The Development of a Precise Articulated Bus Prediction Model for Model Predictive Control Algorithms

Beomjoon Pyun; Jaehoon Jeon; Jaemin Song; Hyungjeen Choi

doi:doi:10.11648/j.ijmea.20221004.14

| Peer-Reviewed

The Development of a Precise Articulated Bus Prediction Model for Model Predictive Control Algorithms

Beomjoon Pyun, Jaehoon Jeon, Jaemin Song, Hyungjeen Choi

Published in International Journal of Mechanical Engineering and Applications (Volume 10, Issue 4)

Received: 31 July 2022 Accepted: 23 August 2022 Published: 31 August 2022

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

This study presents a precise prediction model that can be applied to model-based control logic for the realization of autonomous driving systems (ADS) for bus rapid transit (BRT; articulated buses). When realizing model-based control logic, the more a prediction model is accurate, the greater that control logic will be the robustness. However, a heavy prediction model is not recommended for real-time operation of model based control logic. Therefore, in this study, a revised ‘Multi-axle dynamics model’ is used to develop a prediction model which is considering dominant parts of longitudinal, lateral, and roll dynamics. Consequently, this study shows the process of testing BRT buses (the targets), and designing a prediction model by comparing with the test results. As a result, a BRT prediction model is developed with correlation of 92% or above. Furthermore, the prediction model will be applied in the future to a model predictive control (MPC) algorithm and used to construct ADS for BRT buses. In addition, it is anticipated that the use of this prediction model will contribute to the design of control logic for diverse advanced driver assistance systems (ADAS).

Published in	International Journal of Mechanical Engineering and Applications (Volume 10, Issue 4)
DOI	10.11648/j.ijmea.20221004.14
Page(s)	68-81
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Autonomous Driving Systems (ADS), Articulated Bus, Bus Rapid Transit (BRT), Inner Model, Model Predictive Control (MPC), Modeling, Prediction Model, Vehicle Test

References

[1]	P. F. Muir, and C. P. Neuuman, Modeling and Control of Wheeled Mobile Robots, Dissertation in Carnegie Mellon University, August (1988).
[2]	R. C. Coulter, Implementation of The Pure Pursuit Path Tracking Algorithm, Technical Report CMU-RI-TR-92-01, Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, January (1992).
[3]	M. Samuel, M. Hussein, M. B. Mohamad, A Review of some Pure-Pursuit based Path Tracking Techniques for Control of Autonomous Vehicle, International Journal of Computer Applications, 135, 1, 0975-8887, February (2016).
[4]	T. D. Gillespie, Fundamentals of Vehicle Dynamics, Society of Automotive Engineers, Inc. Pennsylvania, USA (1992).
[5]	S. Thrun, M. Montemerio, H. Dahlkamp, et al., Stanley: The Robot that Won the DARPA Grand Challenge, Journal of Field Robotics, 23 (9), 661-692 (2006).
[6]	M. Cibooglu, Hybrid Controller Approach for an Autonomous Ground Vehicle Path Tracking Problem, Dissertation in Istanbul Technical University, December, (2016).
[7]	N. H. Amer, K. Hudha, H. Zamzuri, et al., Adaptive Modified Stanley Controller with Fuzzy Supervisory System for Trajectory Tracking of an Autonomous Armored Vehicle, Robotics and Autonomous Systems, 105, 94-111 (2018).
[8]	S. D. Keen, and D. J. Cole, Steering Control using Model Predictive Control and Multiple Internal Models, International Symposium on Advanced Vehicle Control, August 20-24, (2006).
[9]	H. Eric Tseng, M. Bujarbaruah, X. Zhang, F. Borrelli, Adaptive MPC for Autonomous Lane Keeping, International Symposium on Advanced Vehicle Control (AVEC), December 3, (2018).
[10]	R. Schmied, H. Waschl, R. Quirynen, M. Diehl, L. d. Re, Nonlinear MPC for Emission Efficient Cooperative Adaptive Cruise Control, International Federation of Automatic Control (IFAC), 160-165 (2015).
[11]	Y. Zhang, A. Khajepour, Y. Huang, Multi-Axle/Articulated Bus Dynamics Modeling: A Reconfigurable Approach. International Journal of Vehicle Mechanics and Mobility, 59, 9, 1315-1343 (2018).
[12]	M. M. Michalek, B. Patkowski, T. Gawron, Modular Kinematic Modelling of Articulated Buses, IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 8381-8394 (2020).
[13]	W. Wenwei, Z. Wei, Z. Hanyu, C. Wanke, Yaw Stability Control through Independent Driving Torque Control of Mid and Rear Wheels of an Articulated Bus, Institution of Mechanical Engineers, Vol. 234, no. 13, pp. 2947-2960 (2020).
[14]	Gibbons, J. D., Chakreborti, S., Nonparametric Statistical Inference. 4th edition. Marcel Dekker, Inc. Alabama, USA (2014).
[15]	L. Wang, Model Predictive Control System Design and Implementation Using MATLAB, Advances in Industrial Control, London, UK (2009).

