Vehicle optimal road departure prevention via model predictive control (original) (raw)
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Optimal control of vehicle dynamics for the prevention of road departure on curved roads
IEEE Transactions on Vehicular Technology
Runoff Road crashes are often associated with excessive speed in curves, which may happen when a driver is distracted or fails to compensate for reduced surface friction. This work introduces an Automated Emergency Cornering (AEC) system to protect against the major effects of over-speeding on curves, especially lateral deviation leading to lane or road departure. The AEC architecture has two levels: an upper level to perform motion planning, based on the optimal control of a nonlinear particle model, and a lower level to distribute the resulting two-dimensional acceleration reference to the available actuators. The lower level adopts the recently introduced Modified Hamiltonian Algorithm (MHA), which continuously adjusts the priority between mass-centre acceleration and yaw moment demands derived from lateral stability targets. AEC makes use of a high precision map and triggers control interventions based on vehicle kinematic states and detailed road geometry. To avoid false-positive interventions, AEC is triggered only when excessive road departure is predicted for the optimal particle motion. AEC then takes control of steering and individual wheel brake actuators to perform autonomous motion control for speed and path curvature at the limits of available friction. The AEC system is tested and evaluated using the high-fidelity simulation software CarMaker.
On Antilock Braking Systems With Road Preview Through Nonlinear Model Predictive Control
IEEE Transactions on Industrial Electronics, 2022
State-of-the-art antilock braking systems (ABS) are reactive, i.e., they activate after detecting that wheels tend to lock in braking. With vehicle-to-everything (V2X) connectivity becoming a reality, it will be possible to gather information on the tire-road friction conditions ahead, and use these data to enhance wheel slip control performance, especially during abrupt friction level variations. This study presents a nonlinear model predictive controller (NMPC) for ABS with preview of the tire-road friction profile. The potential benefits, optimal prediction horizon, and robustness of the preview algorithm are evaluated for different dynamic characteristics of the brake actuation system, through an experimentally validated simulation model. Proof-of-concept experiments with an electric vehicle prototype highlight the real-time capability of the proposed NMPC ABS, and the associated wheel slip control performance improvements in braking maneuvers with high-to-low friction transitions.
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arXiv (Cornell University), 2023
This paper proposes a non-linear Model Predictive Contouring Control (MPCC) for obstacle avoidance in automated vehicles driven at the limit of handling. The proposed controller integrates motion planning, path tracking and vehicle stability objectives, prioritising obstacle avoidance in emergencies. The controller's prediction model is a nonlinear single-track vehicle model with the Fiala tyre to capture the vehicle's non-linear behaviour. The MPCC computes the optimal steering angle and brake torques to minimise tracking error in safe situations and maximise the vehicle-to-obstacle distance in emergencies. Furthermore, the MPCC is extended with the tyre friction circle to fully exploit the vehicle's manoeuvrability and stability. The MPCC controller is tested using real-time rapid prototyping hardware to prove its real-time capability. The performance is compared with a state-of-the-art Model Predictive Control (MPC) in a high-fidelity simulation environment. The double lane change scenario results demonstrate a significant improvement in successfully avoiding obstacles and maintaining vehicle stability.
An improved model-based predictive control of vehicle trajectory by using nonlinear function
Journal of Mechanical Science and Technology, 2009
A new model-based predictive control algorithm for vehicle trajectory control is proposed by using vehicle velocity and sideslip angle. Based on the error function combined with vehicle velocity and side slip of a bicycle model, a predictive control method has been proven to be useful on low velocity. Thus, it could be applied for an autonomous vehicle without a driver. Although an autonomous robot is not necessary to be driven with a high velocity, a commercial vehicle has to be driven at high velocity. Thus the previous predictive control formulation is not enough for a commercial driving system. This study is proposed to enhance the capacity of the predictive controller for rather high speed vehicles.
Nonlinear Model Predictive Control using Lyapunov Functions for Vehicle Lateral Dynamics
IFAC-PapersOnLine, 2016
The purpose of this paper is the development of a nonlinear model based predictive control strategy for the autonomous control of the steering system for ground vehicles. The control system is aimed to automatically steer the vehicle along a desired trajectory. The developed strategy uses control Lyapunov functions to guarantee the stability of the closed-loop control system. In order to obtain reliable results, a nonlinear vehicle dynamic model is used in the design phase of the controller. The model incorporates the tire-ground contact nonlinearities and describes with higher accuracy the real vehicle dynamics. The proposed approach is validated using simulation results and it is shown that this approach could provide good performances in practical use.
Integrated Computer-Aided Engineering, 2015
The vehicular collision can lead to serious casualties and traffic congestions, especially multiple-vehicle collision. Most recent studies mainly focused on collision warning and avoidance strategies for two consecutive vehicles, but only a few on multiple-vehicle situations. This study proposes a coordinated brake control (CBC) strategy for multiple vehicles to minimize the risk of rear-end collision using model predictive control (MPC) framework. The objective is to minimize total impact energy by determining the desired braking force, where the impact energy is defined as the relative kinetic energy for a consecutive pair of vehicles. Under the MPC framework, this problem is further converted to a quadratic programming at each time step for numerical computations. To compare the performance, three other control strategies, i.e. direct brake control (DBC), driver reaction based brake control (DRBC) and linear quadratic regulator (LQR) control are also considered in this paper. The simulation results, in both a typical scenario and a huge number of scenarios under stochastic situations, show that CBC strategy has the best performance among these four strategies. The proposed CBC strategy has the potential to avoid the collision among a group of vehicles, and to mitigate the impact in cases where the collision is unavoidable.
Modelling of Dynamic Speed Limits Using the Model Predictive Control
International Journal of Advanced Studies, 2017
The article considers the issues of traffic management using intelligent system "Car-Road" (IVHS), which consist of interacting intelligent vehicles (IV) and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering). Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC). The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.
LTV-MPC for Yaw Rate Control and Side Slip Control with Dynamically Constrained Differential Braking
European Journal of Control, 2009
In this paper a novel vehicle lateral dynamic control approach is presented. A differential braking control law based on vehicle planar motion has been designed using a two-degrees-of-freedom vehicle model. On the basis of the estimate of tire longitudinal forces we estimate the range of lateral forces which the tire can exert. Using this constraints a model predictive control (MPC) based on a two-track model is designed in order to stabilize the vehicle. The performances are estimated comparing the results with standard manoeuvers. Simulation results show the benefits of the control methodology used: in particular we show how very effective distribution of braking torque are obtained as a result of this feedback policy.
Optimal Emergency Vehicle Braking Control Based on Dynamic Friction Model
2005
th 2005 ABSTRACT A dynamic friction model for the tire-road interface is used in an optimal control scheme for emergency braking of vehicles. The controller sets a target relative velocity curve that the vehicle must track in order to achieve braking in minimum time. It is shown that this curve corresponds to the solution of a minimum time optimal control
Model Predictive Control - Theory and Applications [Working Title]
In this chapter, we present a study on autonomous vehicle path tracking utilizing both Linear, Linear Time Varying, Nonlinear Model Predictive controllers and evaluate their performance on a given path. The vehicle is both a linear and non-linear bicycle model with front steering . The performance metric in the evaluation of the controller/path tracker is the root mean square error between the defined path and the resulting vehicle path. The path tracking algorithm utilized are Serret-Frenet Equations and a modified version of Pure Pursuit. The robustness of the controllers are examined by varying vehicle mass, tire-road coefficient of friction, and tire cornering stiffness.