Ebook: Robust Estimation and Failure Detection: A Concise Treatment

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. This monograph brings together two of the most exciting areas of advanced signal processing and control. The first involves the development of optimal estimators for uncertain signal and noise models. The second is the subject of failure detection and isolation, which has considerable potential in a range of applications. The text provides a gradual build-up of ideas moving from traditional Wiener and Kalman fIltering to risk sensitive control and estimation problems.

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This work presents a concise treatment of robust estimation, with a thorough presentation of Kalman filtering. The robust game theoretic/ H? filtering theory is developed, making it possible to design estimators that are more general than Kalman filters and are robust to model uncertainties and rapid model variations. It also reviews the likelihood ratio method for failure detection and demonstrates how robust filters can enhance such methods by enabling the design of failure detectors that are sensitive to failures but insensitive to model uncertainties and/or rapid model variations. Robust Estimation and Failure Detection is of particular value to students, researchers and engineers with an interest in filtering or failure detection, offering classical and advanced theories and design methods and allowing them to benefit from the robust control theoretic developments of the last fifteen years. Control researchers and engineers will also find it relevant, as it demonstrates how development in their discipline affects these two neighbouring fields.

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This work presents a concise treatment of robust estimation, with a thorough presentation of Kalman filtering. The robust game theoretic/ H? filtering theory is developed, making it possible to design estimators that are more general than Kalman filters and are robust to model uncertainties and rapid model variations. It also reviews the likelihood ratio method for failure detection and demonstrates how robust filters can enhance such methods by enabling the design of failure detectors that are sensitive to failures but insensitive to model uncertainties and/or rapid model variations. Robust Estimation and Failure Detection is of particular value to students, researchers and engineers with an interest in filtering or failure detection, offering classical and advanced theories and design methods and allowing them to benefit from the robust control theoretic developments of the last fifteen years. Control researchers and engineers will also find it relevant, as it demonstrates how development in their discipline affects these two neighbouring fields.
Content:
Front Matter....Pages i-xvii
Introduction....Pages 1-3
Estimation and Failure Detection: An Overview....Pages 5-41
Discrete-Time Robust Estimation....Pages 43-84
Stochastic Interpretation of Robust Estimation: Risk Sensitivity....Pages 85-97
Robust Failure Detection and Isolation....Pages 99-129
Two Applications....Pages 131-150
Back Matter....Pages 151-222

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This work presents a concise treatment of robust estimation, with a thorough presentation of Kalman filtering. The robust game theoretic/ H? filtering theory is developed, making it possible to design estimators that are more general than Kalman filters and are robust to model uncertainties and rapid model variations. It also reviews the likelihood ratio method for failure detection and demonstrates how robust filters can enhance such methods by enabling the design of failure detectors that are sensitive to failures but insensitive to model uncertainties and/or rapid model variations. Robust Estimation and Failure Detection is of particular value to students, researchers and engineers with an interest in filtering or failure detection, offering classical and advanced theories and design methods and allowing them to benefit from the robust control theoretic developments of the last fifteen years. Control researchers and engineers will also find it relevant, as it demonstrates how development in their discipline affects these two neighbouring fields.
Content:
Front Matter....Pages i-xvii
Introduction....Pages 1-3
Estimation and Failure Detection: An Overview....Pages 5-41
Discrete-Time Robust Estimation....Pages 43-84
Stochastic Interpretation of Robust Estimation: Risk Sensitivity....Pages 85-97
Robust Failure Detection and Isolation....Pages 99-129
Two Applications....Pages 131-150
Back Matter....Pages 151-222
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