A Model Predictive Control Strategy for Lateral and Longitudinal Dynamics in Autonomous Driving

Abstract This paper presents a controller dedicated to the lateral and longitudinal vehicle dynamics control for autonomous driving. The proposed strategy exploits a Model Predictive Control strategy to perform lateral guidance and speed regulation. To this end, the algorithm controls the steering angle and the throttle and brake pedals for minimizing the vehicle’s lateral deviation and relative yaw angle with respect to the reference trajectory, while the vehicle speed is controlled to drive at the maximum acceptable longitudinal speed considering the adherence and legal speed limits. The technique exploits data computed by a simulated camera mounted on the top of the vehicle while moving in different driving scenarios. The longitudinal control strategy is based on a reference speed generator, which computes the maximum speed considering the road geometry and lateral motion of the vehicle at the same time. The proposed controller is tested in highway, interurban and urban driving scenarios to check the performance of the proposed method in different driving environments.

Download Full-text

Human-like trapezoidal steering angle model on two-lane urban curves

International Journal of Advanced Robotic Systems ◽

10.1177/1729881419867614 ◽

2019 ◽

Vol 16 (4) ◽

pp. 172988141986761 ◽

Cited By ~ 1

Author(s):

Haobin Jiang ◽

Jie Zhou ◽

Aoxue Li ◽

Xinchen Zhou ◽

Shidian Ma

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Rapid Development ◽

Vehicle Speed ◽

Automated Vehicles ◽

Automated Driving ◽

Steering Angle ◽

The Road ◽

Human Likeness ◽

Simulation Results

With the rapid development of automated vehicles, there is currently a significant amount of automated driving research. Giving automated vehicles capabilities similar to those of experienced drivers will allow them to share the road harmoniously with manned vehicles, especially on two-lane urban curves. To represent the steering behavior of experienced drivers, a series of curve feature distances are proposed, which is determined by multi-regression. These series of curve feature distances are used to generate a trapezoidal steering angle model which imitates the steering behavior of the experienced test drivers. To verify the feasibility and human-likeness of the proposed trapezoidal steering angle model, the model is used with constant vehicle speed to plan a human-like trajectory which is tracked using model predictive control. The simulation results show that the proposed trapezoidal steering angle model is human-like and could be used to give automated vehicles human-like driving capability when driving on two-lane curves.

Download Full-text

Model Predictive Control for Autonomous Driving Vehicles

Electronics ◽

10.3390/electronics10212593 ◽

2021 ◽

Vol 10 (21) ◽

pp. 2593

Author(s):

Trieu Minh Vu ◽

Reza Moezzi ◽

Jindrich Cyrus ◽

Jaroslav Hlava

Keyword(s):

Optimal Control ◽

Model Predictive Control ◽

Predictive Control ◽

Nonlinear Model ◽

Autonomous Driving ◽

Nonlinear Model Predictive Control ◽

System Stability ◽

Vehicle Speed ◽

Model Predictive Controller ◽

Predictive Controller

The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subject to hard and softened constraints. Nonlinear model predictive control subject to softened constraints provides a higher probability of the controller finding the optimal control actions and maintaining system stability. Different parameters of the nonlinear model predictive controller are simulated and analyzed. Results show that nonlinear model predictive control with softened constraints can considerably improve the ability of autonomous driving vehicles to track exactly on different trajectories.

Download Full-text

Vehicle optimal road departure prevention via model predictive control

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407017701286 ◽

2017 ◽

Vol 231 (7) ◽

pp. 952-962 ◽

Cited By ~ 4

Author(s):

