ArticleSensors (Basel, Switzerland)2021
A Variable-Sampling Time Model Predictive Control Algorithm for Improving Path-Tracking Performance of a Vehicle.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multi-Target Vehicle Tracking Algorithm Based on Improved DeepSORT.Sensors (Basel, Switzerland) · 2024Article
- Multi-Tracking Sensor Architectures for Reconstructing Autonomous Vehicle Crashes: An Exploratory Study.Sensors (Basel, Switzerland) · 2024Article
- Machine vision-based autonomous road hazard avoidance system for self-driving vehicles.Scientific reports · 2024Article
- Weight Adaptive Path Tracking Control for Autonomous Vehicles Based on PSO-BP Neural Network.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes a novel model predictive control (MPC) algorithm that increases the path tracking performance according to the control input. The proposed algorithm reduces the path tracking errors of MPC by updating the sampling time of the next step according to the control inputs (i.e., the lateral velocity and front steering angle) calculated in each step of the MPC algorithm. The scenarios of a mixture of straight and curved driving paths were constructed, and the optimal control input was calculated in each step. In the experiment, a scenario was created with the Automated Driving Toolbox of MATLAB, and the path-following performance characteristics and computation times of the existing and proposed MPC algorithms were verified and compared with simulations. The results prove that the proposed MPC algorithm has improved path-following performance compared to those of the existing MPC algorithm.
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Registered trials
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