Development and Modeling of Remotely Operated Scaled Multi-wheeled Combat Vehicle Using System Identification
This paper describes the development and modeling of a remotely operated scaled multi-wheeled combat vehicle (ROMW-CV) using system identification methodology for heading angle tracking. The vehicle was developed at the vehicle dynamics and crash research (VDCR) Lab at the University of Ontario Institute of Technology (UOIT) to analyze the characteristics of the full-size model. For such vehicles, the development of controllers is considered the most crucial issue. In this paper, the ROMWCV is developed first. An experimental test was carried out to record and analyze the vehicle input/output signals in open loop system, which is considered a multi-input-single-output (MISO) system. Subsequently, a fuzzy logic controller (FLC) was developed for heading angle tracking. The experiments showed that it was feasible to represent the dynamic characteristics of the vehicle using the system identification technique. The estimation and validation results demonstrated that the obtained identified model was able to explain 88.44% of the output vari-ation. In addition, the developed FLC showed a good heading angle tracking.
KeywordsAutonomous multi-wheeled vehicle system identification all wheel steering fuzzy logic (FL) parametric identification
Unable to display preview. Download preview PDF.
The authors wish to express their gratitude to the Egyptian Armed Forces for the financial support extended to the undergraduate and graduate students of the Vehicle Dynamics and Crash Research (VDCR) Laboratory for operating the vehicle during the experimental tests.
- A. Fisher. Google's Self–driving Cars: A Quest for Acceptance, [Online], Available: http://www.popsci.com/cars/article/2013–09/google–self–driving–car, 2014.Google Scholar
- A. M. Kessler. Elon Musk Says Self–Driving Tesla Cars W ill Be in the U. S. by Summer, The New York Times, [Online], Available: https://www.nytimes.com/2015/03/20/business/elon–musk–says–self–driving–tesla–cars–willbe–in–the–us–by–summer.html?_r= 0, 2015.Google Scholar
- C. West, A. Montazeri, S. D. Monk, D. Duda, C. J. Taylor. A new approach to improve the parameter estimation accuracy in robotic manipulators using a multi–objective output error identification technique. In Proceedings of the 26th International Symposium on Robot and Human Interactive Communication, Lisbon, Portugal, pp. 14061411, 2017. DOI: 10.1109/R?MAN.2017.8172488.Google Scholar
- M. Li, H. P. Wu, H. Handroos, Y. B. Wang, A. Loving, O. Crofts, M. Coleman, R. Skilton, G. Burroughes, J. Keep. RACE RM Control Team. Dynamic model identification method of manipulators for inside DEMO engineering. Fusion Engineering and Design, vol. 124, pp. 638–644, 2017. DOI: 10.1016/j.fusengdes.2017.02.034.CrossRefGoogle Scholar
- K. Park, Y. Choi. System identification method for robotic manipulator based on dynamic momentum regressor. In Proceedings of the 12th IEEE International Conference on Control and Automation, Kathmandu, Nepal, pp. 755760, 2016, DOI: 10.1109/ICCA.2016.7505369.Google Scholar
- R. Wilensky. An Extensive Review on Generator Excitation System Modeling, Design, and Parameter Identification, Finland: HAL CCSD, 2016.Google Scholar
- F. Ding. Combined state and least squares parameter estimation algorithms for dynamic systems. Applied Mathematical Modelling, vol. 38, no. 1, pp. 403~412, 2014. DOI: 10.1016/j.apm.2013.06.007.Google Scholar
- M. Bisheban, T. Lee. Computational geometric system identification for the attitude dynamics on SO(3). In Proceedings of American Control Conference, Seattle, USA, pp. 2249–2254, 2017. DOI: 10.23919/ACC.2017.7963287.Google Scholar
- B. R. J. Haverkamp, C. T. Chou, M. H. Verhaegen, R. Jo hansson. Identification of continuous–time MIMO state space models from sampled data, in the presence of process and measurement noise. In Proceedings of the 35th IEEE Conference on Decision and Control, Kobe, Japan, pp. 1539–1544, 1996. DOI: 10.1109/CDC.1996.572741.CrossRefGoogle Scholar
- A. Garg, K. Tai, B. N. Panda. System identification: Survey on modeling methods and models. In Proceedings of International Conference on Artificial Intelligence and Evolutionary Computation in Engineering Systems, Springer, Singapore, pp. 607–615, 2017. DOI: 10.1007/978981–10–3174–8.Google Scholar
- Y. C. Lai, Q. L. Tri. System identification and control of a small unmanned helicopter at hover mode. In Proceedings of the 2nd International Conference on Control and Robotics Engineering, Bangkok, Thailand, pp. 92–96, 2017. DOI: 10.1109/ICCRE.2017.7935049.Google Scholar
- E. P. Mendes, A. A. D. Medeiros. Identification of quasilinear dynamic model with dead zone for mobile robot with differential drive. In Proceedings of Latin American Robotics Symposium and Intelligent Robotic Meeting, Sao Bernardo do Campo, Brazil, pp. 132–137, 2010. DOI: 10.1109/LARS.2010.36.Google Scholar
- Fahmizal, C. H. Kuo. Trajectory and heading tracking of a mecanum wheeled robot using fuzzy logic control. In Proceedings of International Conference on Instrumentation, Control and Automation, Bandung, Indonesia, pp. 54–59, 2016. DOI: 10.1109/ICA.2016.7811475.Google Scholar
- A. M. Rao, K. Ramji, B. S. K. S. S. Rao, V. Vasu, C. Puneeth. Navigation of non–holonomic mobile robot using neuro–fuzzy logic with integrated safe boundary algorithm. International Journal of Automation and Computing, vol. 14, no. 3, pp. 285–294, 2017. DOI: 10.1007/s11633–016–1042–y.CrossRefGoogle Scholar
- W. Pedrycz. Fuzzy Sets Engineering, Boca Raton, USA: CRC Press, 1995.Google Scholar
- H. Tan, J. Iacobellis, A. Levenko, R. Mutiger, K. Ng, J. Averill. Design and Development of 8x8 Electric Combat Vehicle. Capstone Systems poroject final Engineering Report, Department of Mechanical Engineering, University of Ontario Institute of Technology, Canada, 2017.Google Scholar
- L. Lennart. System Identification: Theory for the User, 2nd ed., New Jersey, USA: Prentice Hall, 1999.Google Scholar