Mixed local motion planning and tracking control framework for autonomous vehicles based on model predictive control

Yuan, Kun; Shu, Hong; Huang, Yanjun; Zhang, Yubiao; Khajepour, Amir; Zhang, Lin

doi:10.1049/iet-its.2018.5387

Cited by 23 publications

(12 citation statements)

References 32 publications

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“…The above system can be manipulated into a state-space form shown in (4). The state vector is defined as x = e y ⋅ Δψ ⋅ v y r T , the control input is u = f , the disturbance is defined as w = 0 ⋅ v x ρ0 ⋅ 0 T , and the output of the system is defined as…”

Section: System Modellingmentioning

confidence: 99%

“…Nam et al [17] considered the actuator dynamics in the prediction model of the path following controller. Yuan et al [4] proposed a mixed local motion planning and tracking framework of the autonomous vehicle based on MPC.…”

Section: Introductionmentioning

confidence: 99%

“…The development of autonomous driving requires various kinds of technologies involving multiple disciplines including engineering, computer science and mathematics [3, 4]. One of its crucial technologies is a path following control, referring to ensure the vehicle to track a predefined path by designing a proper control law.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐model adaptive predictive control for path following of autonomous vehicles

Liang

Khajepour

et al. 2020

IET intell. transp. syst

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The uncertainties in tire cornering stiffness can degrade the path following the performance of autonomous vehicles, especially in low adhesive conditions, to deal with this problem, a novel multi-model adaptive predictive control is proposed in this study. Firstly, a model predictive path following controller is designed based on a combined model of vehicle dynamics and road-related kinematics relationship. Then, to deal with the model uncertainties, the multiple model adaptive theory is introduced, and the recursive least adaptive law is proposed with its convergence proved by Lyapunov theory. Finally, the multiple-model adaptive law is combined with the proposed model predictive control by a convex polytope of tire cornering stiffness. In this way, the proposed algorithm can be adaptive to the uncertainties of tire cornering stiffness. Simulation results show the effectiveness and robustness of the proposed method to the uncertainties of the tire cornering stiffness resulting in an excellent performance in any road condition without introducing conservativeness.

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Section: System Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi‐model adaptive predictive control for path following of autonomous vehicles

Liang

Khajepour

et al. 2020

IET intell. transp. syst

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“…A hierarchical control scheme combing planning controller and path tracking controller is typically used in collision avoidance problems [30]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Model Predictive Control for Autonomous Collision Avoidance of Distributed Electric Drive Vehicle with Lateral Stability Analysis in Extreme Scenarios

Wang

Lin

Liang

et al. 2021

WEVJ

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This paper proposes an active collision avoidance controller based on a hierarchical model predictive control framework for distributed electric drive vehicles (4IDEV) considering extreme conditions. In this framework, a two-layer strategy is developed. The upper layer is the path replanning controller based on nonlinear MPC (nMPC), from which a collision-free path including the optimal lateral displacement and yaw angle can be obtained in real-time while encountering the obstacles. The lower layer is the path tracking controller based on hybrid MPC (hMPC), and the coordinated control inputs (yaw moment and the front wheel steering angle) are solved by a Mixed-Integer Quadratic Programming (MIQP) with the piecewise affine (PWA) tire model considering tire saturation region. Moreover, to improve the lateral stability when tracking, the stable zone of lateral stability in the high-risk condition is analyzed based on the phase portrait method, by which the constraints of vehicle states and inputs are derived. The verification is carried out on the MATLAB and CarSim co-simulation platform, and the simulation results show that the proposed active collision avoidance controller can track the reference path accurately and prevent vehicle instability in extreme scenarios.

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“…To address this problem, a lane‐keeping assistance system based on the fuzzy Takagi‐Sugeno (T‐S) optimal controller considering the change rate of the control action was proposed in [5]. A novel mixed motion planning and tracking (MPT) control framework for autonomous vehicles (AVs) based on model predictive control (MPC) was proposed in [6, 7]. The framework consisted of a longitudinal motion planning and tracking module and a lateral motion planning and tracking module.…”

Section: Introductionmentioning

confidence: 99%

Lane‐keeping system design considering driver's nervousness via scene analysis

Бин

Zhang

et al. 2020

IET intell. transp. syst

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Mixed local motion planning and tracking control framework for autonomous vehicles based on model predictive control

Cited by 23 publications

References 32 publications

Multi‐model adaptive predictive control for path following of autonomous vehicles

Multi‐model adaptive predictive control for path following of autonomous vehicles

Hierarchical Model Predictive Control for Autonomous Collision Avoidance of Distributed Electric Drive Vehicle with Lateral Stability Analysis in Extreme Scenarios

Lane‐keeping system design considering driver's nervousness via scene analysis

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