scholarly journals Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Kai Yang ◽  
Xiaolin Tang ◽  
Yechen Qin ◽  
Yanjun Huang ◽  
Hong Wang ◽  
...  
Keyword(s):  
Feedback Control ◽  
Model Predictive Control ◽  
Comparative Study ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
State Feedback ◽  
Maximum Velocity ◽  
State Feedback Control ◽  
Advantages And Disadvantages ◽  
Lane Change Test

AbstractA comparative study of model predictive control (MPC) schemes and robust $$H_{\infty }$$ H ∞ state feedback control (RSC) method for trajectory tracking is proposed in this paper. The main objective of this paper is to compare MPC and RSC controllers’ performance in tracking predefined trajectory under different scenarios. MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire, which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode. RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison. Then, three test cases are built in CarSim-Simulink joint platform. Specifically, the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions. Besides, the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability. Furthermore, an extreme curve test is built where the road adhesion changes suddenly, in order to test the performance of both controllers under extreme conditions. Finally, the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.

scholarly journals Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

2020 ◽  
Author(s):  
Kai Yang ◽  
Xiaolin TANG ◽  
Yechen Qin ◽  
Yanjun Huang ◽  
Hong Wang ◽  
...  
Keyword(s):  
Feedback Control ◽  
Model Predictive Control ◽  
Comparative Study ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
State Feedback ◽  
Maximum Velocity ◽  
State Feedback Control ◽  
Lane Change ◽  
Double Lane Change

Abstract A comparative study of model predictive control (MPC) schemes and robust A state feedback control (RSC) method for trajectory tracking, is proposed in this paper. Both MPC-based and RSC-based tracking controllers are designed on the basis of a 3-DOF vehicle model, including longitudinal, lateral and yaw motions. The main objective of this paper is to compare both controllers’ performance in tracking expected trajectory under different scenarios. Therefore, three cases, namely, verification test, double lane change test and curve test, were built in Carsim-Simulink joint platform. The simulation results indicate that MPC controller performed better in terms of accuracy and responding time under well driving conditions. However, in the test of double lane change manoeuvre where the road adhesion was set as 0.2, the maximum velocity RSC can execute was 14m/s, while that for MPC was 10m/s. In addition, in the curve test, the maximum velocity MPC can carry out was only 9m/s and that for RSC was 12m/s. In conclusion, RSC was robust and stable when the driving conditions was worse, while MPC was prone to be unstable.

scholarly journals Comparative Study of Trajectory Tracking Control for Automated Ground Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

2020 ◽  
Author(s):  
Kai Yang ◽  
Xiaolin TANG ◽  
Yechen Qin ◽  
Yanjun Huang ◽  
Hong Wang ◽  
...  
Keyword(s):  
Feedback Control ◽  
Comparative Study ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
State Feedback ◽  
State Feedback Control ◽  
Measurement Noise ◽  
Ground Vehicles ◽  
Vehicle Model ◽  
Sensor Measurement

Abstract A comparative study of longitudinal and lateral control maneuverer in model predictive control (MPC) schemes and robust state feedback control (RSC) method for trajectory tracking of automated ground vehicles (AGVs) is presented in this paper. Both MPC-based and RSC-based tracking controller are designed on the same basis of longitudinal-lateral-yaw motions of a single-track vehicle model. The main objective is to compare the controllers’ performance of tracking accuracy of path and velocity under different test scenarios. The simulation is implemented on Carsim-Simulink joint platform using high-fidelity vehicle model and the mass uncertainties, sensor measurement noise and the performance in extreme driving conditions: turn with big curvature are considered. The simulation results indicate that mass uncertainty and sensor measurement noise of lateral velocity have little effect on the RSC-based controller, while that have relatively great influence on MPC-based one. However, MPC-based controller shows a shorter response time and more accurate tracking performance than RSC-based scheme. Finally, for the test of turn with curvature 0.02 , the maximum velocity that RSC-based controller can carry out has reached 22m/s, which is slightly better than MPC-based one: 21m/s.

scholarly journals Designs of Feedback Controllers for Fluid Flows Based On Model Predictive Control and Regression Analysis

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1325 ◽  
Author(s):  
Yasuo Sasaki ◽  
Daisuke Tsubakino
Keyword(s):  
Regression Analysis ◽  
Feedback Control ◽  
Predictive Control ◽  
Vortex Shedding ◽  
Ridge Regression ◽  
Output Feedback ◽  
State Feedback ◽  
Output Feedback Control ◽  
State Feedback Control ◽  
Control Laws

Complexity of online computation is a drawback of model predictive control (MPC) when applied to the Navier–Stokes equations. To reduce the computational complexity, we propose a method to approximate the MPC with an explicit control law by using regression analysis. In this paper, we extracted two state-feedback control laws and two output-feedback control laws for flow around a cylinder as a benchmark. The state-feedback control laws that feed back different quantities to each other were extracted by ridge regression, and the two output-feedback control laws, whose measurement output is the surface pressure, were extracted by ridge regression and Gaussian process regression. In numerical simulations, the state-feedback control laws were able to suppress vortex shedding almost completely. While the output-feedback control laws could not suppress vortex shedding completely, they moderately improved the drag of the cylinder. Moreover, we confirmed that these control laws have some degree of robustness to the change in the Reynolds number. The computation times of the control input in all the extracted control laws were considerably shorter than that of the MPC.

Sign in / Sign up

Export Citation Format

Share Document