Abstract
This paper presents a neural network based control design to handle the stabilization of a class of multiple input nonlinear systems with time varying uncertain parameters while assuming that the range of each individual uncertain parameter is known. The proposed design approach allows incorporation of complex control performance measures and physical control constraints whereas the traditional adaptive control techniques are generally not applicable. The desired system dynamics are analyzed, and a collection of system dynamics data, that represents the desired system behavior and approximately covers the region of stability interest, is generated and used in the construction of the neural controller based on the proposed neural control design. Furthermore, the theoretical aspects of the proposed neural controller are also studied, which provides insightful justification of the proposed neural control design. The simulation study is conducted on a single-machine infinity-bus (SMIB) system with time varying uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective.
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© 2007 Springer-Verlag Berlin Heidelberg
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Chen, D., Yang, J., Mohler, R.R. (2007). Neural Control Applied to Time Varying Uncertain Nonlinear Systems. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_23
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DOI: https://doi.org/10.1007/978-3-540-72383-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
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