Model Predictive Control with Variable Prediction Horizon for a System including Time-varying Delay

Arai, Hiroki; Uchimura, Yutaka

doi:10.1109/iecon.2019.8927383

Cited by 6 publications

(1 citation statement)

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“…However, the event-based mechanism relied on the error threshold between the current and reference states that reduced the prediction horizon and the solving frequency and thus could not foresee further in the prediction horizon. A study that fused varying the prediction horizon and time-varying delays was introduced in [11], where the authors proposed a method for utilizing only resources that are available at a specific time instant by adjusting the prediction horizon properly. The idea of predicting future driving trajectory based on uncertainties for different time horizons with the use of multiple sensors to create a robust and trustworthy prediction system was studied in [12].…”

Section: Background and Motivationmentioning

confidence: 99%

Multi-Stage NMPC for a MAV based Collision Free Navigation Under Varying Communication Delays

Papadimitriou

Jafari²,

Mansouri³

et al. 2023

J Intell Robot Syst

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Time delays in communication networks are one of the main concerns in deploying robots with computation boards on the edge. This article proposes a multi-stage Nonlinear Model Predictive Control (NMPC) that is capable of handling varying network-induced time delays for establishing a control framework being able to guarantee collision-free Micro Aerial Vehicles (MAVs) navigation. This study introduces a novel approach that considers different sampling times by a tree of discretization scenarios contrary to the existing typical multi-stage NMPC where system uncertainties are modeled by a tree of scenarios. Additionally, the proposed method considers adaptive weights for the multi-stage NMPC scenarios based on the probability of time delays in the communication link. As a result of the multi-stage NMPC, the obtained optimal control action is valid for multiple sampling times. Finally, the overall effectiveness of the proposed novel control framework is demonstrated in various tests and different simulation environments.

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Section: Background and Motivationmentioning

confidence: 99%