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Adaptive Tracking Control of Nonholonomic Mobile Manipulators Using Recurrent Neural Networks

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  • Intelligent Control and Applications
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Abstract

The trajectory tracking problem is considered for a class of nonholonomic mobile manipulators in the presence of uncertainties and disturbances. First, under the assumption that the kinematic subsystem of mobile manipulator is capable of being transformed into the chained form and the dynamic subsystem of mobile manipulator is exactly known without considering external disturbances, a model-based controller is designed at the torque level using backstepping design technology. However, the model-based control may be inapplicable for practical applications, as the uncertainties and disturbances do exist in the dynamics of mobile manipulators inevitably. Thus, a Recurrent Neural Network (RNN) based control system is developed without requiring explicit knowledge of the system dynamics. The control system comprises a RNN identifier and a compensation controller, in which the RNN is utilized to identify the unknown dynamics on-line, and the compensation controller is presented to compensate the approximation error and external disturbances. The online adaptive laws of the control system are derived in the Lyapunov sense so that the stability of the system can be guaranteed. Finally, simulation results for a wheeled mobile manipulator are provided to show the good tracking performance and robustness of the proposed control method.

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Author information

Authors and Affiliations

  1. National Engineering Laboratory for Robot Visual Perception and Control Technology, College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Guo Yi, Jianxu Mao, Yaonan Wang & Siyu Guo

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong

    Zhiqiang Miao

Authors
  1. Guo Yi
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  3. Yaonan Wang
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  4. Siyu Guo
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Corresponding author

Correspondence to Jianxu Mao.

Additional information

Recommended by Associate Editor Choon Ki Ahn under the direction of Editor Hamid Reza Karimi. This work was supported by the National Natural Science Foundation of China(61573134, 61433016, 61471167), National Science and Technology Support Program of China(2015BAF13B00).

Guo Yi received his B.S. degree from Hunan Normal University in 2008, his M.S. degree from Hunan University in 2011. He is currently a Ph.D. candidate at the College of Electrical and Information Engineering, Hunan University. His research interest include computer vision, nonlinear systems and control, and cooperative control for multiagent systems.

Jianxu Mao received his B.S. degree from Nanchang University in 1993, an M.S. degrees from East China Institute of Technology in 1999, and a Ph.D. degree from Hunan University in 2003. He is currently an associate professor at the College of Electrical and Information Engineering, Hunan University. His research interest include computer vision, image processing and pattern recognition.

Yaonan Wang received his B.S. degree in computer engineering from East China University of Technology, Fuzhou, China, in 1981, and his M.S. and Ph.D. degrees in control engineering from Hunan University, Changsha, China, in 1990 and 1994, respectively. He was a Post-Doctoral Research Fellow with the National University of Defense Technology, Changsha, from 1994 to 1995, a Senior Humboldt Fellow in Germany from 1998 to 2000, and a Visiting Professor with the University of Bremen, Bremen, Germany, from 2001 to 2004. He has been a Professor with Hunan University since 1995. His current research interests include robot control, intelligent control and information processing, industrial process control, and image processing.

Siyu Guo received the B.S. and Ph.D. degree from Zhejiang University, in 1997 and 2002, respectively. He is currently an associate professor at the College of Electrical and Information Engineering, Hunan University. His present research interests focus on image processing, computer vision, and system modeling and simulation.

Zhiqiang Miao received the B.S. and Ph.D. degrees from the Hunan University, in 2010 and 2016, respectively. He is currently a Post-Doctoral Fellow with the Department of Mechanical and Automation Engineering, Chinese University of Hong Kong. His current research interests include robotics, nonlinear systems and control, and cooperative control for multiagent systems.

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Yi, G., Mao, J., Wang, Y. et al. Adaptive Tracking Control of Nonholonomic Mobile Manipulators Using Recurrent Neural Networks. Int. J. Control Autom. Syst. 16, 1390–1403 (2018). https://doi.org/10.1007/s12555-017-0309-6

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  • DOI: https://doi.org/10.1007/s12555-017-0309-6

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