Self-triggered Model Predictive Control for Continuous-Time Systems: A Multiple Discretizations Approach
Optimization and Control
2016-09-09 v1
Abstract
In this paper, we propose a new self-triggered formulation of Model Predictive Control for continuous-time linear networked control systems. Our control approach, which aims at reducing the number of transmitting control samples to the plant, is derived by parallelly solving optimal control problems with different sampling time intervals. The controller then picks up one sampling pattern as a transmission decision, such that a reduction of communication load and the stability will be obtained. The proposed strategy is illustrated through comparative simulation examples.
Cite
@article{arxiv.1609.02259,
title = {Self-triggered Model Predictive Control for Continuous-Time Systems: A Multiple Discretizations Approach},
author = {K. Hashimoto and S. Adachi and D. V. Dimarogonas},
journal= {arXiv preprint arXiv:1609.02259},
year = {2016}
}
Comments
extended version of CDC 2016