIShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more