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arXiv · 1603.03677

Self-triggered Model Predictive Control for Nonlinear Input-Affine Dynamical Systems via Adaptive Control Samples Selection

Kazumune Hashimoto·Shuichi Adachi·Dimos. V. Dimarogonas
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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.

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Kazumune Hashimoto, Shuichi Adachi, Dimos. V. Dimarogonas. 2016-03-11. Self-triggered Model Predictive Control for Nonlinear Input-Affine Dynamical Systems via Adaptive Control Samples Selection. https://doi.org/10.1109/tac.2016.2537741

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