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Robust and Fault-Tolerant Control

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Robust and Fault-Tolerant Control

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 197))

Abstract

The chapter introduces the reader into the theory of automatic control, focusing on nonlinear control schemes developed by means of artificial neural networks. It contains essential information on direct control based on neural networks, model reference adaptive control, feed-forward control, model-predictive control and optimal control. The role played by neural networks in each control scheme is emphasized. Since a desirable feature of modern control systems is some level of robustness and fault tolerance, the next two parts of the chapter discuss the problem of robust control as well as fault-tolerant control. The main objective of these parts is to present the existing approaches to achieving robustness and fault tolerance of control systems and to point out their drawbacks. It can be argued that the use of neural networks can help in solving a number of problems observed in the described methods.

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Authors and Affiliations

  1. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Krzysztof Patan

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  1. Krzysztof Patan
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Patan, K. (2019). Robust and Fault-Tolerant Control. In: Robust and Fault-Tolerant Control. Studies in Systems, Decision and Control, vol 197. Springer, Cham. https://doi.org/10.1007/978-3-030-11869-3_3

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