Vehicle Yaw Stability Control at the Handling Limits Based on Model Predictive Control

Li, Shaosong; Guo-dong, Wang; Zhang, Bangcheng; Yu, Zhanjiang; Cui, Gaojian

doi:10.1007/s12239-020-0034-7

Cited by 23 publications

(7 citation statements)

References 17 publications

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“…Luqi Tang et al proposed a cascade control method, a MPC controller is designed based on the vehicle kinematics in upper layer to ensure the prediction accuracy and calculation efficiency and a PID controller is designed to track the upper layer control information [56]. Shaosong Li et al proposed the MPCbased vehicle stability control method in order to enhance the stability of the vehicle under dynamic limit conditions considering the change trend of tire force in the prediction [57][58][59][60][61]. Hu Jiaming et al designed a controller based on MPC for autonomous tracked vehicle and introduced feedback correction to deal with modeling error and disturbance [62].…”

Section: G Mpc Control Methodsmentioning

confidence: 99%

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

123

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Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This paper introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

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Section: G Mpc Control Methodsmentioning

confidence: 99%

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

123

View full text Add to dashboard Cite

show abstract

“…The simplest method for determining reference values for yaw rate and sideslip angle involves the linearization of the steady-state equations of motion of a single-track vehicle model [21,[33][34][35][36][37][38][39][40][74][75][76][77]. This approach results in the reference yaw rate being expressed as a linear function of the input steering angle and thus reads:…”

Section: Reference Generatormentioning

confidence: 99%

“…Most of the times, the reference quantity for vehiclehandling improvement is the yaw rate. In the most straightforward approach, based on the tuning of the understeer gradient, the reference yaw rate is a linear function of the driver steering angle input [21,[33][34][35][36][37][38][39][40]. An evolution of this approach accounts for a variable desired understeer gradient [41], that is selected based on driver intention recognition when entering or exiting a curve.…”

Section: Introductionmentioning

confidence: 99%

On Torque Vectoring Control: Review and Comparison of State-of-the-Art Approaches

Asperti,

Vignati,

Sabbioni

2024

Machines

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Torque vectoring is a widely known technique to improve vehicle handling and to increase stability in limit conditions. With the advent of electric vehicles, this is becoming a key topic since it is possible to have distributed powertrains, i.e., multiple motors are adopted, in which each motor is controlled separately from the others. Moreover, electric motors deliver the torque required by the controller faster and more precisely than internal combustion engines, active differentials and conventional hydraulic brakes. The state of the art of Direct Yaw Moment Control (DYC) techniques, ranging from classical to modern control theories, are analyzed and discussed in this paper. The aim is to give an overview of the currently available approaches while identifying their drawbacks regarding performances and robustness when dealing with common issues like model uncertainties, external disturbances, friction limit and common state estimation problems. This contribution analyzes all the steps from the lateral dynamics reference generation to the desired control action computation and allocation to the available actuators. In addition, some of the presented control logic is evaluated in a simulation environment for a passenger car. Results of both open-loop and closed-loop maneuvers allow the comparison and clarification of each control strategy’s key advantages.

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“…Trajectory tracking is a fundamental function of autonomous vehicles that ensures that the vehicle travels along a preset path. Proportional integral derivative (PID) control, preview control theory, sliding mode control, deep learning, model predictive control (MPC), and other methods have provided numerous trajectory tracking strategies for autonomous vehicles [3][4][5][6][7][8]. MPC is widely used by researchers to address the trajectory tracking control problems for intelligent vehicles.…”

Section: Introductionmentioning

confidence: 99%

Fast Trajectory Tracking Control Algorithm for Autonomous Vehicles Based on the Alternating Direction Multiplier Method (ADMM) to the Receding Optimization of Model Predictive Control (MPC)

Dong,

Ye,

Luo

et al. 2023

Sensors

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In order to improve the real-time performance of the trajectory tracking of autonomous vehicles, this paper applies the alternating direction multiplier method (ADMM) to the receding optimization of model predictive control (MPC), which improves the computational speed of the algorithm. Based on the vehicle dynamics model, the output equation of the autonomous vehicle trajectory tracking control system is constructed, and the auxiliary variable and the dual variable are introduced. The quadratic programming problem transformed from the MPC and the vehicle dynamics constraints are rewritten into the solution of the ADMM form, and a decreasing penalty factor is used during the solution process. The simulation verification is carried out through the joint simulation platform of Simulink and Carsim. The results show that, compared with the active set method (ASM) and the interior point method (IPM), the algorithm proposed in this paper can not only improve the accuracy of trajectory tracking, but also exhibits good real-time performance in different prediction time domains and control time domains. When the prediction time domain increases, the calculation time shows no significant difference. This verifies the effectiveness of the ADMM in improving the real-time performance of MPC.

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Vehicle Yaw Stability Control at the Handling Limits Based on Model Predictive Control

Cited by 23 publications

References 17 publications

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

On Torque Vectoring Control: Review and Comparison of State-of-the-Art Approaches

Fast Trajectory Tracking Control Algorithm for Autonomous Vehicles Based on the Alternating Direction Multiplier Method (ADMM) to the Receding Optimization of Model Predictive Control (MPC)

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