Self-triggered Model Predictive Control for Nonlinear Input-Affine Dynamical Systems via Adaptive Control Samples Selection
Optimization and Control
2016-11-17 v1
Abstract
In this paper, we propose a self-triggered formulation of Model Predictive Control for continuous-time nonlinear input-affine networked control systems. Our control method specifies not only when to execute control tasks but also provides a way to discretize the optimal control trajectory into several control samples, so that the reduction of communication load will be obtained. Stability analysis under the sample-and-hold implementation is also given, which guarantees that the state converges to a terminal region where the system can be stabilized by a local state feedback controller. Some simulation examples validate our proposed framework.
Cite
@article{arxiv.1603.03677,
title = {Self-triggered Model Predictive Control for Nonlinear Input-Affine Dynamical Systems via Adaptive Control Samples Selection},
author = {Kazumune Hashimoto and Shuichi Adachi and Dimos. V. Dimarogonas},
journal= {arXiv preprint arXiv:1603.03677},
year = {2016}
}
Comments
To appear in IEEE TAC