Abstract
In control theory, in order to meet certain performance objectives with less precise advanced knowledge of system dynamics, it is necessary to develop control algorithms with high levels of autonomy. Learning control systems are good examples of such highly autonomous controllers. Within this class of controllers, neurologically inspired control algorithms have been gaining much attention in recent years. Artificial Neural Network (ANN) based control algorithms have been successfully applied for the tracking control of nonlinear systems with unknown or changing dynamics [1, 2, 3]. There is also much research on the stability properties of these, rather unconventional, control algorithms [4, 5]. Performance of ANN based controllers is mainly due to these networks’ remarkable capabilities of approximating arbitrary nonlinear functions over compact spaces. Fault tolerance is also another much pronounced property of ANNs [6].
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References
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© 1993 Springer-Verlag Berlin Heidelberg
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Ciliz, K. (1993). Robustness and Fault Tolerance Issues In Artificial Neural Network Based Robotic Control Applications. In: Kaynak, O., Honderd, G., Grant, E. (eds) Intelligent Systems: Safety, Reliability and Maintainability Issues. NATO ASI Series, vol 114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58021-5_14
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DOI: https://doi.org/10.1007/978-3-642-58021-5_14
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