SUPERVISED NEURAL NETWORK CONTROL OF REAL-TIME TWO WHEEL INVERTED PENDULUM

Nabil, Hanan

doi:10.21608/jaet.2020.73061

SUPERVISED NEURAL NETWORK CONTROL OF REAL-TIME TWO WHEEL INVERTED PENDULUM

Author

Hanan Nabil

Teaching Assistant Minia University, Faculty of Engineering

10.21608/jaet.2020.73061

Abstract

This research paper investigates an intelligent control technique stabilizing a real-time model ofthe two-wheel inverted pendulum. The TWIP model is a highly non-linear, open-loop, and unstable system which makes control a challenge. Initially,a state-feedback controller that uses the dynamical system states and control signals to construct the precise control decision is used to stabilize the system. Later, Supervised Feed-Forward Neural Networks (SFFNN) based on back propagation Levenberg-Marquardt optimization algorithm are trained by using real-time measurements of system states and motors control signals from state-feedback controller stabilization. SFFNN control the two-wheel inverted pendulum better than a state-feedback controller.

Keywords

References

[1] Ponce, "A REVIEW OF INTELLIGENT CONTROL SYSTEMS APPLIED TO THE INVERTED-PENDULUM PROBLEM," American Journal of Engineering and Applied Sciences American Journal of Engineering and Applied Sciences, vol. 7, pp. 194-240, 2014.

[2] J. Goncalves, N. Gago, C. Arantes, F. Soares, J. S. Esteves, P. Garrido, et al., "A realization of the inverted pendulum and cart," Lect. Notes Eng. Comput. Sci. Lecture Notes in Engineering and Computer Science, vol. 2217, pp. 434-439, 2015.

[3] C.-H. Huang, W.-J. Wang, and C.-H. Chiu, "Design and implementation of fuzzy control on a two-wheel inverted pendulum," IEEE Transactions on Industrial Electronics, vol. 58, pp. 2988-3001, 2011.

[4] F. Grasser, A. D'arrigo, S. Colombi, and A. C. Rufer, "JOE: a mobile, inverted pendulum," IEEE Transactions on industrial electronics, vol. 49, pp. 107-114, 2002.

[5] O. Boubaker, "The inverted pendulum: history and survey of open and current problems in control theory and robotics," TheInverted Pendulum in Control Theory and Robotics: From Theory to New Innovations, p. 1, 2017.

[6] R. P. M. Chan, K. A. Stol, and C. R. Halkyard, "Review of modelling and control of two-wheeled robots," Annual Reviews in Control, vol. 37, pp. 89-103, 2013.

[7] A. A. Bature, S. Buyamin, M. N. Ahmad, M. Muhammad, and A. A. Muhammad, "Identification and model predictive position control of Two Wheeled Inverted Pendulum mobile robot," J. Teknol, vol. 73, pp. 153-156, 2015.

[8] S. Nawawi, M. Ahmad, and J. Osman, "Real-time control of a two-wheeled inverted pendulum mobile robot," World Academy of Science, Engineering and Technology, vol. 39, pp. 214-220, 2008.

[9] J. Gonçalves, N. Gago, C. Arantes, F. Soares, G. Lopes, J. S. Esteves, et al., "A Realization of the Inverted Pendulum and Cart," in Proceedings of the World Congress on Engineering, 2015.

[10] H.-J. Lee and S. Jung, "Gyro sensor drift compensation by Kalman filter to control a mobile inverted pendulum robot system," in Industrial Technology, 2009. ICIT 2009. IEEE International Conference on, 2009, pp. 1-6.

[11] V. Mladenov, "Application of neural networks for control of inverted pendulum," WSEAS Transactions on Circuits and Systems, vol. 10, pp. 49-58, 2011.

[12] T. Callinan, "Artificial Neural Network identification and control of the inverted pendulum," MEng Project Reports, School of Electronic Engineering Dublin City University, 2003.

[13] S. Jung and S. S. Kim, "Control experiment of a wheel-driven mobile inverted pendulum using neural network," IEEE Transactions on Control Systems Technology, vol. 16, pp. 297-303, 2008.

