Improved Model Predictive Control Path Tracking Strategy Based an Online Updating Algorithm With Cosine Similarity and a Horizon Factor

Lin, Xinyou; Tang, Yunliang; Zhou, Binhao

doi:10.1109/tits.2021.3114060

Cited by 16 publications

(4 citation statements)

References 31 publications

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“…To ensure that the vehicle follows its desired trajectory, the bicycle model in [38] is adopted to represent the vehicle dynamics. In this model, we do not consider the effects of suspension and aerodynamics, the state variables are defined as ξ(t) = [ ẋ, ẏ, ψ, ψ, Y, X] T , and the control input vector is the front wheel angle, defined as u(t) = δ f .…”

Section: Vehicle Dynamics Modelmentioning

confidence: 99%

Fast Trajectory Tracking Control Algorithm for Autonomous Vehicles Based on the Alternating Direction Multiplier Method (ADMM) to the Receding Optimization of Model Predictive Control (MPC)

Dong,

Ye,

Luo

et al. 2023

Sensors

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In order to improve the real-time performance of the trajectory tracking of autonomous vehicles, this paper applies the alternating direction multiplier method (ADMM) to the receding optimization of model predictive control (MPC), which improves the computational speed of the algorithm. Based on the vehicle dynamics model, the output equation of the autonomous vehicle trajectory tracking control system is constructed, and the auxiliary variable and the dual variable are introduced. The quadratic programming problem transformed from the MPC and the vehicle dynamics constraints are rewritten into the solution of the ADMM form, and a decreasing penalty factor is used during the solution process. The simulation verification is carried out through the joint simulation platform of Simulink and Carsim. The results show that, compared with the active set method (ASM) and the interior point method (IPM), the algorithm proposed in this paper can not only improve the accuracy of trajectory tracking, but also exhibits good real-time performance in different prediction time domains and control time domains. When the prediction time domain increases, the calculation time shows no significant difference. This verifies the effectiveness of the ADMM in improving the real-time performance of MPC.

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Section: Vehicle Dynamics Modelmentioning

confidence: 99%

Fast Trajectory Tracking Control Algorithm for Autonomous Vehicles Based on the Alternating Direction Multiplier Method (ADMM) to the Receding Optimization of Model Predictive Control (MPC)

Dong,

Ye,

Luo

et al. 2023

Sensors

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“…Liang et al used RBF neural networks to adaptively compensate for errors caused by changes in the curvature of complex paths at high speed, with a maximum error of 0.285 m [21]. Lin et al adopted fuzzy control to optimize the prediction and control horizon online with v x and v y as inputs and the horizon factor as output [22]. Dai et al proposed a kind of MPC with adaptive preview characteristics, and the maximum lateral error was 0.04 m at 10 m/s under double-lane path tracking by the simulation outcomes [23].…”

Section: Introductionmentioning

confidence: 99%

Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

Wang,

Niu,

Wang

et al. 2024

Agriculture

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To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations.

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“…In recent years, for the vehicle tracking problem under variable speed, Yuan et al (2020) proposed an optimal heading angle reference model based on preview information, and utilized an upgraded model predictive controller to increase the vehicle’s tracking accuracy at various speeds. Lin et al (2021) investigated the improved MPC path-tracking theory to elevate the tracking accuracy of the vehicle, where the cosine similarity mechanism was combined with fuzzy control to enhance the effectiveness of the improved MPC controller.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered model predictive-preview control strategy for trajectory tracking of autonomous vehicle

Zhang,

Lu,

Wang

et al. 2024

Transactions of the Institute of Measurement and Control

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This paper proposes an event-triggered model predictive-preview control strategy for autonomous vehicle trajectory tracking. First, the dynamic equation is established based on the preview road curvature and the vehicle’s 2 degree-of-freedom relationship. A model predictive tracking controller is designed by predicting the system dynamics in the future. Second, to reduce the computational burden of the controller, the triggering conditions are designed according to the system’s stability and feasibility. The proposed event-triggered model predictive-preview control strategy not only makes the autonomous vehicles maintain tracking accuracy but also reduces the number of online optimization. Then, asymptotically stable of closed-loop systems are proved by using Lyapunov’s theorem. Finally, simulation experiments show that the proposed strategy has some advantages over traditional model predictive control in terms of improving vehicle tracking accuracy and reducing algorithm complexity.

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Improved Model Predictive Control Path Tracking Strategy Based an Online Updating Algorithm With Cosine Similarity and a Horizon Factor

Cited by 16 publications

References 31 publications

Fast Trajectory Tracking Control Algorithm for Autonomous Vehicles Based on the Alternating Direction Multiplier Method (ADMM) to the Receding Optimization of Model Predictive Control (MPC)

Fast Trajectory Tracking Control Algorithm for Autonomous Vehicles Based on the Alternating Direction Multiplier Method (ADMM) to the Receding Optimization of Model Predictive Control (MPC)

Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

Event-triggered model predictive-preview control strategy for trajectory tracking of autonomous vehicle

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