2020
DOI: 10.1109/access.2020.2980188
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An Improved Kinematic Model Predictive Control for High-Speed Path Tracking of Autonomous Vehicles

Abstract: Kinematic model predictive control (MPC) is well known for its simplicity and computational efficiency for path tracking of autonomous vehicles, however, it merely works well at low speed. In addition, earlier studies have demonstrated that tracking accuracy is improved by the feedback of yaw rate, as it improves the system transients. With this in mind, it is expected that the performance of path tracking can be improved by a cascaded controller that utilizes kinematic MPC to determine desired yaw rate rather… Show more

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Cited by 84 publications
(35 citation statements)
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“…In order to achieve the goal of expressway emergency collision avoidance, the collision avoidance path planning and path tracking control method based on the MPC are proposed [52][53][54][55]. Luqi Tang et al proposed a cascade control method, a MPC controller is designed based on the vehicle kinematics in upper layer to ensure the prediction accuracy and calculation efficiency and a PID controller is designed to track the upper layer control information [56]. Shaosong Li et al proposed the MPCbased vehicle stability control method in order to enhance the stability of the vehicle under dynamic limit conditions considering the change trend of tire force in the prediction [57][58][59][60][61].…”
Section: G Mpc Control Methodsmentioning
confidence: 99%
“…In order to achieve the goal of expressway emergency collision avoidance, the collision avoidance path planning and path tracking control method based on the MPC are proposed [52][53][54][55]. Luqi Tang et al proposed a cascade control method, a MPC controller is designed based on the vehicle kinematics in upper layer to ensure the prediction accuracy and calculation efficiency and a PID controller is designed to track the upper layer control information [56]. Shaosong Li et al proposed the MPCbased vehicle stability control method in order to enhance the stability of the vehicle under dynamic limit conditions considering the change trend of tire force in the prediction [57][58][59][60][61].…”
Section: G Mpc Control Methodsmentioning
confidence: 99%
“….λ q T . It should be noted that the effect of d(k) is taken in to account in (20) by the term X 1 k , defined in (11), which its components are calculated from (10). Remark 1.…”
Section: A the Nmpc Formulationmentioning
confidence: 99%
“…From reported work in [10], the adaptive safe experimentation dynamics (ASED) have shown to be the most effective trajectory-based optimization than other trajectorybased optimization tools due to less number of coefficients, memory-based framework (able to keep the best design parameter during the tuning process) and simple algorithm. On the other hand, there are many types of advanced PID have been proposed by many control researchers, such as PD-PID [11], fuzzy-PID [12,13], PD based fuzzy sliding mode control [14], fractional-PID [15], neural-network-PID (NNPID) [16,17], fractional-fuzzy-PID [18], kinematic model predictive control PID (K-MPC-PID) [19], integral separation PID (IPID) [20], variable structure PID [21] and also sigmoid-PID (SPID) [22,23] controllers. Based on the reported advanced PIDs, the SPID is reported to be effective since the proposed method uses variable PID coefficients depending on error signal behaviour according to the sigmoid function.…”
Section: Introductionmentioning
confidence: 99%