scholarly journals A Variable-Sampling Time Model Predictive Control Algorithm for Improving Path-Tracking Performance of a Vehicle

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6845
Author(s):  
Yoonsuk Choi ◽  
Wonwoo Lee ◽  
Jeesu Kim ◽  
Jinwoo Yoo
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Path Following ◽  
Path Tracking ◽  
Sampling Time ◽  
Tracking Performance ◽  
Control Input ◽  
Automated Driving ◽  
Lateral Velocity ◽  
Time Model

This paper proposes a novel model predictive control (MPC) algorithm that increases the path tracking performance according to the control input. The proposed algorithm reduces the path tracking errors of MPC by updating the sampling time of the next step according to the control inputs (i.e., the lateral velocity and front steering angle) calculated in each step of the MPC algorithm. The scenarios of a mixture of straight and curved driving paths were constructed, and the optimal control input was calculated in each step. In the experiment, a scenario was created with the Automated Driving Toolbox of MATLAB, and the path-following performance characteristics and computation times of the existing and proposed MPC algorithms were verified and compared with simulations. The results prove that the proposed MPC algorithm has improved path-following performance compared to those of the existing MPC algorithm.

Integrated Line of Sight and Model Predictive Control for Path Following and Roll Motion Control Using Rudder

2015 ◽  
Vol 59 (02) ◽  
pp. 99-112 ◽  
Author(s):  
Cheng Liu ◽  
Jing Sun ◽  
Zaojian Zou
Keyword(s):  
Model Predictive Control ◽  
Motion Control ◽  
Predictive Control ◽  
Path Following ◽  
Design Parameters ◽  
Line Of Sight ◽  
Roll Motion ◽  
Control Input ◽  
Problem Model ◽  
Trade Offs

Roll motion control and path following are two representative marine control problems that have been traditionally treated separately. However, these two problems are closely coupled, as roll motion could cause negative effects on marine surface vessels during path following in seaways and path following actions could cause undesirable roll motion. In this article, an optimal controller is proposed for the integrated path following and roll motion control problem. The rudder, whose actuation amplitude and rate are both limited, is the only control input, while the cross-track error, heading angle, roll rate, and roll angle are the outputs that collectively define the performance of the system. This leads to a classic underactuated problem. Model predictive control is the natural choice for the solution, given its capability in dealing with constraints and multi-input-multi-output system, and its design will be pursued in this article. Line of sight technique is used to extend the straight-line path following to arbitrary path following. A four degrees of freedom high-fidelity model is implemented as the simulation model, and the simulation results verify the effectiveness of the proposed controller. The influences of the control design parameters on system performance are investigated and the trade-offs between two key attributes, namely, the roll reduction and path following, are explored.

scholarly journals Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2703
Author(s):  
Jui-An Yang ◽  
Chung-Hsien Kuo
Keyword(s):  
Model Predictive Control ◽  
Electric Vehicle ◽  
Predictive Control ◽  
Autonomous Vehicle ◽  
Path Tracking ◽  
Tracking Performance ◽  
Campus Environment ◽  
Rtk Gps ◽  
Vehicle Positioning

This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a campus environment, including vehicle positioning and path tracking. Such a project is helpful to the students to learn and practice key technologies of autonomous vehicles conveniently. Therefore, a laboratory-made EV was equipped with real-time kinematic GPS (RTK-GPS) to provide centimeter position accuracy. Furthermore, the model predictive control (MPC) was proposed to perform the path tracking capability. Nevertheless, the RTK-GPS exhibited some robust positioning concerns in practical application, such as a low update rate, signal obstruction, signal drift, and network instability. To solve this problem, a multisensory fusion approach using an unscented Kalman filter (UKF) was utilized to improve the vehicle positioning performance by further considering an inertial measurement unit (IMU) and wheel odometry. On the other hand, the model predictive control (MPC) is usually used to control autonomous EVs. However, the determination of MPC parameters is a challenging task. Hence, reinforcement learning (RL) was utilized to generalize the pre-trained datum value for the determination of MPC parameters in practice. To evaluate the performance of the RL-based MPC, software simulations using MATLAB and a laboratory-made, full-scale electric vehicle were arranged for experiments and validation. In a 199.27 m campus loop path, the estimated travel distance error was 0.82% in terms of UKF. The MPC parameters generated by RL also achieved a better tracking performance with 0.227 m RMSE in path tracking experiments, and they also achieved a better tracking performance when compared to that of human-tuned MPC parameters.

