Improved LTVMPC design for steering control of autonomous vehicle

Velhal, Shridhar; Thomas, Susy

doi:10.1088/1742-6596/783/1/012028

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…Berntorp et al [18] put forward a nonlinear MPC scheme that adapts a tire model in response to the estimated road surface. Velhal et al [19] present an improved linear time varying MPC controller for automatic steering control of unmanned vehicles, which can execute trajectory following at high speed on the slippery roads. Du et al [20] design a controller that can simultaneously control the steering and speed of unmanned vehicles based on nonlinear MPC.…”

Section: B Autonomous Vehicle Based On Apf and Mpcmentioning

confidence: 99%

Combined Trajectory Planning and Tracking for Autonomous Vehicle Considering Driving Styles

Chu

et al. 2021

IEEE Access

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Autonomous driving is one of the promising technologies to tackle traffic accident and congestion problems nowadays. Even though an autonomous vehicle is operated without humans, it is necessary to reflect the driving characteristics of a human driver. This can increase user acceptance to autonomous driving system, which in turn will improve driving safety because of human occupants' trust in it. In this paper, a combined trajectory planning and tracking algorithm is proposed for the vehicle control. Firstly, traffic environments and driving styles are modeled with the Artificial Potential Field (APF) approach. Secondly, those APF values are integrated into the Model Predictive Control (MPC) design process, which can optimize the trajectories and control outputs. In this way, we add people's driving habits and styles into the controller, so that the controlled vehicle can move under the effects of the traffic environments and human's driving styles. At last, autonomous driving, which reflects two types of human drivers' driving styles (a cautious driving style and an aggressive one), is tested by the simulation experiments in two scenarios (carfollowing and lane-changing). Furthermore, the result demonstrates that the proposed algorithm can reflect driving styles. Accordingly, this novel controller can be utilized in the autonomous vehicle control field.

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Section: B Autonomous Vehicle Based On Apf and Mpcmentioning

confidence: 99%

Combined Trajectory Planning and Tracking for Autonomous Vehicle Considering Driving Styles

Chu

et al. 2021

IEEE Access

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“…Control for Intelligent Vehicles of an unmanned car. Velhal and Thomas [19] improved the linear time varying model for steering control of an autonomous vehicle. Although these researches have contributed to excellent insight and knowledge to the longitudinal and lateral control for autonomous vehicles, they limit themselves to separate the longitudinal control from lateral control; vice versa.…”

Section: Energy Dissipation Based Longitudinal and Lateral Couplingmentioning

confidence: 99%

Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

Zhang

et al. 2018

IEEE Intell. Transport. Syst. Mag.

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This paper proposes a combined longitudinal and lateral control approach for an intelligent vehicle system based on energy dissipation. The vehicle system dynamics resembles a series of mass/spring/damper systems that are dissipative, i.e., the energy of the system decays to zero eventually. Thus, the nonlinear-optimal longitudinal and lateral coupling control problem of the intelligent vehicle system is transformed into a dissipative control design based on an energy storage function. To satisfy the γ-performance, with respect to the quadratic supply rate, the storage function is developed by using a back-stepping based Lyapunov method and a step-by-step improvement of performance bounds. A dissipative feedback control law is formulated by successive approximation based on the step-by-step reduction of the value of γ. The results of the adaptive vehicle control simulations and test-bed experiments are provided and verified by the respective comparison of energy consumption on different values of γ and speed adaption under different road geometries.

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“…Once this method is used to simulate the driver to achieve trajectory following, the control goal is to eliminate the trajectory error. [11][12][13] Compared with real drivers, this method does not take into account the actual driver characteristics. Because the driver's control goal does not only include trajectory following accuracy, it also has the path error neglecting feature.…”

Section: Introductionmentioning

confidence: 99%

Shared control strategy based on trajectory following and driver intention optimization

Abi

Jin

Xiong

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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During the emergency braking process on the split-[Formula: see text] road, the lateral stability of the vehicle is poor, and the intervention of ABS will cause corresponding lateral disturbance. It is difficult for the driver to control the vehicle accurately. Especially at the end of the braking process, due to the withdrawal of ABS, the increase in braking pressure causes the longitudinal force of the tires on both sides to be inconsistent, which reduces the stability of the vehicle at this time. This paper proposed a shared control strategy to solve the related problems. First, a segmented active steering strategy is used in the driver’s intention optimization algorithm to optimize the driver’s actions in time at the initial stage of the braking process and to optimize the lateral stability of the vehicle by tracking the estimated tire slip angle at the end of the braking process. Then, according to the path envelope based on the driver’s path error neglecting feature and the dynamic state of the vehicle, a flexible control transfer mechanism is established. The trajectory following algorithm based on linear quadratic regulator is used to correct the driver’s intention optimization algorithm according to the flexible control transfer mechanism.

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Improved LTVMPC design for steering control of autonomous vehicle

Cited by 11 publications

References 5 publications

Combined Trajectory Planning and Tracking for Autonomous Vehicle Considering Driving Styles

Combined Trajectory Planning and Tracking for Autonomous Vehicle Considering Driving Styles

Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

Shared control strategy based on trajectory following and driver intention optimization

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