Model predictive longitudinal control for autonomous electric vehicles with tracking differentiator

Wang, Yijing; Cao, Shizhuo; Yang, Hongjiu; Zuo, Zhiqiang; Wang, Li; Luo, Xiaoyuan

doi:10.1080/00207721.2021.1892236

Cited by 7 publications

(1 citation statement)

References 37 publications

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“…However, the issue of jitter generated by the sliding mode control algorithm in proximity to the sliding surface remains a challenge. A model predictive controller with a tracking differentiator for longitudinal speed tracking of electric intelligent vehicles was proposed and its performance was compared to a traditional model predictive controller (Wang et al, 2021a). On a similar note, Coskun et al (2021) addressed the adaptive cruise control problem by designing controllers based on MPC and Kalman filtering strategies.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal and lateral stability control for autonomous vehicles in curved road scenarios with road undulation

Guo,

Liu,

Shang

et al. 2023

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PurposeThis research aims to present a novel cooperative control architecture designed specifically for roads with variations in height and curvature. The primary objective is to enhance the longitudinal and lateral tracking accuracy of the vehicle.Design/methodology/approachIn addressing the challenges posed by time-varying road information and vehicle dynamics parameters, a combination of model predictive control (MPC) and active disturbance rejection control (ADRC) is employed in this study. A coupled controller based on the authors’ model was developed by utilizing the capabilities of MPC and ADRC. Emphasis is placed on the ramifications of road undulations and changes in curvature concerning control effectiveness. Recognizing these factors as disturbances, measures are taken to offset their influences within the system. Load transfer due to variations in road parameters has been considered and integrated into the design of the authors’ synergistic architecture.FindingsThe framework's efficacy is validated through hardware-in-the-loop simulation. Experimental results show that the integrated controller is more robust than conventional MPC and PID controllers. Consequently, the integrated controller improves the vehicle's driving stability and safety.Originality/valueThe proposed coupled control strategy notably enhances vehicle stability and reduces slip concerns. A tailored model is introduced integrating a control strategy based on MPC and ADRC which takes into account vertical and longitudinal force variations and allowing it to effectively cope with complex scenarios and multifaceted constraints problems.

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Section: Introductionmentioning

confidence: 99%