The Design of a Two-Wheeled Auto-Balancing Robot under Impulse Interruption
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Abstract
The innovation of two-wheeled balancing robots affects human life in different ways. Immense research continues to be undertaken to make such robots cheap, efficient, and reliable. Essentially, autonomous mobile robots are two-wheeled, vertical, and self-balancing. The robot's control system and automation application are intergraded with daily human life. Autonomy is applied to vehicles such as mobile robots, referred to as a vehicle capable of independent motion. Mobile robots can be used in various applications such as exploration, the food industry, home service, security, logistics, and many more. Moreover, it can be classified into four types: locomotion, perception, cognition, and navigation. In this work, the two-wheel, auto-balancing robot is investigated. The two-wheeled robot cannot operate without a controller and is susceptible to interruption and lean-to plunging outside the field. The PD controller, linear quadratic regulator (LQR), sliding mode control (SMC), and fuzzy logic control (FLC) can be used to set the robot into a stable upright position in the rotation angle condition. In this research, four control strategies are compared to obtain the solid validation of a two-wheeled balancing robot. These models are investigated in this study to find the best controller among the PD, LQR, SMC, and FLC and achieve solid validation. The PD and LQR show a convergence response time of 1.2–2.0 s to the equilibrium state for distance and time, which is slower than the SMC and FLC. The intersection to the equilibrium zone is 1.8 s and 1.2 s, respectively. The angle position response of the PD is 2.5 s, which is slower than the others. Whereas the LQR, SMC, and FLC reach equilibrium in 1.5, 1.5, and 1.25 s, respectively. According to the results, the FLC performed better in two-wheeled auto-balancing under the pendulum within the linear distance in centimeters and angle positions in radian.
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References
F. Jepsen, A. Soborg, A. R. Pedersen, and Z. Yang, “Development and control of an inverted pendulum driven by a reaction wheel,” in 2009 International Conference on Mechatronics and Automation, 2009, pp. 2829–2834.
A. Isidori, L. Marconi, and O. S. University, Robust Autonomous Guidance: An Internal Model Approach. New York, USA: Springer, 2003.
Y.-S. Ha and S. Yuta, “Trajectory tracking control for navigation of the inverse pendulum type self-contained mobile robot,” Robotics and Autonomous Systems, vol. 17, no. 1-2, pp. 65–80, Apr. 1996.
A.-J. Baerveldt and R. Klang, “A low-cost and low-weight attitude estimation system for an autonomous helicopter,” in Proceedings of IEEE International Conference on Intelligent Engineering Systems, 1997, pp. 391–395.
A. Salerno and J. Angeles, “The control of semiautonomous two-wheeled robots undergoing large payload-variations,” in Proceedings of IEEE International Conference on Robotics and Automation (ICRA’04), 2004, pp. 1740–1745.
A. Salerno and J. Angeles, “On the nonlinear controllability of a quasiholonomic mobile robot,” in 2003 IEEE International Conference on Robotics and Automation, 2003, pp. 3379–3384.
L. Sun and J. Gan, “Researching of two-wheeled self-balancing robot base on LQR combined with PID,” in 2010 2nd International Workshop on Intelligent Systems and Applications, 2010.
A. Blankespoor and R. Roemer, “Experimental verification of the dynamic model for a quarter size self-balancing wheelchair,” in Proceedings of the 2004 American Control Conference, 2004, pp. 488–492.
Z. Chen, Z. Li, and C. L. P. Chen, “Adaptive neural control of uncertain MIMO nonlinear systems with state and input constraints,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 6, pp. 1318–1330, Jun. 2017.
K. Pathak, J. Franch, and S. Agrawal, “Velocity and position control of a wheeled inverted pendulum by partial feedback linearization,” IEEE Transactions on Robotics, vol. 21, no. 3, pp. 505–513, Jun. 2005.
F. Grasser, A. D’Arrigo, S. Colombi, and A. Rufer, “JOE: a mobile, inverted pendulum,” IEEE Transactions on Industrial Electronics, vol. 49, no. 1, pp. 107–114, Feb. 2002.
A. N. K. Nasir, M. A. Ahmad, and R. M. T. R. Ismail, “The control of a highly nonlinear two-wheels balancing robot: A comparative assessment between LQR and PID-PID control schemes,” International Journal of Mechanical and Mechatronics Engineering, vol. 4, no. 10, pp. 942–947, 2010.
