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Research for Nonlinear Model Predictive Controls to Laterally Control Unmanned Vehicle Trajectory Tracking
| Content Provider | MDPI |
|---|---|
| Author | Zhao, Kegang Wang, Chengxia Xiao, Guoquan Li, Haolin Ye, Jie Liu, Yanwei |
| Copyright Year | 2020 |
| Description | The autonomous driving is rapid developing recently and model predictive controls (MPCs) have been widely used in unmanned vehicle trajectory tracking. MPCs are advantageous because of their predictive modeling, rolling optimization, and feedback correction. In recent years, most studies on unmanned vehicle trajectory tracking have used only linear model predictive controls to solve MPC algorithm shortcomings in real time. Previous studies have not investigated problems under conditions where speeds are too fast or trajectory curvatures change rapidly, because of the poor accuracy of approximate linearization. A nonlinear model predictive control optimization algorithm based on the collocation method is proposed, which can reduce calculation load. The algorithm aims to reduce trajectory tracking errors while ensuring real-time performance. Monte Carlo simulations of the uncertain systems are carried out to analyze the robustness of the algorithm. Hardware-in-the-loop simulation and actual vehicle experiments were also conducted. Experiment results show that under i7-8700, the calculation time is less than 100 ms, and the mean square error of the lateral deviation is maintained at |
| Starting Page | 6034 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app10176034 |
| Journal | Applied Sciences |
| Issue Number | 17 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-08-31 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Marine Engineering Trajectory Tracking Nonlinear Model Predictive Controls Collocation Method Real-time |
| Content Type | Text |
| Resource Type | Article |