Hongliang Yuan ◽

Yangyan Gao ◽

Timothy J Gordon

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

High Speed ◽

Weather Conditions ◽

Particle Model ◽

Vehicle Speed ◽

Safety Control ◽

The Road ◽

Adverse Weather ◽

Actuator Control

This article addresses the problem of road departure prevention using integrated brake control. The scenario considered is when a high-speed vehicle leaves the highway on a curve and enters the shoulder or another lane, owing to excessive speed or a reduction in the friction of the road due to adverse weather conditions. In such a scenario, the vehicle speed is too high for the available tyre–road friction and road departure is inevitable; however, its effect can be minimized with an optimal braking strategy. To achieve online implementation, the task is formulated as a receding horizon optimization problem and solved in a linear model predictive control (MPC) framework. In this formulation, a nonlinear tyre model is adopted in order to work properly at the friction limits. The optimization results are close to those obtained previously using a particle model optimization, parabolic path reference (PPR), coupled to a control algorithm, the modified Hamiltonian algorithm (MHA), specifically designed to operate at the vehicle friction limits. This shows that the MPC formulation may be equally effective for vehicle control at the friction limits. The major difference here, compared with the earlier PPR/MHA control formulation, is that the proposed MPC strategy directly generates an optimal brake sequence, while PPR provides an optimal reference first, then MHA responds to the reference to give closed-loop actuator control. The presented MPC approach has the potential for use in future vehicle systems as part of the overall active safety control to improve overall vehicle agility and safety.

Download Full-text

Composite Path Tracking Control for Tractor–Trailer Vehicles Via Constrained Model Predictive Control and Direct Adaptive Fuzzy Techniques

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4036884 ◽

2017 ◽

Vol 139 (11) ◽

Cited By ~ 11

Author(s):

Ming Yue ◽

Xiaoqiang Hou ◽

Wenbin Hou

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Control Strategy ◽

Tracking Control ◽

Path Tracking ◽

Reference Trajectory ◽

Control Effect ◽

Adaptive Fuzzy ◽

Dynamic Controller ◽

Fuzzy Techniques

Tractor–trailer vehicles will suffer from nonholonomic constraint, uncertain disturbance, and various physical limits, when they perform path tracking maneuver autonomously. This paper presents a composite path tracking control strategy to tackle the various problems arising from not only vehicle kinematic but also dynamic levels via two powerful control techniques. The proposed composite control structure consists of a model predictive control (MPC)-based posture controller and a direct adaptive fuzzy-based dynamic controller, respectively. The former posture controller can make the underactuated trailer midpoint follow an arbitrary reference trajectory given by the earth-fixed frame, as well as satisfying various physical limits. Meanwhile, the latter dynamic controller enables the vehicle velocities to track the desired velocities produced by the former one, and the global asymptotical convergence of dynamic controller is strictly guaranteed in the sense of Lyapunov stability theorem. The simulation results illustrate that the presented control strategy can achieve a coordinated control effect for the sophisticated tractor–trailer vehicles, thereby enhancing their movement performance in complex environments.

Download Full-text

Path Following Control of Autonomous Underwater Vehicle Using Nonlinear Model Predictive Control

Volume 6B: Ocean Engineering ◽

10.1115/omae2020-18241 ◽

2020 ◽

Author(s):

Tomoki Taniguchi ◽

Jun Umeda ◽

Toshifumi Fujiwara ◽

Kangsoo Kim ◽

Takumi Sato ◽

...

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Nonlinear Model ◽

Control Strategy ◽

Autonomous Underwater Vehicle ◽

Path Following ◽

Nonlinear Model Predictive Control ◽

Reference Trajectory ◽

Arrival Times ◽

Path Following Control

Abstract The path following control of an AUV considering arrival times at waypoints is proposed in this paper. The temporal constraint is considered by adding the surge velocity and the nominal thrust force as reference trajectory in the objective function of the nonlinear model predictive control (NMPC). The proposed control strategy uses fewer reference variables than conventional trajectory tracking problems. The simulated results of the proposed control strategy are compared to the NMRI Cruising AUV#4 actual dive data. The simulated arrival times of waypoints were matched well to the measured data. Two guidance laws, the line of sight with lookahead-based steering law and the pure pursuit guidance law, are also applied to NMPC to determine reference yaw angle.

Download Full-text