reference governor
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Automatica ◽  
2022 ◽  
Vol 136 ◽  
pp. 110089
Author(s):  
Mehdi Hosseinzadeh ◽  
Ilya Kolmanovsky ◽  
Sanjoy Baruah ◽  
Bruno Sinopoli

2022 ◽  
Author(s):  
Kosuke Ikeya ◽  
Kaiwen Liu ◽  
Anouck Girard ◽  
Ilya V. Kolmanovsky
Keyword(s):  

Author(s):  
Kaiwen Liu ◽  
Nan Li ◽  
Ilya Kolmanovsky ◽  
Denise Rizzo ◽  
Anouck Girard

Abstract This paper proposes a learning reference governor (LRG) approach to enforce state and control constraints in systems for which an accurate model is unavailable; and this approach enables the reference governor to gradually improve command tracking performance through learning while enforcing the constraints during learning and after learning is completed. The learning can be performed either on a black-box type model of the system or directly on the hardware. After introducing the LRG algorithm and outlining its theoretical properties, this paper investigates LRG application to fuel truck (tank truck) rollover avoidance. Through simulations based on a fuel truck model that accounts for liquid fuel sloshing effects, we show that the proposed LRG can effectively protect fuel trucks from rollover accidents under various operating conditions.


Automatica ◽  
2021 ◽  
Vol 133 ◽  
pp. 109864
Author(s):  
Nan Li ◽  
Anouck Girard ◽  
Ilya Kolmanovsky
Keyword(s):  

2021 ◽  
Author(s):  
Chuan Yan ◽  
Ke Shao ◽  
Xueqian Wang ◽  
Jinchuan Zheng ◽  
Bin Liang

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