Trajectory tracking of a wheeled mobile robot with uncertainties and disturbances: proposed adaptive neural control

• Autorzy: Martins N. A. , Alencar M. , Lombardi W. C. , Bertol D. W. , Pieri E. R. , Filho H. F.
• Języki publikacji: EN

Abstrakty

EN

This paper analyses a trajectory tracking control problem for a wheeled mobile robot, Rusing integration of a kinematic neural controller (KNC) and a torque neural controller (TNC), in which both the kinematic and dynamic models contain uncertainties and disturbances. The proposed adaptive neural controller (PANC) is composed of the KNC and the TNC and is designed with use of a modeling technique of Gaussian radial basis function neural networks (RBFNNs). The KNC is a variable structure controller, based on the sliding mode theory and is applied to compensate for the disturbances of the wheeled mobile robot kinematics. The TNC is an inertia-based controller composed of a dynamic neural controller (DNC) and a robust neural compensator (RNC) applied to compensate for the wheeled mobile robot dynamics, bounded unknown disturbances, and neural network modeling errors. To minimize the problems found in practical implementations of the classical variable structure controllers (VSC) and sliding mode controllers (SMC), and to eliminate the chattering phenomenon, the nonlinear and continuous KNC and RNC of the TNC are applied in lieu of the discontinuous components of the control signals that are present in classical forms. Additionally, the PANC neither requires the knowledge of the wheeled mobile robot kinematics and dynamics nor the timeconsuming training process. Stability analysis, convergence of the tracking errors to zero, and the learning algorithms for the weights are guaranteed based on the Lyapunov method. Simulation results are provided to demonstrate the effectiveness of the proposed approach.

Słowa kluczowe

EN

wheeled mobile robot; trajectory tracking; kinematic control; variable structure control; dynamic control; sliding mode theory; neural networks; Lyapunov theory

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2015
• Tom: Vol. 44, no. 1
• Strony: 47--98
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Martins N. A.

• Universidade Estadual de Maring´a, Departamento de Inform´atica, Avenida Colombo, 5790, CEP 87020-900, Maring´a, PR, Brasil

autor

Alencar M.

autor

Lombardi W. C.

• Lyon Universit´e, INSA - Institut National des Sciences Appliqu´ees, 20, avenue Albert Einstein, 69621 Villeurbanne Cedex, France

autor

Bertol D. W.

• Universidade Federal de Santa Catarina, Departamento de Automa¸cao e Sistemas, Programa de Pos Gradua¸cao em Engenharia de Automa¸cao e Sistemas, Caixa Postal 476, CEP 88040-900, Florian´opolis, SC, Brasil

autor

Pieri E. R.

• Universidade Federal de Santa Catarina, Departamento de Automa¸cao e Sistemas, Programa de Pos Gradua¸cao em Engenharia de Automa¸cao e Sistemas, Caixa Postal 476, CEP 88040-900, Florian´opolis, SC, Brasil

autor

Filho H. F.

• Universidade Estadual Paulista Ju´lio de Mesquita Filho, Faculdade de Ciencias, Departamento de Computa¸cao, Avenida Luiz Edmundo Carrijo Coube, Caixa Postal 473, CEP 17033-360, Bauru, SP, Brasil

