2022
DOI: 10.1177/09544070221081299
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Data-driven trajectory-tracking in automated parking system via iterative learning compensation and model predictive control

Abstract: Automated parking system (APS) that explicitly considers the time efficiency of the motion has received large amounts of attention in recent years. Trajectory planning module in these APS delivered parking trajectory, which was expected to be precisely tracked by tracking module. However, the reference points of frequently used trackers were selected in the spatial domain, resulting in significant trajectory tracking errors with temporal information. In this paper, a tracking control method called ILC-MPC, whi… Show more

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Cited by 3 publications
(1 citation statement)
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“…For example, Qiu et al designed an MPC-based autonomous parallel parking trajectory tracking algorithm for solving the problem of parallel parking in a narrow space, and the simulation results showed that the method could make the vehicle park safely, quickly, and accurately in the parking space [42].Wang designed two MPCand PP-based vertical parking trajectory tracking controllers with automatic reverse parking charging as the application scenario, and combined with PI-based The simulation results proved the feasibility and stability of the controller [43]. Song et al proposed a tracking control method combining MPC and Iterative learning control (ILC), and the simulation results proved that the method has higher tracking accuracy than openloop control, linear quadratic regulator (LQR) and traditional MPC The simulation results proved that this method has higher tracking accuracy than open-loop control, linear quadratic regulator (LQR) and traditional MPC algorithm [44].…”
Section: Pid Algorithmmentioning
confidence: 99%
“…For example, Qiu et al designed an MPC-based autonomous parallel parking trajectory tracking algorithm for solving the problem of parallel parking in a narrow space, and the simulation results showed that the method could make the vehicle park safely, quickly, and accurately in the parking space [42].Wang designed two MPCand PP-based vertical parking trajectory tracking controllers with automatic reverse parking charging as the application scenario, and combined with PI-based The simulation results proved the feasibility and stability of the controller [43]. Song et al proposed a tracking control method combining MPC and Iterative learning control (ILC), and the simulation results proved that the method has higher tracking accuracy than openloop control, linear quadratic regulator (LQR) and traditional MPC The simulation results proved that this method has higher tracking accuracy than open-loop control, linear quadratic regulator (LQR) and traditional MPC algorithm [44].…”
Section: Pid Algorithmmentioning
confidence: 99%