AI Article Synopsis
- The paper introduces a new model predictive control (MPC) algorithm aimed at enhancing path tracking accuracy by adjusting the sampling time based on control inputs like lateral velocity and steering angle.
- The research involves testing this algorithm in both straight and curved driving scenarios to calculate optimal control inputs at each step.
- Experiments conducted using MATLAB's Automated Driving Toolbox show that the new MPC algorithm outperforms existing methods in terms of path-following performance and efficiency.
Article 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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8541678 | PMC |
| http://dx.doi.org/10.3390/s21206845 | DOI Listing |
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