Hardware in the loop simulation and hierarchical predictive control of a TWPTR

HIL and HPC of a TWPTR

  • Mohammadbagher Shahgholian m_shahgholian@sbu.ac.ir
  • Davood Gharavian
Keywords: Two-wheeled robot, Segway, HIL, Model Predictive Control, Time Delay Control

Abstract

In this paper, we use a nonlinear hierarchical model predictive control (MPC) to stabilize the Segway robot. We also use hardware in the loop (HIL) simulation in order to model the delay response of the wheels' motor and verify the control algorithm. In Two-Wheeled Personal Transportation Robots (TWPTR), changing the center of mass location and value, the nonlinearity of the equations, and the dynamics of the system are topics complicating the control problem. A nonlinear MPC predicts the dynamics of the system and solves the control problem efficiently, but requires the exact information of the system models. Since model uncertainties are unavoidable, the time-delay control (TDC) method is used to cancel the unknown dynamics and unexpected disturbances. When TDC method is applied, the results show that the maximum required torque for engines is reduced by 7%. And the maximum displacement of the robot has dropped by 44% around the balance axis. In other words, robot stability has increased by 78%. Due to the cost of implementing control in practice, this research runs the HIL simulation for the first time. The use of this simulation helps in implementing the control algorithms without approximation, and also the system response can be discussed in a more realistic way.

