Titlebook: Robust and Fault-Tolerant Control; Neural-Network-Based Krzysztof Patan Book 2019 Springer Nature Switzerland AG 2019 Control System Synthe

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maintenance

Iterative Learning Control, system. Additionally, the chapter contains both the stability and convergence analysis of the proposed nonlinear ILC. The portrayed control strategies are tested on the examples of a pneumatic servomechanism and a magnetic suspension system.

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Concluding Remarks and Further Research Directions,ed approaches mainly use the capability of a neural network to learn from historical data and to approximate nonlinear functions with an assumed accuracy. These two properties are extremely useful when dealing with nonlinear industrial plants for which a mathematical model is unknown or is very expensive to determine.

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2198-4182 robust and fault-tolerant control.Allows the reader easy ac.Robust and Fault-Tolerant Control.?proposes novel automatic control strategies for nonlinear systems developed by means of artificial neural networks and pays special attention to robust and fault-tolerant approaches. The book discusses ro

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Robust and Fault-Tolerant Control,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 c

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Model Predictive Control,pter is devoted to nonlinear predictive control developed by means of neural networks. Some of the most important issues connected with optimization and stability are investigated in detail. The next part introduces the sensor fault-tolerant control (For this purpose, predictive control is equipped

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Control Reconfiguration,m detects a fault, estimates it and corrects a control law in order to compensate the fault effect observed in the control system. In order to create a control system, it is necessary to take into account the model of a plant as well as the state observer. Both models are designed using neural netwo

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