An adaptive-prediction-horizon model prediction control for path tracking in a four-wheel independent control electric vehicle

Zhang, Bing; Zong, Changfu; Chen, Guoying; Li, Guiyuan

doi:10.1177/0954407018821527

Cited by 25 publications

(18 citation statements)

References 36 publications

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“…To realize vehicle path tracking MPC control under different speed and different curvature conditions, Reference [69][70][71][72] proposed parameters adaptive MPC control strategies using fuzzy rules and multiple controllers combination to achieve adaptive adjustment of control parameters under different operating conditions. Reference [73][74][75][76][77] studied the MPC fast online solution methods of path tracking for autonomous vehicle using differential evolution algorithm, Laguerre function, and look-up table to improve the efficiency of MPC controller calculations.…”

Section: G Mpc Control Methodsmentioning

confidence: 99%

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

108

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Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This paper introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

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Section: G Mpc Control Methodsmentioning

confidence: 99%

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

108

View full text Add to dashboard Cite

show abstract

“…Combining the constraints (15), (16), and (19)- (22), the continuous optimal control problem can be transformed into a discrete optimal control problem. The optimal path is calculated as…”

Section: Path Planningmentioning

confidence: 99%

“…On the contrary, a long predictive horizon is required to guarantee the accuracy of tracking performance. Zhang et al 22 propose a MPC path tracking controller with adaptive predictive horizon to balance stability and tracking accuracy. Richards et al 23 extend the constraint tightening method to robust MPC using a varying predictive horizon.…”

Section: Introductionmentioning

confidence: 99%

Overtaking control strategy based on model predictive control with varying horizon for unmanned ground vehicle

Zhao

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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In this paper, a synthesized novel strategy of varying predictive horizon-based model predictive control is proposed for the overtaking control of unmanned ground vehicle. The whole control strategy includes path planning and path tracking. First, the preferred path in presence of diverse constraints of states, inputs, and collision avoidance can be calculated using Gauss pseudospectral method where expected position, velocity, and attitude are provided. Correspondingly, the continuous optimal control problem is converted to discrete nonlinear programming. Second, model predictive control is developed for tracking the optimized path. Considering the effect of the predictive horizon and the Gauss points’ distribution on tracking performance, the varying predictive horizon is introduced to improve the tracking accuracy in non-smooth path. By the varying predictive horizon-based model predictive control method, less computation burden and better control performance are achieved. For the difference between the mathematical expressions and the real unmanned ground vehicle dynamics, genetic algorithm is utilized to identify the parameters of tire model. Simulations in MATLAB and CarSim are both implemented to illustrate the effectiveness of the proposed method.

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“…Path following control, whose task is to accurately track the reference path on the premise of maintaining stability, is essential for AV. 1 It has attracted attention from many researchers to improve the path tracking performance and reduce traffic accidents. Liu et al 2 presented a path tracking architecture synthesizing a preview feedforward controller and an adaptive sliding model feedback controller.…”

Section: Introductionmentioning

confidence: 99%

Dynamic path planning and path following control for autonomous vehicle based on the piecewise affine tire model

Chen

Yan

Wang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper proposes a novel dynamic path planning and path following control method for collision avoidance, which works based on an improved piecewise affine tire model. The main contribution of this work is the design of a dynamic path planning method based on model predictive control, where it replans a maneuverable path to avoid moving obstacle in real time. A hierarchical control framework contains a high-level path replanning model predictive control and a low-level path following model predictive control. A collision avoidance cost function in the high hierarchies was designed to calculate the relative dynamic distance, which copes with the sudden obstacle. Moreover, the replanning path is the optimized output according to reference trajectory, obstacle, and handling stability. The control objective of the low hierarchies is to accurately track the replanning path, especially for the increased nonlinearity of large tire sideslip angle. For this reason, an improved piecewise affine tire model is designed and used for model predictive control to improve the path following performance and reduce calculated burden. The main improvement of the piecewise affine tire model is that the varied lateral stiffness coefficients adapt to the change of the tire sideslip angle in different tire regions. Based on the CarSim and Simulink platform, the dynamic path planning and path following simulations are designed to test the proposed method. The simulation results demonstrate the effectiveness of the proposed method.

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An adaptive-prediction-horizon model prediction control for path tracking in a four-wheel independent control electric vehicle

Cited by 25 publications

References 36 publications

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Overtaking control strategy based on model predictive control with varying horizon for unmanned ground vehicle

Dynamic path planning and path following control for autonomous vehicle based on the piecewise affine tire model

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