Observer-based PD controller for balancing robot with uncertain model
Corressponding author's email:
tamnm@hcmute.edu.vnKeywords:
PD controller, Matlab/Simulink, LQR, observer, Lyapunov component, Balancing RobotAbstract
This research focuses on the observer-based controller of a balancing robot. Firstly, the dynamic model of the robot is inferred. The uncertainty of the model parameters is then introduced as constraints in state space. Practically, it is very difficult to measure state variables as velocity and acceleration. Moreover, system parameters are not always fixed in operating period. In order to avoid this difficulty, a nonlinear observer associated with the uncertain model system is used to estimate the state variables. A PD control algorithm is proposed in order to achieve the control performances. The exponential stabilities are ensured by Lyapunov techniques. Numerical simulations show the control performance successfully.
Downloads: 0
References
Olfar Boubaker, The inverted Pedulum: a fundamental Benchmark in Control Theory and Robotics, pp 1-6, International Conference on Education and e-Learning Innovations (ICEELI), IEEE, 2012.
Ahmad Nor Kasrudin Nasir, Mohd, Ashraf Ahmad, Riduwan Ghazali, Nasrul Salim Pakheri, Performance Comparison Between Fuzzy Logic Controller (FLC) and PID Controller for a Highly Nonlinear Two-wheels Balancing Robot, pp. 176-181, First International Conference on Informatics and Computaional Intelligence (IEEE), 2011.
Amir A. Bature, Mustafa Muhammad, A comparision of controllers for balancing two-whelled inverted pendulum robot, pp. 62-68, Vol. 14, Issue. 3, International Journal of Mechanical and Mechatronics Engineering, 2014.
Song Xin, Min Gong, Yang Sun, Zhongqiu Zhang, Control system design for two-wheeled self-balanced robot based on Fuzzy-PD control, pp. 169-174, Fifth International Conference on Intelligent Control and Information Processing (IEEE), 2014.
Zhi-hui Li, Yong-li Zhang, Hong-xing Li, Design of Optimal Cost Fuzzy Controller for Spatial Double Inverted Pendulum System, pp. 129-138, Vol. 147, Series of Advances in Intelligent and Soft Computing, Book of Fuzzy Engineering and Operations Research, 2012.
Kwakernaak, Huibert and Sivan, Raphael, Linear Optimal Control Systems, First Edition. Wiley-Interscience. ISBN 0-471-51110-2, 1972.
Fantoni, I. and Lozano, R., Nonlinear Control for Underactuated Mechanical System, Springer-Verlag, London, 2002.
Chenxi Sun, Tao Lu, Kui Yuan, Balance control of two-wheeled self-balancing robot based on Linear Quadrtic Regulator and Neural Network, Fourth International Conference on Intelligent Control and information Processing (ICICIP), IEEE, 2013.
M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.
Quanser, Linear experiment #5: LQR Control, Linear Motion Servo Plants: IP01 or IP02 (Student Hanout).
Downloads
Published
How to Cite
Issue
Section
Categories
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright © JTE.