Bibliografia

• 1. GULDNER, J. and UTKIN, V. I. (1994) Stabilization of Nonholonomic Mobile Robots using Lyapunov Functions for Navigation and Sliding Mode Control. In: Proceedings of the 33rd IEEE Conference on Decision and Control , IEEE, 3, 2967-2972.
• 2. HAYKIN, S. O. (2008) Neural Networks and Learning Machines. Third Edition. Prentice Hall, Upper Saddle River, New Jersey, USA.
• 3. HU, T., and YANG, S. X. (2001) An Efficient Neural Controller for a Nonholonomic Mobile Robot. In: Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation. IEEE, 461-466.
• 4. JIN, YAOCHU and SENDHOFF, BERNHARD (2003) Extracting Interpretable Fuzzy Rules from RBF Networks. Neural Processing Letters 17 (2), 149-164.
• 5. KUNPENG, L., XUEWEN, W., MINGXIN, Y., XIAOHU, L. and SUNAN, W.(2009) Adaptive Sliding Mode Trajectory Tracking Control of Mobile Robot with Parameter Uncertainties. In: Proceedings of the 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA). IEEE, 148-152.
• 6. LEE, J. H., LIN, C., LIM, H. and LEE, J. M. (2009) Sliding Mode Control for Trajectory Tracking of Mobile Robot in the RFID Sensor Space. International Journal of Control, Automation, and Systems 7 (3), 429 436.
• 7. LEWIS, F., DAWSON, D. and ABDALLAH, C. (2004) Robot Manipulator Control: Theory and Practice. CRC Press, New York.
• 8. LI, Y., QIANG, S., ZHUANG, X. and KAYNAK, O. (2004) Robust and Adaptive Backstepping Control for Nonlinear Systems using RBF Neural Networks. IEEE Transactions on Neural Networks 15 (3), 693 701.
• 9. LI, Y., ZHU, L., WANG, Z. and LIU, T. (2009) Trajectory Tracking for Nonholonomic Wheeled Mobile Robots based on an Improved Sliding Mode Control Method. In: Proceedings of the International Colloquium on Computing, Communication, Control, and Management. IEEE, 2, 55–58.
• 10. LIU, Y., ZHANG, Y. and WANG, H. (2011) Tracking Control of Wheeled mobile Robots Based on Sliding-mode Control. In: Proceedings of the 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC). IEEE, 1787-1790.
• 11. MARTINS, N. A., ALENCAR, M. DE, LOMBARDI, W. C., BERTOL, D. W., DE PIERI, E. R. and FERASOLI FILHO, H. (2012) A Proposed Neural Control for the Trajectory Tracking of a Nonholonomic Mobile Robot with Disturbances. In: Artificial Neural Networks and Machine Learning - Proceedings of the International Conference on Artificial Neural Networks (ICANN), A.E.P. Villa et al. (eds.), Part I, Lecture Notes in Computer Science (LNCS) 7552. Springer-Verlag, Berlin, Heidelberg, 330-338. MORIN, P. and SAMSON, C. (2008) Motion Control of Wheeled Mobile Robots. Handbook of Robotics. Springer-Verlag, Berlin, Heidelberg, 799826.
• 12. OH, C., KIM, M.-S. and LEE, J.-J. (2004) Control of a Nonholonomic Mobile Robot Using an RBF Network. Journal Artificial Life and Robotics 8 (1), 14-19. Springer, Japan.
• 13. ORIOLO, G., DE LUCA, A. and VENDITTELLI, M. (2002) WMR Control Via Dynamic Feedback Linearization: Design, Implementation, and Experimental Validation. IEEE Transactions on Control Systems Tech nology 10 (6), 835-852.
• 14. PARK, B. S., YOO, S. J., PARK, J. B. and CHOI, Y. H. (2009) Adaptive Neural Sliding Mode Control of Nonholonomic Wheeled Mobile Robots with Model Uncertainty. IEEE Transactions on Control Systems Technology 17 (1), 207-214.
• 15. PASSOLD, F. (2009) Applying RBF Neural Nets for Position Control of an Inter/Scara Robot. International Journal of Computers, Communications and Control 4 (2), 148-157.
• 16. SHIM, H.-S., KIM, J. H. and KOH, K. (1995) Variable Structure Control of Nonholonomic Wheeled Mobile Robot. In: Proceedings of the 1995 IEEE International Conference on Robotics and Automation. IEEE, 2, 16941699.
• 17. SHUWEN, P., HONGYE, S., XIEHE, H. and JIAN, C. (2000) Variable Structure Control Theory and Application: A Survey. In: Proceedings of the 3rd World Congress on Intelligent Control and Automation. IEEE, 4, 2977-2981.
• 18. SILVEIRA J´UNIOR, A. V. DA and HEMERLY, E. M. (2004) Control of Mobile Robots Via Biased Wavelet Networks. Learning and Nonlinear Models 2 (2), 84-98.
• 19. SOLEA, R., FILIPESCU, A. and NUNES, U. (2009) Sliding-Mode Control for Trajectory-Trackingof a Wheeled Mobile Robot in Presence of Uncertainties. In: Proceedings of the 7th Asian Control Conference. IEEE, 1701-1706.
• 20. SOUSA JR., C., HEMERLY, E. M. and GALVAO, R. K. H. (2002) Adaptive Control for Mobile Robot Rusing Wavelet Networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B 32 (4), 493 504.
• 21. UTKIN, V., GULDNER, J. and SHI, J. (2009) Sliding Mode Control in Eletro-Mechanical Systems. Second Edition. CRC Press, Taylor & Francis Group, Boca Raton, Florida. YANG, J.-M. and KIM, J.-H. (1999a) Sliding Mode Control for Trajectory Tracking of Nonholonomic Wheeled Mobile Robots. IEEE Transactions on Robotics and Automation 15 (3), 578-587.
• 22. YANG, J.-M. and KIM, J.-H. (1999b) Sliding Mode Motion Control of Nonholonomic Mobile Robots. IEEE Control Systems Magazine 19 (2), 15-23. YU, X. and KAYNAK, O. (2009) Sliding-Mode Control With Soft Computing: A Survey. IEEE Transactions on Industrial Electronics 56 (9), 3275-3285.