Path following control of unmanned vehicle based on improved model predictive control

ZHOU Yipeng; LI Cong; YANG Wei

doi:10.12299/jsues.22-0251

Volume 37 Issue 2

Jun. 2023

Turn off MathJax

Article Contents

Article Navigation > Journal of Shanghai University of Engineering Science > 2023 > 37(2): 164-172

ZHOU Yipeng, LI Cong, YANG Wei. Path following control of unmanned vehicle based on improved model predictive control[J]. Journal of Shanghai University of Engineering Science, 2023, 37(2): 164-172. doi: 10.12299/jsues.22-0251

Citation:

ZHOU Yipeng, LI Cong, YANG Wei. Path following control of unmanned vehicle based on improved model predictive control[J]. Journal of Shanghai University of Engineering Science, 2023, 37(2): 164-172. doi: 10.12299/jsues.22-0251

Citation:

PDF( 2594 KB)

Path following control of unmanned vehicle based on improved model predictive control

doi: 10.12299/jsues.22-0251

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2022-08-15
Publish Date: 2023-06-20

Abstract

Abstract

In order to improve the path following accuracy and stability of unmanned vehicles, a parameter adaptive model predictive control (MPC) method based on particle swarm optimization (PSO) and Gaussian process regression (GPR) was proposed. By using PSO to optimize MPC parameters offline and GPR to generate optimal parametric surfaces, the path following performances of unmanned vehicles can be improved under various working conditions. The simulated results show that the improved MPC method achieves good path tracking accuracy while maintaining vehicle stability throughout the path following process. Finally, the effectiveness of the improved MPC method was verified on a real unmanned vehicle.
- model predictive control (MPC),
- particle swarm optimization (PSO) algorithm,
- path following,
- lateral control,
- Gaussian process regression (GPR)

FullText(HTML)

References(11)

References

[1]	NI J J, CHEN Y N, CHEN Y, et al. A survey on theories and applications for self-driving cars based on deep learning methods[J] . Applied Sciences,2020,10(8):2749. doi: 10.3390/app10082749
[2]	CLEMENTS L M, KOCKELMAN K M. Economic effects of automated vehicles[J] . Transportation Research Record,2017,2606(1):106 − 114. doi: 10.3141/2606-14
[3]	陈特, 陈龙, 徐兴, 等. 基于 Hamilton 理论的无人车路径跟踪控制[J] . 北京理工大学学报,2019,39(7):676 − 682.
[4]	ZHANG Y H, WANG W D, WANG W, et al. An Adaptive Constrained Path Following Control Scheme for Autonomous Electric Vehicles[J] . IEEE Transactions on Vehicular Technology,2022,71(4):3569 − 3578. doi: 10.1109/TVT.2022.3146134
[5]	SUSUKI R, KAWAI F, NAKAZAWA C, et al. Parameter optimization of model predictive control using PSO[C]//Proceedings of 2008 SICE Annual Conference. Tokyo: IEEE, 2008: 1981 − 1988.
[6]	MOUMOUH H, LANGLOIS N, HADDAD M. Off-line Robustness Improvement of a Predictive Controller Using a Novel Tuning Approach Based on Artificial Neural Network[C]//Proceedings of 2020 IEEE 16th International Conference on Control & Automation (ICCA). Sapporo, Hokkaido: IEEE, 2020: 797 − 802.
[7]	王银, 张灏琦, 孙前来, 等. 基于自适应 MPC 算法的轨迹跟踪控制研究[J] . 计算机工程与应用,2021,57(14):251 − 258. doi: 10.3778/j.issn.1002-8331.2007-0015
[8]	XU S B, PENG H. Design, analysis, and experiments of preview path tracking control for autonomous vehicles[J] . IEEE Transactions on Intelligent Transportation Systems,2019,21(1):48 − 58.
[9]	HUBER M F. Recursive Gaussian process: On-line regression and learning[J] . Pattern Recognition Letters,2014,45:85 − 91. doi: 10.1016/j.patrec.2014.03.004
[10]	ARANY R R, AUWERAER H V D, SON T D. Learning Control for Autonomous Driving on Slippery Snowy Road Conditions[C]//Proceedings of 2020 IEEE Conference on Control Technology and Applications (CCTA). Montréal: IEEE, 2020: 312 − 317.
[11]	SEEGER M. Gaussian processes for machine learning[J] . International journal of neural systems,2004,14(2):69 − 106. doi: 10.1142/S0129065704001899