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. © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Herzallah, R., & Lowe, D. (2002). A novel approach to modelling and exploiting uncertainty in stochastic control systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 801–806). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_130
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