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

Received: 31 July 2022     Accepted: 23 August 2022     Published: 31 August 2022
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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), 2022. 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
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[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
  • 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

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    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

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    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

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  • @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}
    }
    

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  • 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
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    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  - 

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Author Information
  • School of Electrical Engineering, Korea Advanced Institute of Science & Technology, Daejeon, Republic of Korea

  • Korea Automotive Technology Institute, Cheonan City, Republic of Korea

  • Korea Automotive Technology Institute, Cheonan City, Republic of Korea

  • Korea Automotive Technology Institute, Cheonan City, Republic of Korea

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