Linear model predictive control of automatic parking path tracking with soft constraints

Hao, Yue; Jiang, Haobin; Ma, Shidian; Tang, Bin; Wahab, Lukuman

doi:10.1177/1729881419852201

Cited by 48 publications

(24 citation statements)

References 31 publications

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“…At present, constrained control strategy has been studied by some scholars [14][15][16][17][18][19][20][21]. A path tracking control strategy based on linear model predictive control with softening constraints is proposed in [14]. In the optimization process, a relaxation factor is added, and the quadratic programming method is used to calculate the control increment of each cycle.…”

Section: Introductionmentioning

confidence: 99%

Barrier Lyapunov Function-Based Adaptive Back-Stepping Control for Electronic Throttle Control System

Wang

Liu

et al. 2021

Mathematics

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This paper presents an adaptive constraint control approach for Electronic Throttle Control System (ETCS) with asymmetric throttle angle constraints. The adaptive constraint control method, which is based on barrier Lyapunov function (BLF), is designed not only to track the desired throttle angle but also to guarantee no violation on the throttle angle constraints. An ETC mathematic model with complex non-linear system is considered and the asymmetric barrier Lyapunov function (ABLF) is introduced into the design of the controller. Based on Lyapunov stability theory, it can be concluded that the proposed controller can guarantee the stability of the whole system and uniformly converge the state error to track the desired throttle angle. The results of simulations show that the proposed controller can ensure that there is no violation on the throttle angle constraints.

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Section: Introductionmentioning

confidence: 99%

Barrier Lyapunov Function-Based Adaptive Back-Stepping Control for Electronic Throttle Control System

Wang

Liu

et al. 2021

Mathematics

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“…The deviation model of the path and the reference path, the Kalman filter is used to reduce the measurement noise in the vehicle position and heading angle measurement data, and the simulation model is established through Matlab/Simulink to verify the effectiveness of the proposed algorithm. Hao Ye, et al [15] proposed a linear model predictive control path tracking control strategy with softening constraints on how to improve the accuracy of automatic parking path tracking control. Establish a linear time-varying prediction model of the vehicle, predict the future state of the vehicle, add a relaxation factor in the optimization process, calculate the control increment of each cycle through quadratic programming, roll optimization and feedback correction, and correct various deviations in the control process in time.…”

Section: Introductionmentioning

confidence: 99%

Automatic Parking Path Planning Based on Ant Colony Optimization and the Grid Method

Han

2021

Journal of Sensors

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This paper analyzes the path planning problem in the automatic parking process, and studies a path planning method for automatic parking. The grid method and the ant colony optimization are combined to find the shortest path from the parking start point to the end point. The grid method is used to model the parking environment to simulate the actual parking space of automatic parking; then this paper makes some improvements to the basic ant colony optimization, finds the destination by setting the ants’ movement rules in the grid, and finds the shortest path after N iterations; since the optimal path found is a polyline, it will increase the difficulty of controlling vehicle path tracking and affect the accuracy of vehicle path tracking. The bezier curve is used to generate a smooth path suitable for vehicle walking. Finally, through matlab simulation, the obstacles in the environment are simulated, and the parking trajectory is obtained. The results show that the path planning method proposed in this paper is feasible.

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“…In recent years, the research results of path tracking control based on LMPC have not paid enough attention to this issue [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. However, in the path tracking control based on feedforward-feedback control, the idea of introducing feedforward information through the preview to improve the control accuracy appears.…”

Section: Introductionmentioning

confidence: 99%

Efficient Path Tracking Control for Autonomous Driving of Tracked Emergency Rescue Robot under 6G Network

Bai

Meng

et al. 2021

Wireless Communications and Mobile Computing

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This paper proposes a path tracking control algorithm of tracked mobile robots based on Preview Linear Model Predictive Control (MPC), which is used to achieve autonomous driving in the unstructured environment under an emergency rescue scenario. It is the future trend to realize the communication and control of rescue equipment with 6G and edge cloud cooperation. In this framework, linear MPC (LMPC) is suitable for the path tracking control of rescue robots due to its advantages of less computing resources and good real-time performance. However, in such a scene, the driving environment is complex and the path curvature changes greatly. Since LMPC can only introduce linearized feedforward information, the tracking accuracy of the path with large curvature changes is low. To overcome this issue, combined with the idea of preview control, preview-linear MPC is designed in this paper. The controller is verified by MATLAB/Simulink simulation and prototype experiment. The results show that the proposed method can improve the tracking accuracy while ensuring real-time performance and has better tracking performance for the path with large curvature variation.

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Linear model predictive control of automatic parking path tracking with soft constraints

Cited by 48 publications

References 31 publications

Barrier Lyapunov Function-Based Adaptive Back-Stepping Control for Electronic Throttle Control System

Barrier Lyapunov Function-Based Adaptive Back-Stepping Control for Electronic Throttle Control System

Automatic Parking Path Planning Based on Ant Colony Optimization and the Grid Method

Efficient Path Tracking Control for Autonomous Driving of Tracked Emergency Rescue Robot under 6G Network

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