Abstract
We consider an inversion-based neurocontroller for solving control problems of uncertain nonlinear systems. Classical approaches do not use uncertainty information in the neural network models. In this paper we show how we can exploit knowledge of this uncertainty to our advantage by developing a novel robust inverse control method. Simulations on a nonlinear uncertain second order system illustrate the approach.
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© 2002 Springer-Verlag Berlin Heidelberg
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Herzallah, R., Lowe, D. (2002). A Novel Approach to Modelling and Exploiting Uncertainty in Stochastic Control Systems. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_130
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DOI: https://doi.org/10.1007/3-540-46084-5_130
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