Dual-envelop-oriented moving horizon path tracking control for fully automated vehicles

Guo, Hongyan; Li, Jun; Cao, Dongpu; Chen, Hong; Yu, Ru; Lv, Chen

doi:10.1016/j.mechatronics.2017.02.001

Cited by 88 publications

(47 citation statements)

References 37 publications

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“…The linear single-track model is employed here to describe the dynamics of an intelligent vehicle and the parameters can be obtained in [6]. When the intelligent vehicle runs into the nonlinear region, the modelling error will become large.…”

Section: A Vehicle Modelmentioning

confidence: 99%

“…where ω p is a small positive number selected to avoid colliding with the road boundaries. The relationship among the vehicle lateral position, front end F and rear end R can be described as [6]…”

Section: Parallel Steering Controller Design a Parallel Sharedmentioning

confidence: 99%

“…The difference between the scheme in [6] and the approach in this manuscript is that the control issue is differentiated from path following and parallel steering control. Despite MPC control approach is used in these two control issues, the cost functions are selected different and the safety constraints are additional considered in the proposed scheme here.…”

Section: ) Actuator Saturation Formulationmentioning

confidence: 99%

“…W ITH the rapid development of parallel control and management theory and its numerous applications in transport automation and vehicle intelligence over the past decade [1], [2], parallel steering control, which is an aspect of parallel driving, has been steadily developed and applied in practice [3], [4]. Moreover, furthered by emerging developments in connected and automated vehicles [5], [6], parallel steering control has become a hot topic and has been garnering increased attention from both academic and industrial researchers [7].…”

Section: Introductionmentioning

confidence: 99%

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Hazard-evaluation-oriented moving horizon parallel steering control for driver-automation collaboration during automated driving

Guo

Song

et al. 2018

IEEE/CAA J. Autom. Sinica

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Prompted by emerging developments in connected and automated vehicles, parallel steering control, one aspect of parallel driving, has become highly important for intelligent vehicles for easing the burden on and ensuring the safety of human drivers. This paper presents a parallel steering control framework for an intelligent vehicle using moving horizon optimization. The framework considers lateral stability, collision avoidance and actuator saturation and describes them as constraints, which can blend the operation of a human driver and a parallel steering controller effectively. Moreover, the road hazard and the steering operation error are employed to evaluate the operational hazardous of an intelligent vehicle. Under the hazard evaluation, the intelligent vehicle will be mainly operated by the human driver when the vehicle operates in a safe and stable manner. The automated steering driving objective will play an active role and regulate the steering operations of the intelligent vehicle based on the hazard evaluation. To verify the effectiveness of the proposed hazard-evaluation-oriented moving horizon parallel steering control approach, various validations are conducted, and the results are compared with a parallel steering scheme that does not consider automated driving situations. The results illustrate that the proposed parallel steering controller achieves acceptable performance under both conventional conditions and hazardous conditions.

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Section: A Vehicle Modelmentioning

confidence: 99%

Section: Parallel Steering Controller Design a Parallel Sharedmentioning

confidence: 99%

Section: ) Actuator Saturation Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hazard-evaluation-oriented moving horizon parallel steering control for driver-automation collaboration during automated driving

Guo

Song

et al. 2018

IEEE/CAA J. Autom. Sinica

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“…In recent years, autonomous vehicles have become a hotspot in the field of vehicle engineering and a new driving force for the growth of the automotive industry. Autonomous vehicle is a comprehensive system which integrates environmental perception, 2,3 planning and decision-making, 4,5 motion control, 6,7 human physiology monitoring, 8,9 and other functions.…”

Section: Introductionmentioning

confidence: 99%

Path tracking of autonomous vehicle based on adaptive model predictive control

Lin

Chen

Zhao

et al. 2019

International Journal of Advanced Robotic Systems

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In most cases, a vehicle works in a complex environment, with working conditions changing frequently. For most model predictive tracking controllers, however, the impacts of some important working conditions, such as speed and road conditions, are not concerned. In this regard, an adaptive model predictive controller is proposed, which improves tracking accuracy and stability compared with general model predictive controllers. First, the proposed controller utilizes the recursive least square algorithm to estimate tire cornering stiffness and road friction coefficient online. Then, the estimated tire cornering stiffness is used to update vehicle dynamics model and the estimated road friction coefficient is used to update the road adhesion constraint. Moreover, the control parameters consist of prediction horizon, control horizon, and sampling time, all of which are set according to vehicle speed. A co-simulation based on MATLAB/Simulink and CarSim is conducted. The simulation results illustrate that the proposed controller has a great adaptive ability to changing working conditions, especially to speed and road conditions.

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Set‐Membership Filtering Based Model Predictive Control for Trajectory Tracking of Automated Guided Vehicles

Liang,

Hu,

Zhang

et al. 2024

Adaptive Control & Signal

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This article considers the trajectory tracking problem of automated guided vehicles with unknown‐but‐bounded noises and proposes a set‐membership filtering (SMF) based model predictive control strategy. A 2‐DOF vehicle model is first established with considering the unknown‐but‐bounded process and measurement noises. Then, a trajectory tracking control scheme is proposed which consists of two parts, that is, a set‐membership observer and a model predictive controller. The proposed set‐membership observer is able to deal with the unknown‐but‐bounded noises and obtain the state estimation ellipsoid for the considered automated guided vehicle. Further, the designed model predictive controller utilizes the estimated ellipsoid to compute the control sequence. Finally, simulation results demonstrate the effectiveness and superiority of the proposed method.

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Dual-envelop-oriented moving horizon path tracking control for fully automated vehicles

Cited by 88 publications

References 37 publications

Hazard-evaluation-oriented moving horizon parallel steering control for driver-automation collaboration during automated driving

Hazard-evaluation-oriented moving horizon parallel steering control for driver-automation collaboration during automated driving

Path tracking of autonomous vehicle based on adaptive model predictive control

Set‐Membership Filtering Based Model Predictive Control for Trajectory Tracking of Automated Guided Vehicles

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