Trajectory Tracking of Autonomous Driving Vehicles via Output Feedback MPC

Zhou, Guichao; Tang, Xiaoming; Tang, Hao; Li, Wei

doi:10.1109/cac53003.2021.9728177

Cited by 1 publication

(3 citation statements)

References 3 publications

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“…Let 𝜉 = [𝜉 1 𝜉 2 𝜉 3 𝜉 4 ] 𝑇 = [𝜚 𝜚̇𝜚 𝜓 𝜚̇𝜓] 𝑇 , 𝜇 = 𝛿 𝑓 , substituting Equation ( 7) into Equation (6) and simplifying yield the control system equation for vehicle-trajectory tracking:…”

Section: The Trajectory Tracking Modelmentioning

confidence: 99%

“…y r . ψ r into Equation (6). According to the two degree of freedom vehicle-dynamical system model, the control objective of the vehicle-trajectory tracking is to make the deviation of the current vehicle from the desired trajectory as close to 0 as possible through the design of the appropriate Hamilton dissipative controller.…”

Section: Intelligent Vehicle Dynamic Control Systemmentioning

confidence: 99%

“…From the perspective of control algorithms, trajectory-tracking control technology needs to balance the accuracy and stability of tracking. Currently, common control methods include PID control, sliding mode controllers (SMC), model predictive control (MPC), linear quadratic regulator (LQR) control, and fuzzy control [ 5 , 6 ]. Specifically, the PID control method has a wide range of applications and has the advantages of a simple structure and a fast response speed.…”

Section: Introductionmentioning

confidence: 99%

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A Generalized Hamilton Robust Control Scheme of Trajectory Tracking for Intelligent Vehicles

Zhang,

Pei,

Zhang

et al. 2023

Sensors

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To ensure the accuracy and stability of intelligent-vehicle-trajectory tracking, a robust trajectory-tracking control strategy based on generalized Hamilton theory is proposed. Firstly, a dynamic Hamilton dissipative controller (DHDC) and trajectory-tracking Hamilton dissipative controller (TTHDC) were designed based on the established vehicle-dynamics control system and trajectory-tracking control system using the orthogonal decomposition method and control-switching method. Next, the feedback-dissipative Hamilton realizations of the two systems were obtained separately to ensure the convergence of the system. Secondly, based on the dissipative Hamilton system designed by TTHDC, a generalized Hamilton robust controller (GHRC) was designed. Finally, the co-simulation of Carsim and MATLAB/Simulink was used to verify the effectiveness of the three control algorithms. The simulation results show that DHDC and TTHDC can achieve self-stabilizing control of vehicles and enable certain control effects for the trajectory tracking of vehicles. The GHRC solves the problems of low tracking accuracy and poor stability of DHDC and TTHDC. Compared with the sliding mode controller (SMC) and linear quadratic regulator (LQR) controller, the GHRC can reduce the lateral error by 84.44% and the root mean square error (RMSE) by 83.92%, which effectively improves the accuracy and robustness of vehicle-trajectory tracking.

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Section: The Trajectory Tracking Modelmentioning

confidence: 99%

Section: Intelligent Vehicle Dynamic Control Systemmentioning

confidence: 99%