D. Nasrallah, H. Michalska, and J. Angeles, “Controllability and posture control of a wheeled pendulum moving on an inclined plane,” IEEE Transactions on Robotics, vol. 23, no. 3, pp. 564–577, Jun. 2007.
J. Wu, W. Zhang, and S. Wang, “A two-wheeled self-balancing robot with the fuzzy PD control method,” Mathematical Problems in Engineering, vol. 2012, 2012, Art. no. 469491.
K. Pawananont and T. Leephakpreeda, “Sequential control of multichannel on–off valves for linear flow characteristics via averaging pulse width modulation without flow meter: An application for pneumatic valves,” Journal of Dynamic Systems, Measurement, and Control, vol. 141, no. 1, Jan. 2019, Art. no. 011007.
K. Pawananont and T. Leephakpreeda, “Experimental investigation and optimal combustion control of untreated landfill gas via fuzzy logic rule knowledge based approach,” Waste Management, vol. 121, pp. 383–392, Feb. 2021.
K. Charoensuk, T. Sethaput, and I. Nilkhamhang, “Electrical modeling of dynamical interaction among intracranial pressure, intraocular pressure, cerebral perfusion pressure, and arterial blood pressure,” in 2019 12th Biomedical Engineering International Conference (BMEiCON), 2019.
C. W. de Silva, Modeling and Control of Engineering Systems. Boca Raton, Florida, USA: CRC Press, 2009.
A. S. Chouhan, D. R. Parhi, and A. Chhotray, “Control and balancing of two-wheeled mobile robots using Sugeno fuzzy logic in the domain of AI techniques,” in Emerging Trends in Engineering, Science and Manufacturing (ETESM-2018), 2018.
M. Yue, W. Sun, and P. Hu, “Sliding mode robust control for two-wheeled mobile robot with lower center of gravity,” International Journal of Innovative Computing, Information and Control, vol. 7, no. 2, pp. 637–646, Feb. 2011.
A. A. Bature, S. Buyamin, M. N. Ahmad, and M. Muhammad, “A comparison of controllers for balancing two wheeled inverted pendulum robot,” International Journal of Mechanical & Mechatronics Engineering, vol. 14, no. 3, pp. 62–68, Jun. 2014.
B. Mahler and J. Haase, “Mathematical model and control strategy of a two-wheeled self-balancing robot,” in IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, 2013, pp. 4198–4203.
C. Gonzalez, I. Alvarado, and D. M. L. Peña, “Low cost two-wheels self-balancing robot for control education,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 9174–9179, Jul. 2017.
Q. Yong, L. Yanlong, Z. Xizhe, and L. Ji, “Balance control of two-wheeled self-balancing mobile robot based on TS fuzzy model,” in Proceedings of 2011 6th International Forum on Strategic Technology, 2011, pp. 406–409.
J. Zhang, T. Zhao, B. Guo, and S. Dian, “Fuzzy fractional-order PID control for two-wheeled selfbalancing robots on inclined road surface,” Systems Science & Control Engineering, vol. 10, no. 1, pp. 289–299, 2022.
S. Khatoon, M. Istiyaque, N. Hasan, and D. K. Chaturvedi, “Two-wheeled self-balancing mobile robot using kalman filter and LQG regulator,” in Recent Advances in Mechanical Engineering, M. Muzammil, A. Chandra, P. K. Kankar, and H. Kumar, Eds. Singapore: Springer, 2021, pp. 213–221.
S. Miasa, M. Al-Mjali, A. A.-H. Ibrahim, and T. A. Tutunji, “Fuzzy control of a two-wheel balancing robot using DSPIC,” in 2010 7th International Multi-Conference on Systems, Signals and Devices, 2010.
L. J. Saud and M. M. Alwan, “Design and implementation of classical sliding mode controller for ball and plate system,” Journal of Engineering, vol. 23, no. 6, pp. 74–92, 2017.
M. Herrera, P. Leica, D. Chávez, and O. Camacho, “A blended sliding mode control with linear quadratic integral control based on reduced order model for a vtol system,” in Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2017), 2017, pp. 606–612.