Cite This Article

Plain Text BibTeX RIS

APA Style

Beomjoon Pyun, Jaehoon Jeon, Jaemin Song, Hyungjeen Choi. (2022). The Development of a Precise Articulated Bus Prediction Model for Model Predictive Control Algorithms. International Journal of Mechanical Engineering and Applications, 10(4), 68-81. https://doi.org/10.11648/j.ijmea.20221004.14

Copy | Download

ACS Style

Beomjoon Pyun; Jaehoon Jeon; Jaemin Song; Hyungjeen Choi. The Development of a Precise Articulated Bus Prediction Model for Model Predictive Control Algorithms. Int. J. Mech. Eng. Appl. 2022, 10(4), 68-81. doi: 10.11648/j.ijmea.20221004.14

Copy | Download

AMA Style

Beomjoon Pyun, Jaehoon Jeon, Jaemin Song, Hyungjeen Choi. The Development of a Precise Articulated Bus Prediction Model for Model Predictive Control Algorithms. Int J Mech Eng Appl. 2022;10(4):68-81. doi: 10.11648/j.ijmea.20221004.14

Copy | Download

@article{10.11648/j.ijmea.20221004.14,
  author = {Beomjoon Pyun and Jaehoon Jeon and Jaemin Song and Hyungjeen Choi},
  title = {The Development of a Precise Articulated Bus Prediction Model for Model Predictive Control Algorithms},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {10},
  number = {4},
  pages = {68-81},
  doi = {10.11648/j.ijmea.20221004.14},
  url = {https://doi.org/10.11648/j.ijmea.20221004.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20221004.14},
  abstract = {This study presents a precise prediction model that can be applied to model-based control logic for the realization of autonomous driving systems (ADS) for bus rapid transit (BRT; articulated buses). When realizing model-based control logic, the more a prediction model is accurate, the greater that control logic will be the robustness. However, a heavy prediction model is not recommended for real-time operation of model based control logic. Therefore, in this study, a revised ‘Multi-axle dynamics model’ is used to develop a prediction model which is considering dominant parts of longitudinal, lateral, and roll dynamics. Consequently, this study shows the process of testing BRT buses (the targets), and designing a prediction model by comparing with the test results. As a result, a BRT prediction model is developed with correlation of 92% or above. Furthermore, the prediction model will be applied in the future to a model predictive control (MPC) algorithm and used to construct ADS for BRT buses. In addition, it is anticipated that the use of this prediction model will contribute to the design of control logic for diverse advanced driver assistance systems (ADAS).},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - The Development of a Precise Articulated Bus Prediction Model for Model Predictive Control Algorithms
AU  - Beomjoon Pyun
AU  - Jaehoon Jeon
AU  - Jaemin Song
AU  - Hyungjeen Choi
Y1  - 2022/08/31
PY  - 2022
N1  - https://doi.org/10.11648/j.ijmea.20221004.14
DO  - 10.11648/j.ijmea.20221004.14
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 68
EP  - 81
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20221004.14
AB  - This study presents a precise prediction model that can be applied to model-based control logic for the realization of autonomous driving systems (ADS) for bus rapid transit (BRT; articulated buses). When realizing model-based control logic, the more a prediction model is accurate, the greater that control logic will be the robustness. However, a heavy prediction model is not recommended for real-time operation of model based control logic. Therefore, in this study, a revised ‘Multi-axle dynamics model’ is used to develop a prediction model which is considering dominant parts of longitudinal, lateral, and roll dynamics. Consequently, this study shows the process of testing BRT buses (the targets), and designing a prediction model by comparing with the test results. As a result, a BRT prediction model is developed with correlation of 92% or above. Furthermore, the prediction model will be applied in the future to a model predictive control (MPC) algorithm and used to construct ADS for BRT buses. In addition, it is anticipated that the use of this prediction model will contribute to the design of control logic for diverse advanced driver assistance systems (ADAS).
VL  - 10
IS  - 4
ER  -

