Neural Network-Based State Estimation of Nonlinear Systems, 2010
Application to Fault Detection and Isolation
Lecture Notes in Control and Information Sciences Series, Vol. 395

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Language: English

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154 p. · 15.5x23.5 cm · Paperback

"Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.

Neural Network-Based State Estimation Schemes.- Neural Network-Based System Identification Schemes.- An Actuator Fault Detection and Isolation Scheme: Experiments in Robotic Manipulators.- A Robust Actuator Gain Fault Detection and Isolation Scheme.- A Robust Sensor and Actuator Fault Detection and Estimation Approach.

Presents both the Linear-in-Parameter Neural Network based observer and the Nonlinear-in-Parameter Neural Network based observer approaches to nonlinear systems

Discusses the neural network structure for fault detection actuators using an application to satellite attitude control systems and robotic manipulators

Discusses robust sensor and actuator fault detection and estimation

Includes supplementary material: sn.pub/extras