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.

scholarly journals Current Prediction-Based Three-Vector Voltage Optimization System for the Induction Motor with Current Static Error Correction

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Li Haixia ◽  
Lin Jican
Keyword(s):  
Steady State ◽  
Model Predictive Control ◽  
Induction Motor ◽  
Predictive Control ◽  
Control Method ◽  
Tracking Error ◽  
Current Control ◽  
Static Error ◽  
Control Stability ◽  
Steady State Performance

In the present study, the current control method of the model predictive control is applied to the field-oriented control induction motor. The augmentation model of the motor is initially established based on the stator current equation, which performs the current predictive control and formulates the new cost function by means of tracking error. Then, the influence of parameter error on the current control stability in the prediction model is analysed, and the current static error is corrected according to the correlation between the input and feedback. Finally, a simple and effective three-vector control strategy is proposed. Moreover, three adjacent basic voltage vectors are utilized, and then six candidate voltage vectors are synthesized in each sector to replace eight basic voltage vectors in the conventional model predictive control (MPC). The obtained results show that synthesized vectors, which have arbitrary amplitude and direction, significantly expand the coverage of the system’s control set, reduce the torque and flux pulsation in the conventional MPC, and improve the steady-state performance of the system. Finally, the dSPACE platform is employed to validate the performed experiment. It is concluded that the proposed method can reduce the torque and flux pulse, perform the induction motor current control, and improve the steady-state performance of the system.

scholarly journals A New Path Tracking Method Based on Multilayer Model Predictive Control

2019 ◽  
Vol 9 (13) ◽  
pp. 2649 ◽  
Author(s):  
Guoxing Bai ◽  
Yu Meng ◽  
Li Liu ◽  
Weidong Luo ◽  
Qing Gu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Control Method ◽  
Control Period ◽  
Path Tracking ◽  
Positioning Systems ◽  
The Real ◽  
Time Performance ◽  
Tracking Controllers

At present, many path tracking controllers are unable to actively adjust the longitudinal velocity according to path information, such as the radius of the curve, to further improve tracking accuracy. For this problem, we propose a new path tracking framework based on model predictive control (MPC). This is a multilayer control system that includes three path tracking controllers with fixed velocities and a velocity decision controller. This new control method is named multilayer MPC. This new control method is compared to other control methods through simulation. In this paper, the maximum values of the displacement error and the heading error of multilayer MPC are 92.92% and 77.02%, respectively, smaller than those of nonlinear MPC. The real-time performance of multilayer MPC is very good, and parallel computation can further improve the real-time performance of this control method. In simulation results, the calculation time of multilayer MPC in each control period does not exceed 0.0130 s, which is much smaller than the control period. In addition, when the error of positioning systems is at the centimeter level, the performance of multilayer MPC is still good.

scholarly journals Model predictive control of steering torque in shared driving of autonomous vehicles

2020 ◽  
Vol 103 (3) ◽  
pp. 003685042095013
Author(s):  
Chunjiang Bao ◽  
Jiwei Feng ◽  
Jian Wu ◽  
Shifu Liu ◽  
Guangfei Xu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Tracking Control ◽  
Control Method ◽  
Path Tracking ◽  
Hardware In The Loop ◽  
Control Loop ◽  
Vehicle Model ◽  
Automatic Driving ◽  
Steering Torque

The current path tracking control method is usually based on the steering wheel angle loop, which often makes the driver lose control of the automatic driving control loop. In order to involve the driver in the automatic driving control loop, and to solve the vehicle path tracking control problem with system robustness and model uncertainty, this paper puts forward a steering torque control method based on model predictive control algorithm. Based on the vehicle model, this method introduces the steering system model and the steering resistance torque model, and calculates the optimal control torque of the vehicle through the real-time vehicle status, so as to make up for the model mismatch, interference and other uncertainties, and ensure the real-time participation of the driver in the automatic driving control loop. To combine the nonlinear vehicle dynamics model with the steering column model, and to take the vehicle state parameters as the feedback variables of the model predictive controller model, then input the solution of the steering superposition control rate into the vehicle model, the design of the steering controller is realized. Finally, to carry out the simulation of lane keeping based on CarSim software and Simulink control model, and the hardware in-the-loop test on the hardware in-the-loop experimental platform of CarSim/LabVIEW-RT. The simulation and test results indicate that the designed torque loop path tracking control method based on model predictive control can help the driver track the target path better.

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.

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