Coupled Lateral and Longitudinal Control for Trajectory Tracking, Lateral Stability, and Rollover Prevention of High-Speed Automated Vehicles Using Minimum-Time Model Predictive Control

2021 ◽  
Author(s):  
Shuping Chen ◽  
Huiyan Chen ◽  
Alex Pletta ◽  
Dan Negrut
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Speed ◽  
Minimum Time ◽  
Input State ◽  
Control Input ◽  
Vertical Force ◽  
Lateral Stability ◽  
Lateral Velocity ◽  
Rollover Prevention

Abstract Most controllers concerning lateral stability and rollover prevention for autonomous vehicles are designed separately and used simultaneously. However, roll motion influences lateral stability in cornering maneuvers, especially at high speed. Typical rollover prevention control stabilizes the vehicle with differential braking to create an understeering condition. Although this method can prevent rollover, it can also lead to deviation from a reference path specified for an autonomous vehicle. This contribution proposes and implements a coupled longitudinal and lateral controller for path tracking via model predictive control (MPC) to simultaneously enforce constraints on control input, state output, lateral stability, and rollover prevention. To demonstrate the approach in simulation, an 8 degrees of freedom (DOF) vehicle model is used as the MPC prediction model, and a high-fidelity 14-DOF model as the plant. The MPC-based lateral control generates a sequence of optimal steering angles, while a PID speed controller adjusts the driving or braking torque. The lateral stability envelope is determined by the phase plane of yaw rate and lateral velocity, while the roll angle threshold is derived from the load transfer ratio (LTR) and tire vertical force under the condition of quasi-steady-state rollover. To track the desired trajectory as fast as possible, a minimum-time velocity profile is determined using a forward-backward integration approach, subject to tire friction limit constraints. We demonstrate the approach in simulation, by having the vehicle track an arbitrary course of continuously varying curvature thus highlighting the accuracy of the controller and its ability to satisfy lateral and roll stability requirements. The MATLAB® code for the 8-DOF and 14-DOF vehicle models, along with the implementation of the proposed controller are available as open source in the public domain.

Low-Speed Vehicle Path-Tracking Algorithm Based On Model Predictive Control Using QPKWIK Solver

2021 ◽  
Author(s):  
Yihuai Zhang ◽  
Baijun Shi ◽  
Xizhi Hu ◽  
Wandong Ai
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Control Method ◽  
Tracking Error ◽  
Path Tracking ◽  
Tracking Algorithm ◽  
Tracking Performance ◽  
Low Speed ◽  
Path Tracking Algorithm

Abstract Automated valet parking is a part of autonomous vehicles. Path tracking is a vital capability of autonomous vehicles. In the scenario of automatic valet parking, the existing control algorithm will produce a high tracking error and a high computational burden. This paper proposes a path-tracking algorithm based on model predictive control to adapt to low-speed driving. By using the model predictive control method and vehicle kinematics model, a path tracking controller is designed. Combining the dual algorithm to further optimize the solver, a new QPKWIK solver is proposed. The simulation results show that the solution time of the QPKWIK solver is 25% less than that of the QP solver, and the tracking error is reduced by up to 41% compared with the QP solver. In the parking lot, the tracking performance is tested under four common scenarios, and the experimental results show that this controller has better tracking performance.

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