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Learning from NN output feedback control of nonlinear systems in Brunovsky canonical form

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Abstract

In this paper, we investigate the learning issue in the adaptive neural network (NN) output feedback control of nonlinear systems in Brunovsky canonical form with unknown affine term. With only output measurements, a high-gain observer (HGO) is employed to estimate the derivatives of the system output which may be associated with the generation of peaking phenomenon. The adverse effect of peaking on learning and its elimination strategies are analyzed. When the gain of HGO is chosen too high, it may cause the failure of learning from the unknown closed-loop system dynamics. Hence, the gain of HGO is not chosen too high to relieve peaking and guarantee the accuracy of the estimated system states. Then, learning from the unknown closed-loop system dynamics can be achieved. When repeating the same or similar control tasks, a neural learning controller is presented which can effectively recall and reuse the learned knowledge to guarantee the output tracking performance. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

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Authors and Affiliations

  1. School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou Guangdong, 510640, China

    Wei Zeng

  2. School of Physics and Mechanical & Electrical Engineering, Longyan University, Longyan Fujian, 364000, China

    Wei Zeng

  3. School of Automation Science and Engineering, South China University of Technology, Guangzhou Guangdong, 510640, China

    Cong Wang

Authors
  1. Wei Zeng
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  2. Cong Wang
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Corresponding author

Correspondence to Cong Wang.

Additional information

Wei ZENG received his M.E. degree from the Department of Automation, Xiamen University, Xiamen, China, in 2008, and Ph.D. degree from the College of Automation Science and Engineering, South China University of Technology, Guangzhou, China, in 2012. Currently, he is a postdoctoral fellow at South China University of Technology. His current research interests include deterministic learning theory, adaptive NN control and dynamical pattern recognition.

Cong WANG received his B.E. and M.E. degrees from Beijing University of Aeronautic and Astronautics, Beijing, China, in 1989 and 1997, respectively, and the Ph.D. degree from the National University of Singapore, Singapore, in 2002. Currently, he is a professor at the College of Automation Science and Engineering, South China University of Technology, Guangzhou, China. His current research interests include adaptive NN control/identification, deterministic learning theory, dynamical pattern recognition, pattern-based intelligent control, oscillation fault diagnosis, and cognitive and brain sciences.

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Zeng, W., Wang, C. Learning from NN output feedback control of nonlinear systems in Brunovsky canonical form. J. Control Theory Appl. 11, 156–164 (2013). https://doi.org/10.1007/s11768-013-1124-0

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  • DOI: https://doi.org/10.1007/s11768-013-1124-0

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