Dynamic option hedging with transaction costs: A stochastic model predictive control approach

2017 ◽  
Vol 29 (15) ◽  
pp. 5058-5077 ◽  
Author(s):  
Mogens Graf Plessen ◽  
Laura Puglia ◽  
Tommaso Gabbriellini ◽  
Alberto Bemporad
Keyword(s):  
Model Predictive Control ◽  
Stochastic Model ◽  
Transaction Costs ◽  
Predictive Control ◽  
Control Approach ◽  
Option Hedging ◽  
Stochastic Model Predictive Control

Stochastic model predictive control for driver assistance control of intelligent vehicles considering uncertain driving environment

2022 ◽  
pp. 107754632110523
Author(s):  
Yimin Chen ◽  
Yunxuan Song ◽  
Liru Shi ◽  
Jian Gao
Keyword(s):  
Model Predictive Control ◽  
Stochastic Model ◽  
Predictive Control ◽  
Control Method ◽  
Controller Design ◽  
Intelligent Vehicles ◽  
Driving Safety ◽  
Driver Assistance ◽  
Control Approach ◽  
Stochastic Model Predictive Control

Advanced driver assistance control faces great challenges in cooperating with the nearby vehicles. The assistance controller of an intelligent vehicle has to provide control efforts properly to prevent possible collisions without interfering with the drivers. This paper proposes a novel driver assistance control method for intelligent ground vehicles to cooperate with the nearby vehicles, using the stochastic model predictive control algorithm. The assistance controller is designed to correct the drivers’ steering maneuvers when there is a risk of possible collisions, so that the drivers are not interfered. To enhance the cooperation between the vehicles, the nearby vehicle motion is predicted and included in the assistance controller design. The position uncertainties of the nearby vehicle are considered by the stochastic model predictive control approach via chance constraints. Simulation studies are conducted to validate the proposed control method. The results show that the assistance controller can help the drivers avoid possible collisions with the nearby vehicles and the driving safety can be guaranteed.

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