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.

A tube-based robust MPC for duty-cycled rotation needle steering systems with bounded disturbances

2021 ◽  
pp. 014233122110430
Author(s):  
Zhi Qi ◽  
Qianyue Luo ◽  
Hui Zhang
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Control Method ◽  
Controller Design ◽  
Control Approach ◽  
Bounded State ◽  
Non Linear ◽  
System States ◽  
Trajectory Tracking Controller

In this paper, we aim to design the trajectory tracking controller for variable curvature duty-cycled rotation flexible needles with a tube-based model predictive control approach. A non-linear model is adopted according to the kinematic characteristics of the flexible needle and a bicycle method. The modeling error is assumed to be an unknown but bounded disturbance. The non-linear model is transformed to a discrete time form for the benefit of predictive controller design. From the application perspective, the flexible needle system states and control inputs are bounded within a robust invariant set when subject to disturbance. Then, the tube-based model predictive control is designed for the system with bounded state vector and inputs. Finally, the simulation experiments are carried out with tube-based model predictive control and proportional integral derivative controller based on the particle swarm optimisation method. The simulation results show that the tube-based model predictive control method is more robust and it leads to much smaller tracking errors in different scenarios.

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