[14] K. Pathak, J. Franch, and S. K. Agrawal, "Velocity and position control of a wheeled inverted pendulum by partial feedback linearization," IEEE Transactions on robotics, vol. 21, pp. 505-513, 2005.

[15] J. S. Noh, G. H. Lee, and S. Jung, "Position control of a mobile inverted pendulum system using radial basis function network," in Neural Networks, 2008. IJCNN 2008.(IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on, 2008, pp. 370-376.

[16] C. Yang, Z. Li, R. Cui, and B. Xu, "Neural network-based motion control of an underactuated wheeled inverted pendulum model," IEEE Transactions on Neural Networks and Learning Systems, vol. 25, pp. 2004-2016, 2014.

[17] C.-C. Tsai, H.-C. Huang, and S.-C. Lin, "Adaptive neural network control of a self-balancing two-wheeled scooter," IEEE Transactions on Industrial Electronics, vol. 57, pp. 1420-1428, 2010.

[18] Z. Li and C. Yang, "Neural-adaptive output feedback control of a class of transportation vehicles based on wheeled inverted pendulum models," IEEE Transactions on Control Systems Technology, vol. 20, pp. 1583-1591, 2012.

[19] C. Yang, Z. Li, and J. Li, "Trajectory planning and optimized adaptive control for a class of wheeled inverted pendulum vehicle models," IEEE Transactions on Cybernetics, vol. 43, pp. 24-36, 2013.

[20] S. Jung and S. su Kim, "Hardware implementation of a real-time neural network controller with a DSP and an FPGA for nonlinear systems," IEEE Transactions on Industrial Electronics, vol. 54, pp. 265-271, 2007.

[21] R. C. Ooi, "Balancing a two-wheeled autonomous robot," University of Western Australia, vol. 3, 2003.

[22] S. Jadlovska and J. Sarnovsky, "A complex overview of modeling and control of the rotary single inverted pendulum system," Advances in Electrical and Electronic Engineering, vol. 11, p. 73, 2013.

[23] M. T. Hagan and M. B. Menhaj, "Training feedforward networks with the Marquardt algorithm," IEEE transactions on Neural Networks, vol. 5, pp. 989-993, 1994.

[24] B. C. Aissa and C. Fatima, "Neural Networks Trained with Levenberg-Marquardt-Iterated Extended Kalman Filter for Mobile Robot Trajectory Tracking," Journal of Engineering Science & Technology Review, vol. 10, 2017.

[25] R. Battiti, "First-and second-order methods for learning: between steepest descent and Newton's method," Neural computation, vol. 4, pp. 141-166, 1992.

[26] B. M. Wilamowski, "Neural network architectures and learning algorithms," IEEE Industrial Electronics Magazine, vol. 3, 2009.

[27] H. Yu and M. Bogdan, "Levenberg-Marquardt training," in Electrical Engineering Handbook intelligent systems, ed:

, 2011, pp. 1-16

[28] D. W. Marquardt, "An algorithm for least-squares estimation of nonlinear parameters," Journal of the society for Industrial and Applied Mathematics, vol. 11, pp. 431-441, 1963.

[29] J. J. Callahan, Advanced calculus: a geometric view: Springer Science & Business Media, 2010.

[30] Y. N. Dauphin, R. Pascanu, C. Gulcehre, K. Cho, S. Ganguli, and Y. Bengio, "Identifying and attacking the saddle point problem in high-dimensional non-convex optimization," in Advances in neural information processing systems, 2014, pp. 2933-2941.

[31] S. Wright and J. Nocedal, "Numerical optimization," Springer Science, vol. 35, p. 7, 1999.

[32] A. A. Suratgar, M. B. Tavakoli, and A. Hoseinabadi, "Modified Levenberg-Marquardt method for neural networks training," World Acad Sci Eng Technol, vol. 6, pp. 46-48, 2005.

[33] B. M. Wilamowski and H. Yu, "Improved computation for Levenberg–Marquardt training," IEEE transactions on neural networks, vol. 21, pp. 930-937, 2010.