Evaluating Model Predictive Path Following and Yaw Stability Controllers for Over-Actuated Autonomous Electric Vehicles

Zhang, Wenliang; Wang, Zhenpo; Drugge, Lars; Nybacka, Mikael

doi:10.1109/tvt.2020.3030863

Cited by 48 publications

(24 citation statements)

References 27 publications

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“…Yaw rate signal and sideslip angle signal are generally used to evaluate the stability of vehicles during steering [35,36]. Among them, the yaw rate is determined by the yaw moment of the force on the tire to the centre of mass, and the resultant force determines the sideslip angle on the tire.…”

Section: Multi-model Predictive Stability Control Strategy Based On C...mentioning

confidence: 99%

Multi-model Predictive Stability Control Strategy for Vehicle Four-wheel Steering System Considering Tire Nonlinear Cornering Characteristics

Luan

Zhao

Wang

2022

Preprint

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In extreme working conditions such as poor road conditions, the low road adhesion coefficient is easy to cause the tire cornering characteristics curve to be in the nonlinear domain, making the vehicle system in the critical instability state. To this end, this paper proposes a cascade deep learning framework combining multi-model predictive control (MMPC) and LSTM tire cornering stiffness estimation (TCSE) neural network and designs a stability control strategy of vehicle four-wheel steering system considering tire nonlinear cornering characteristics. The four-wheel steering system and vehicle tire dynamic model are analyzed and established, and the online estimation method of tire cornering stiffness and MMPC's sub-model classification method is developed. On this basis, the tire angle is creatively introduced as the phase plane stability region boundary, used to design the MMPC controller's boundary condition. The hardware in the loop test results shows that compared with the existing research, the strategy proposed in this paper can effectively improve the tracking accuracy of the target steering signal and ensure the system's stability when the road adhesion coefficient is low.

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Section: Multi-model Predictive Stability Control Strategy Based On C...mentioning

confidence: 99%

Multi-model Predictive Stability Control Strategy for Vehicle Four-wheel Steering System Considering Tire Nonlinear Cornering Characteristics

Luan

Zhao

Wang

2022

Preprint

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“…In [71], an MPCbased motion control method is proposed, in which the appropriate MPC cost function weights can be automatically determined through a logistic function. In [72], MPC-based controllers for driving autonomous vehicles in dangerous situations, as for example lane changes in order to avoid an obstacle, are proposed and evaluated. In [73], an MPC-based simultaneous trajectory planning and tracking approach for obstacle avoidance of an intelligent vehicle is proposed, where the reference trajectory is determined by both the lateral position and velocity of the intelligent vehicle and the velocity and yaw angle of the obstacle vehicle.…”

Section: Motion Control Of Single Agvs Using Mpcmentioning

confidence: 99%

Model predictive control for autonomous ground vehicles: a review

Hirche

Huang

et al. 2021

Auton. Intell. Syst.

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This paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

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“…e inverse-based calculation of the control distribution can be performed based on predefined rules or optimization. e optimization-based methods have more degrees of freedom in designing [30], so this method was used in the presented work to include more effective safety and stability issues. e optimization task will be detailed later.…”

Section: Imc-based Force-allocation Controlmentioning

confidence: 99%

Integrated Lateral and Longitudinal Control with Optimization-Based Allocation Strategy for Autonomous Electric Vehicles

Kovács

Vajk

2021

Journal of Advanced Transportation

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This paper presents a novel approach for path-following control of a four-wheeled autonomous vehicle. The rear wheels of the vehicle are driven independently, all four wheels can be braked independently, and the front wheels are steered together. The proposed cascade structure consists of two convex optimization-based parts: one for path-following and another for the control allocation problem of the actuators. The control algorithm presents cost functions for the allocation problem focusing on safety. The proposed cost functions were examined and compared to former ones in a simulation environment. After all, the controller was tested in real-time test on a Lotus Evora test vehicle developed by ThyssenKrupp.

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Evaluating Model Predictive Path Following and Yaw Stability Controllers for Over-Actuated Autonomous Electric Vehicles

Cited by 48 publications

References 27 publications

Multi-model Predictive Stability Control Strategy for Vehicle Four-wheel Steering System Considering Tire Nonlinear Cornering Characteristics

Multi-model Predictive Stability Control Strategy for Vehicle Four-wheel Steering System Considering Tire Nonlinear Cornering Characteristics

Model predictive control for autonomous ground vehicles: a review

Integrated Lateral and Longitudinal Control with Optimization-Based Allocation Strategy for Autonomous Electric Vehicles

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