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Neural network-based adaptive output feedback control for MIMO non-affine systems

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Abstract

An adaptive output feedback control scheme is proposed for a class of multi-input-multi-output (MIMO) non-affine nonlinear systems in which the output signal can track the reference signal. In the systems, the relative degree of the regulated output is assumed to be known. A state observer is constructed to estimate the unknown state in the systems. A neural network (NN) is introduced to compensate the modeling errors, and a robust control is also used to reduce the approximation error, which improves the capacity of resisting disturbance of the systems. The stability of the systems is rigidly proved through Lyapunov’s direct method. Simulation results demonstrate the effectiveness and feasibility of proposed scheme.

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Acknowledgments

This research is supported by Shandong natural science fund project (Y2007G06).

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Authors and Affiliations

  1. Department of Automatic Control, Qingdao University of Science and Technology, Qingdao, China

    Zhao Tong & Fan Feng-li

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  1. Zhao Tong
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  2. Fan Feng-li
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Correspondence to Zhao Tong.

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Tong, Z., Feng-li, F. Neural network-based adaptive output feedback control for MIMO non-affine systems. Neural Comput & Applic 21, 145–151 (2012). https://doi.org/10.1007/s00521-011-0638-y

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  • DOI: https://doi.org/10.1007/s00521-011-0638-y

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