Parallel parking of a car‐like mobile robot based on the P‐domain path tracking controllers

Samani, Nasrin Naderi; Danesh, Mohammad; Ghaisari, Jafar

doi:10.1049/iet-cta.2015.1064

Cited by 19 publications

(3 citation statements)

References 19 publications

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“…Little research has been reported to follow the trajectory point considering time delay. Commonly used control methods include pure pursuit (PP), 10 proportion integral differential (PID), 11 sliding mode control (SMC), 12 and feedback plus feedforward controllers. 13 PP is simple in theory, but the results are sensitive to look-ahead distance.…”

Section: Introductionmentioning

confidence: 99%

Data-driven trajectory-tracking in automated parking system via iterative learning compensation and model predictive control

Song

Zhang

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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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, which combined model predictive control (MPC) and iterative learning control (ILC), was proposed to improve the spatiotemporal tracking accuracy of the autonomous vehicle. ILC was utilized for longitudinal compensation using the error signal between historical and expected speed. Accordingly, the error model in the longitudinal direction was simplified to decrease the number of decision variables in MPC. Simulation experiments using CarSim were carried out to compare the proposed method with open-loop control, linear quadratic regulator (LQR), and pure MPC that had a similar computing time with ILC-MPC. ILC-MPC converged in a few iterations of the learning process and achieved the highest tracking accuracy in spatiotemporal domain among the mentioned methods.

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Section: Introductionmentioning

confidence: 99%

Data-driven trajectory-tracking in automated parking system via iterative learning compensation and model predictive control

Song

Zhang

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…In general, car-like robots typically travel at low speeds, but track reference paths that have large curvature and dramatic curvature changes. Therefore, some researchers, such as Naderi Samani et al, are interested in the path tracking of car-like robots and have published some papers on this topic [2][3][4][5][6][7][8][9][10][11][12]. In 2016, Naderi Samani et al designed controllers for the parallel parking path tracking of car-like robots based on feedback linearization control and sliding mode control, respectively [2].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, some researchers, such as Naderi Samani et al, are interested in the path tracking of car-like robots and have published some papers on this topic [2][3][4][5][6][7][8][9][10][11][12]. In 2016, Naderi Samani et al designed controllers for the parallel parking path tracking of car-like robots based on feedback linearization control and sliding mode control, respectively [2]. Hwang proposed a path tracking model and algorithm for car-like robots based on hierarchical variable structure control [3].…”

Section: Introductionmentioning

confidence: 99%

Path Tracking for Car-like Robots Based on Neural Networks with NMPC as Learning Samples

et al. 2022

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In the field of path tracking for car-like robots, although nonlinear model predictive control (NMPC) can handle the system constraints well, its real-time performance is poor. To solve this problem, a neural network control method with NMPC as the learning sample is proposed. The design process of this control method includes establishing the NMPC controller based on the time-varying local model, generating learning samples based on this NMPC controller, and training to obtain the neural network controller. The proposed controller is tested by a joint simulation of MATLAB and Carsim and compared with other controllers. According to the simulation results, the accuracy of the NN controller is close to that of the NMPC controller and far better than that of the Stanley controller. In all simulations, the absolute value of displacement error of the NN controller does not exceed 0.2854 m, and the absolute value of heading error does not exceed 0.2279 rad. In addition, the real-time performance of the NN controller is better than that of the NMPC controller. The maximum time cost and average time cost of the NN controller are, respectively, 40.91% and 22.37% smaller than those of the NMPC controller under the same conditions.

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A novel adaptive pseudospectral method for the optimal control problem of automatic car parking

Chen

Mai

Zhang

et al. 2021

Asian Journal of Control

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This paper proposes a novel adaptive pseudospectral (NAP) method for solving the time-energy optimal control model to minimize the parking time and the energy output of the vehicle. The time-energy optimal control model is first discretized into a nonlinear programming problem at Gauss collocations by the NAP method. Subsequently, we prove the equivalence between the nonlinear programming problem derived from the NAP method and the time-energy optimal control model problem. Compared to those of the interior point method and the piecewise gauss pseudospectal method, simulation results show that the NAP method has higher computational efficiency and accuracy in solving the time-energy optimal control model problem.

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Parallel parking of a car‐like mobile robot based on the P‐domain path tracking controllers

Cited by 19 publications

References 19 publications

Data-driven trajectory-tracking in automated parking system via iterative learning compensation and model predictive control

Data-driven trajectory-tracking in automated parking system via iterative learning compensation and model predictive control

Path Tracking for Car-like Robots Based on Neural Networks with NMPC as Learning Samples

A novel adaptive pseudospectral method for the optimal control problem of automatic car parking

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