Adaptive-neural-network-based robust lateral motion control for autonomous vehicle at driving limits

Ji, Xuewu; He, Xiangkun; Lv, Chen; Liu, Yahui; Wu, Jian

doi:10.1016/j.conengprac.2018.04.007

Cited by 210 publications

(119 citation statements)

References 40 publications

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“…Compared with PID, the steering wheel under the control of LMI responds more quickly and has a larger angle where the path curvature varies greatly, the intervention of active steering adds more turning angles to ensure the stability of the vehicle. Furthermore, it can provide a more stable steering angle compared with the model by Ji et al, 24 which gives the driver a better road feel. Figure 7(c) and (d) is the comparison between the yaw velocity and the side slip angle data collected by the experiment under different control conditions.…”

Section: Hil Test Implementationmentioning

confidence: 98%

“…Many previous studies [15][16][17][18][19][20][21][22][23] have integrated the yaw torque control or brake control into AFS with different control methods to increase the stability of the vehicle. Ji et al 24,25 pay attention to the vehicle stability based on AFS mainly using adaptive neural networkbased method which gets a good result. Although the integrated control can make the vehicle more stable, it increases the complexity of the system and affects the execution efficiency of the system to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

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Research on vehicle active steering control based on linear matrix inequality and hardware in the loop test scheme design and implement for active steering

Diao

et al. 2019

Advances in Mechanical Engineering

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In the research process of automotive active steering control, due to the model uncertainty, road surface interference, sensor noise, and other influences, the control accuracy of the active steering system will be reduced, and the driver’s road sense will become worse. The traditional robust controller can solve the model uncertainty, pavement disturbance and sensor noise in the design process, but cannot consider the performance enough. Therefore, this article proposes an active steering control method based on linear matrix inequality. In this method, the model uncertainty, road interference, sensor noise, yaw velocity, and slip side angle tracking errors are all considered as constraint targets, respectively, so that the performance and robust stability of the active front steering system can be guaranteed. Finally, simulation and hardware in the loop experiment are implemented to verify the effect of active front steering system under the linear matrix inequality controller. The results show that the proposed control method can achieve better robust performance and robust stability.

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Section: Hil Test Implementationmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Research on vehicle active steering control based on linear matrix inequality and hardware in the loop test scheme design and implement for active steering

Diao

et al. 2019

Advances in Mechanical Engineering

Self Cite

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“…In this case, the output of the neural network is not just a reference point but a feasible trajectory. Ji et al [12] introduced an adaptive-neural-network-based lateral control for autonomous vehicles.…”

Section: Neural-network-based Algorithmsmentioning

confidence: 99%

Design of a Low-complexity Graph-Based Motion-Planning Algorithm for Autonomous Vehicles

2020

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In the development of autonomous vehicles, the design of real-time motion-planning is a crucial problem. The computation of the vehicle trajectory requires the consideration of safety, dynamic and comfort aspects. Moreover, the prediction of the vehicle motion in the surroundings and the real-time planning of the autonomous vehicle trajectory can be complex tasks. The goal of this paper is to present low-complexity motion-planning for overtaking scenarios in parallel traffic. The developed method is based on the generation of a graph, which contains feasible vehicle trajectories. The reduction of the complexity in the real-time computation is achieved through the reduction of the graph with clustering. In the motion-planning algorithm, the predicted motion of the surrounding vehicles is taken into consideration. The prediction algorithm is based on density functions of the surrounding vehicle motion, which are developed through real measurements. The resulted motion-planning algorithm is able to guarantee a safe and comfortable trajectory for the autonomous vehicle. The effectiveness of the method is illustrated through simulation examples using a high-fidelity vehicle dynamic simulator.

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“…Assuming that all localization information is available, the path tracking becomes a motion control problem of the vehicle, which mainly includes lateral control and longitudinal control [ 2 ]. The lateral control aims to track a planned trajectory [ 4 ] through steer-by-wire (SBW) system or differential braking system [ 5 , 6 , 7 , 8 , 9 ], while the longitudinal control is to achieve closed-loop velocity control through drive-by-wire (DBW) and brake-by-wire system (BBW) [ 10 ]. Considering that longitudinal control already has mature commercial applications, such as cruise control (CC) and adaptive cruise control (ACC) [ 10 , 11 ], this study will mainly focus on the lateral control strategies.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Lateral Control Scheme for Autonomous Vehicle with Uneven Time Delays Induced by Vision Sensors

Liu

et al. 2018

Sensors

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Vision-based sensors are widely used in lateral control of autonomous vehicles, but the large computational cost of the visual algorithms often induces uneven time delays. In this paper, a hierarchical vision-based lateral control scheme is proposed, where the upper controller is designed by robust H∞-based linear quadratic regulator (LQR) algorithm to compensate sensor-induced delays, and the lower controller is based on logic threshold method, in order to achieve strong convergence of the steering angle. Firstly, the vehicle lateral model is built, and the nonlinear uncertainties induced by time delays are linearized with Taylor expansion. Secondly, the state space of the system is augmented to describe such uncertainties with polytopic inclusions, which is controlled by an H∞-based LQR controller with a low cost of online computation. Then, a lower controller is designed for the control of the steering motor. According to the results of the vehicle experiment as well as the hardware-in-the-loop (HIL) experiment, the proposed control scheme shows good performance in vehicle’s lateral control task, and exhibits better robustness compared with a conventional LQR controller. The proposed control scheme provides a feasible solution for the lateral control of autonomous driving.

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Adaptive-neural-network-based robust lateral motion control for autonomous vehicle at driving limits

Cited by 210 publications

References 40 publications

Research on vehicle active steering control based on linear matrix inequality and hardware in the loop test scheme design and implement for active steering

Research on vehicle active steering control based on linear matrix inequality and hardware in the loop test scheme design and implement for active steering

Design of a Low-complexity Graph-Based Motion-Planning Algorithm for Autonomous Vehicles

Hierarchical Lateral Control Scheme for Autonomous Vehicle with Uneven Time Delays Induced by Vision Sensors

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