References

[1] P. Cramton, R. R. Geddes, and A. Ockenfels, “Set road charges in real time to ease traffic,” Nature, vol. 560, no. 7716, p. 23, Aug. 2018, doi: 10.1038/d41586-018-05836-0.
[2] J. Huang, F. Ding, T. Fukuda, and T. Matsuno, “Modeling and Velocity Control for a Novel Narrow Vehicle Based on Mobile Wheeled Inverted Pendulum,” IEEE Trans. Control Syst. Technol., vol. 21, no. 5, pp. 1607–1617, Sep. 2013, doi: 10.1109/TCST.2012.2214439.
[3] J. Huang, M. Zhang, S. Ri, C. Xiong, Z. Li, and Y. Kang, “High-Order Disturbance Observer Based Sliding Mode Control for Mobile Wheeled Inverted Pendulum Systems,” IEEE Trans. Ind. Electron., pp. 1–1, 2019, doi: 10.1109/TIE.2019.2903778.
[4] R. P. M. Chan, K. A. Stol, and C. R. Halkyard, “Review of modelling and control of two-wheeled robots,” Annu. Rev. Control, vol. 37, no. 1, pp. 89–103, Apr. 2013, doi: 10.1016/j.arcontrol.2013.03.004.
[5] J. X. Xu, Z. Q. Guo, and T. H. Lee, “Design and Implementation of a Takagi #x2013;Sugeno-Type Fuzzy Logic Controller on a Two-Wheeled Mobile Robot,” IEEE Trans. Ind. Electron., vol. 60, no. 12, pp. 5717–5728, Dec. 2013, doi: 10.1109/TIE.2012.2230600.
[6] A. Castro, “Modeling and dynamic analysis of a two-wheeled inverted-pendulum,” Thesis, Georgia Institute of Technology, 2012.
[7] H. Jian-hai, Z. Shu-shang, L. Ji-shun, and L. Hang, “Research on developed parallel two-wheeled robot and its control system,” in 2008 IEEE International Conference on Automation and Logistics, 2008, pp. 2471–2475, doi: 10.1109/ICAL.2008.4636583.
[8] D. B. Pham and S.-G. Lee, “Hierarchical sliding mode control for a two-dimensional ball segway that is a class of a second-order underactuated system,” J. Vib. Control, vol. 25, no. 1, pp. 72–83, Jan. 2019, doi: 10.1177/1077546318770089.
[9] T. Xi, “Fuzzy adaptive control of a two-wheeled inverted pendulum,” Thesis, 2018.
[10] A. Lim, S. Lim, and S. Kim, “Enhancer prediction with histone modification marks using a hybrid neural network model,” Methods, Mar. 2019, doi: 10.1016/j.ymeth.2019.03.014.
[11] I. Chawla and A. Singla, “System Identification of an Inverted Pendulum Using Adaptive Neural Fuzzy Inference System,” in Harmony Search and Nature Inspired Optimization Algorithms, 2019, pp. 809–817.
[12] D. E. Seborg, T. F. Edgar, D. A. Mellichamp, and F. J. Doyle, Process Dynamics and Control, Chapter 20: Model Predictive Control, 4th ed. Wiley, 2016.
[13] A. Sheikhlar, M. Zarghami, A. Fakharian, and M. B. Menhaj, “Delay Compensation on Fuzzy Trajectory Tracking Control of Omni-Directional Mobile Robots,” AUT J. Electr. Eng., vol. 45, no. 2, pp. 57–64, Sep. 2015, doi: 10.22060/eej.2015.508.
[14] Y. Shao, M. A. Mohd Zulkefli, Z. Sun, and P. Huang, “Evaluating connected and autonomous vehicles using a hardware-in-the-loop testbed and a living lab,” Transp. Res. Part C Emerg. Technol., vol. 102, pp. 121–135, May 2019, doi: 10.1016/j.trc.2019.03.010.
[15] Shixianjun, S. jiakun, and L. hongxing, “Hardware-in-the-loop Simulation Framework Design For a UAV Embedded Control System,” in 2006 Chinese Control Conference, 2006, pp. 1890–1894, doi: 10.1109/CHICC.2006.280880.
[16] D. Titterton and J. Weston, Strapdown Inertial Navigation Technology, 2nd edition. Stevenage: The Institution of Engineering and Technology, 2005.
[17] H.-H. Yoo and B.-J. Choi, “Design of Vectored Sum-Based Fuzzy Logic Control System and Its Application to Segway-Type Mobile Robot,” Int. J. Humanoid Robot., vol. 14, no. 02, p. 1750003, Feb. 2017, doi: 10.1142/S0219843617500037.
[18] G. Bleser, Towards Visual-Inertial SLAM for Mobile Augmented Reality. München: Dr. Hut, 2009.
[19] K. E. Atkinson, W. Han, and D. Stewart, “Taylor and Runge–Kutta methods,” in Numerical Solution of Ordinary Differential Equations, Wiley-Blackwell, 2011, pp. 67–93.
[20] N. Hu, S. Li, Y. Zhu, and F. Gao, “Constrained Model Predictive Control for a Hexapod Robot Walking on Irregular Terrain,” J. Intell. Robot. Syst., vol. 94, no. 1, pp. 179–201, Apr. 2019, doi: 10.1007/s10846-018-0827-3.
[21] D. M. E. El-Hawary, Principles of Electric Machines with Power Electronic Applications; Chapter 4: Direct-Current Motors, 2 edition. Piscataway, NJ : New York, NY: Wiley-IEEE Press, 2002.
[22] D. X. Ba, H. Yeom, and J. Bae, “A direct robust nonsingular terminal sliding mode controller based on an adaptive time-delay estimator for servomotor rigid robots,” Mechatronics, vol. 59, pp. 82–94, May 2019, doi: 10.1016/j.mechatronics.2019.03.007.
[23] D. Chandler and J. Vallino, “Control System Plant Simulator: A Framework For Hardware In The Loop Simulation,” presented at the Annual Conference & Exposition, Pittsburgh, Pennsylvania, 2008, p. 13.
[24] H. Zhang, Y. Zhang, and C. Yin, “Hardware-in-the-Loop Simulation of Robust Mode Transition Control for a Series #x2013;Parallel Hybrid Electric Vehicle,” IEEE Trans. Veh. Technol., vol. 65, no. 3, pp. 1059–1069, Mar. 2016, doi: 10.1109/TVT.2015.2486558.
Published
2021-11-16
How to Cite
Shahgholian, M., & Gharavian, D. (2021). Hardware in the loop simulation and hierarchical predictive control of a TWPTR. Majlesi Journal of Electrical Engineering, 16(2). Retrieved from http://mjee.iaumajlesi.ac.ir/index/index.php/ee/article/view/3782
Section
Articles