Vehicle yaw stability control with a two-layered learning MPC

Zhang, Zhiming; Xie, Lei; Lu, Shan; Wu, Xialai; Su, Hongye

doi:10.1080/00423114.2022.2052328

Cited by 15 publications

(9 citation statements)

References 27 publications

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“…Zhang et al develop a Gaussian process-based MPC with a classical two-layer structure to enhance stability control. More importantly, the corresponding stability proof is also conducted [193]. Zhu et al present a novel trajectory tracking control approach based on MPC to avoid high-frequency oscillations automatically by the switching algorithm.…”

Section: Feedback Control Algorithms With Predictionmentioning

confidence: 99%

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Liu,

Hua,

Deng

et al. 2023

IEEE Internet Things J.

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Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and indepth look at the state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.

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Section: Feedback Control Algorithms With Predictionmentioning

confidence: 99%

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Liu,

Hua,

Deng

et al. 2023

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Yaw stability control is of important significance for ensuring vehicle safety [5]. To promote the rapid development of four-wheel-drive vehicles, many scholars have carried out related research on improving their yaw stability, such as PID control [6,7], sliding mode control [8,9], adaptive control [10,11], neural network control [12,13], model predictive control (MPC) [14,15], and other methods. Among them, the MPC can make local optimization adjustments at each time step and obtain optimal control input.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al [21] constructed a coordinated yaw and roll motion controller based on data-driven multi-MPC in the upperlayer controller, and the lower-layer controller combined the tire slip rate and the vertical load transfer amount and used the quadratic programming algorithm to transform the fusion expected yaw moment into the optimal drive torque of each wheel. Zhang et al [15] designed the upper supervisor controller to calculate the compensation torque required when the vehicle turns and used Gaussian process regression to reduce the error. The lower-layer controller distributes the torque to the wheels in the form of braking torque.…”

Section: Introductionmentioning

confidence: 99%

Yaw Stability Control of Unmanned Emergency Supplies Transportation Vehicle Considering Two-Layer Model Predictive Control

Tang,

Zhang,

Wang

et al. 2024

Actuators

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The transportation of emergency supplies is characterized by real-time, urgent, and non-contact, which constitute the basic guarantee for emergency rescue and disposal. To improve the yaw stability of the four-wheel-drive unmanned emergency supplies transportation vehicle (ESTV) during operation, a two-layer model predictive controller (MPC) method based on a Kalman filter is proposed in this paper. Firstly, the dynamics model of the ESTV is established. Secondly, the improved Sage–Husa adaptive extended Kalman filter (SHAEKF) is used to decrease the impact of noise on the ESTV system. Thirdly, a two-layer MPC is designed for the yaw stability control of the ESTV. The upper-layer controller solves the yaw moment and the front wheel steering angle of the ESTV. The lower-layer controller optimizes the torque distribution of the four tires of the ESTV to ensure the self-stabilization of the ESTV operation. Finally, analysis and verification are carried out. The simulation results have verified that the improved SHAEKF can decrease the state estimation error by more than 78% and achieve the noise reduction of the ESTV state. Under extreme conditions of high velocity and low adhesion, the average relative error is within 6.77%. The proposed control method can effectively prevent the instability of the ESTV and maintain good yaw stability.

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“…The research on hierarchical DYC mainly concentrates on two aspects: (1) how to determine if the vehicle is unstable in the upper layer; (2) how to accurately allocate the desired torque to four driving motors in the lower layer [11]. For the upper layer, there are currently two main methods to determine if the vehicle is unstable.…”

Section: Introductionmentioning

confidence: 99%

Direct Yaw Moment Control for Distributed Drive Electric Vehicles Based on Hierarchical Optimization Control Framework

Hu,

Zhang,

Zhang

et al. 2024

Mathematics

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Direct yaw moment control (DYC) can effectively improve the yaw stability of four-wheel distributed drive electric vehicles (4W-DDEVs) under extreme conditions, which has become an indispensable part of active safety control for 4W-DDEVs. This study proposes a novel hierarchical DYC architecture for 4W-DDEVs to enhance vehicle stability during ever-changing road conditions. Firstly, a vehicle dynamics model is established, including a two-degree-of-freedom (2DOF) vehicle model for calculating the desired yaw rate and sideslip angle as the control target of the upper layer controller, a DDEV model composed of a seven-degree-of-freedom (7DOF) vehicle model, a tire model, a motor model and a driver model. Secondly, a hierarchical DYC is designed combining the upper layer yaw moment calculation and low layer torque distribution. Specifically, based on Matlab/Simulink, improved linear quadratic regulator (LQR) with weight matrix optimization based on inertia weight cosine-adjustment particle swarm optimization (IWCPSO) is employed to compute the required additional yaw moment in the upper-layer controller, while quadratic programming (QP) is used to allocate four motors’ torque with the optimization objective of minimizing the tire utilization rate. Finally, a comparative test with double-lane-change and sinusoidal conditions under a low and high adhesion road surface is conducted on Carsim and Matlab/Simulink joint simulation platform. With IWCPSO-LQR under double-lane-change (DLC) condition on a low adhesion road surface, the yaw rate and sideslip angle of the DDEV exhibits improvements of 95.2%, 96.8% in the integral sum of errors, 94.9%, 95.1% in the root mean squared error, and 78.8%, 98.5% in the peak value compared to those without control. Simulation results indicate the proposed hierarchical control method has a remarkable control effect on the yaw rate and sideslip angle, which effectively strengthens the driving stability of 4W-DDEVs.

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Vehicle yaw stability control with a two-layered learning MPC

Cited by 15 publications

References 27 publications

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Yaw Stability Control of Unmanned Emergency Supplies Transportation Vehicle Considering Two-Layer Model Predictive Control

Direct Yaw Moment Control for Distributed Drive Electric Vehicles Based on Hierarchical Optimization Control Framework

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