Copy | Download

Author Information

Beomjoon Pyun

School of Electrical Engineering, Korea Advanced Institute of Science & Technology, Daejeon, Republic of Korea
Jaehoon Jeon

Korea Automotive Technology Institute, Cheonan City, Republic of Korea
Jaemin Song

Korea Automotive Technology Institute, Cheonan City, Republic of Korea
Hyungjeen Choi

Korea Automotive Technology Institute, Cheonan City, Republic of Korea

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Beomjoon Pyun, Jaehoon Jeon, Jaemin Song, Hyungjeen Choi. (2022). The Development of a Precise Articulated Bus Prediction Model for Model Predictive Control Algorithms. International Journal of Mechanical Engineering and Applications, 10(4), 68-81. https://doi.org/10.11648/j.ijmea.20221004.14

Copy | Download

ACS Style

Beomjoon Pyun; Jaehoon Jeon; Jaemin Song; Hyungjeen Choi. The Development of a Precise Articulated Bus Prediction Model for Model Predictive Control Algorithms. Int. J. Mech. Eng. Appl. 2022, 10(4), 68-81. doi: 10.11648/j.ijmea.20221004.14

Copy | Download

AMA Style

Beomjoon Pyun, Jaehoon Jeon, Jaemin Song, Hyungjeen Choi. The Development of a Precise Articulated Bus Prediction Model for Model Predictive Control Algorithms. Int J Mech Eng Appl. 2022;10(4):68-81. doi: 10.11648/j.ijmea.20221004.14

Copy | Download

@article{10.11648/j.ijmea.20221004.14,
  author = {Beomjoon Pyun and Jaehoon Jeon and Jaemin Song and Hyungjeen Choi},
  title = {The Development of a Precise Articulated Bus Prediction Model for Model Predictive Control Algorithms},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {10},
  number = {4},
  pages = {68-81},
  doi = {10.11648/j.ijmea.20221004.14},
  url = {https://doi.org/10.11648/j.ijmea.20221004.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20221004.14},
  abstract = {This study presents a precise prediction model that can be applied to model-based control logic for the realization of autonomous driving systems (ADS) for bus rapid transit (BRT; articulated buses). When realizing model-based control logic, the more a prediction model is accurate, the greater that control logic will be the robustness. However, a heavy prediction model is not recommended for real-time operation of model based control logic. Therefore, in this study, a revised ‘Multi-axle dynamics model’ is used to develop a prediction model which is considering dominant parts of longitudinal, lateral, and roll dynamics. Consequently, this study shows the process of testing BRT buses (the targets), and designing a prediction model by comparing with the test results. As a result, a BRT prediction model is developed with correlation of 92% or above. Furthermore, the prediction model will be applied in the future to a model predictive control (MPC) algorithm and used to construct ADS for BRT buses. In addition, it is anticipated that the use of this prediction model will contribute to the design of control logic for diverse advanced driver assistance systems (ADAS).},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - The Development of a Precise Articulated Bus Prediction Model for Model Predictive Control Algorithms
AU  - Beomjoon Pyun
AU  - Jaehoon Jeon
AU  - Jaemin Song
AU  - Hyungjeen Choi
Y1  - 2022/08/31
PY  - 2022
N1  - https://doi.org/10.11648/j.ijmea.20221004.14
DO  - 10.11648/j.ijmea.20221004.14
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 68
EP  - 81
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20221004.14
AB  - This study presents a precise prediction model that can be applied to model-based control logic for the realization of autonomous driving systems (ADS) for bus rapid transit (BRT; articulated buses). When realizing model-based control logic, the more a prediction model is accurate, the greater that control logic will be the robustness. However, a heavy prediction model is not recommended for real-time operation of model based control logic. Therefore, in this study, a revised ‘Multi-axle dynamics model’ is used to develop a prediction model which is considering dominant parts of longitudinal, lateral, and roll dynamics. Consequently, this study shows the process of testing BRT buses (the targets), and designing a prediction model by comparing with the test results. As a result, a BRT prediction model is developed with correlation of 92% or above. Furthermore, the prediction model will be applied in the future to a model predictive control (MPC) algorithm and used to construct ADS for BRT buses. In addition, it is anticipated that the use of this prediction model will contribute to the design of control logic for diverse advanced driver assistance systems (ADAS).
VL  - 10
IS  - 4
ER  -

Copy | Download