An optimal model-based trajectory following architecture synthesising the lateral adaptive preview strategy and longitudinal velocity planning for highly automated vehicle

Cao, Haotian; Song, Xiaolin; Zhao, Song; Bao, Shan; Huang, Zhi Feng

doi:10.1080/00423114.2017.1305114

Cited by 63 publications

(33 citation statements)

References 20 publications

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“…There has been substantial research on vehicle control. 4–8 Several motion control designs 1 applicable to autonomous vehicle are pure pursuit, rear wheel-based feedback, front wheel-based feedback, feedback linearization, control Lyapunov design, linear model predictive control (MPC), and nonlinear MPC. MPC has become an attractive approach to address state and control constraints systematically.…”

Section: Introductionmentioning

confidence: 99%

Path tracking framework synthesizing robust model predictive control and stability control for autonomous vehicle

Pei

Guo

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper proposes a framework for path tracking under additive disturbance when a vehicle travels at high speed or on low-friction road. A decoupling control strategy is adopted, which is made up of robust model predictive control and the stability control combining preview G-vectoring control and direct yaw moment control. A vehicle-road model is adopted for robust model predictive control, and a robust positively invariant set calculated online ensures state constraints in the presence of disturbances. Preview G-vectoring control in stability control generates deceleration and acceleration based on lateral jerk, later acceleration, and curvature at preview point when a vehicle travels through a cornering. Direct yaw moment control with additional activating conditions provides an external yaw moment to stabilize lateral motion and enhances tracking performance. A comparative analysis of stability performance of stability control is presented in simulations, and furthermore, many disturbances are considered, such as varying wind, road friction, and bounded state disturbances from motion planning and decision making. Simulation results show that the stability control combining preview G-vectoring control and direct yaw moment control with additional activating conditions not only guarantees lateral stability but also improves tracking performance, and robust model predictive control endows the overall control system with robustness.

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Section: Introductionmentioning

confidence: 99%

Path tracking framework synthesizing robust model predictive control and stability control for autonomous vehicle

Pei

Guo

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…Traffic jams, vehicle safety problems have become more and more prominent. In 2016, traffic accidents led to 63,093 deaths, 226,430 injuries and directly 1.21 billion CNY of property losses in China [1]. According to the World Health Organization report, 93% of traffic accidents were caused by human error operation [2].…”

Section: Introductionmentioning

confidence: 99%

Gain‐scheduling control of dynamic lateral lane change for automated and connected vehicles based on the multipoint preview

Nie

Wang

et al. 2020

IET intell. transp. syst

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“…Generally, velocity planning appears in the applications of longitudinal control, such as collision avoidance system 8,9 and maneuver turning at junction. 10 Cao et al 11 use the quadruplicate function to design a path following architecture with a velocity planning module. Furthermore, Cao et al 12 propose a path and velocity planning method by the combination of the natural cubic spline and the convex optimization technical.…”

Section: Introductionmentioning

confidence: 99%

Overtaking control strategy based on model predictive control with varying horizon for unmanned ground vehicle

Zhao

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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In this paper, a synthesized novel strategy of varying predictive horizon-based model predictive control is proposed for the overtaking control of unmanned ground vehicle. The whole control strategy includes path planning and path tracking. First, the preferred path in presence of diverse constraints of states, inputs, and collision avoidance can be calculated using Gauss pseudospectral method where expected position, velocity, and attitude are provided. Correspondingly, the continuous optimal control problem is converted to discrete nonlinear programming. Second, model predictive control is developed for tracking the optimized path. Considering the effect of the predictive horizon and the Gauss points’ distribution on tracking performance, the varying predictive horizon is introduced to improve the tracking accuracy in non-smooth path. By the varying predictive horizon-based model predictive control method, less computation burden and better control performance are achieved. For the difference between the mathematical expressions and the real unmanned ground vehicle dynamics, genetic algorithm is utilized to identify the parameters of tire model. Simulations in MATLAB and CarSim are both implemented to illustrate the effectiveness of the proposed method.

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An optimal model-based trajectory following architecture synthesising the lateral adaptive preview strategy and longitudinal velocity planning for highly automated vehicle

Cited by 63 publications

References 20 publications

Path tracking framework synthesizing robust model predictive control and stability control for autonomous vehicle

Path tracking framework synthesizing robust model predictive control and stability control for autonomous vehicle

Gain‐scheduling control of dynamic lateral lane change for automated and connected vehicles based on the multipoint preview

Overtaking control strategy based on model predictive control with varying horizon for unmanned ground vehicle

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