Shinung Ching

Quasilinear Control (eBook, PDF)

Performance Analysis and Design of Feedback Systems with Nonlinear Sensors and Actuators

• Format: PDF

Produktbeschreibung

This is a textbook and reference for readers interested in quasilinear control (QLC). QLC is a set of methods for performance analysis and design of linear plant or nonlinear instrumentation (LPNI) systems. The approach of QLC is based on the method of stochastic linearization, which reduces the nonlinearities of actuators and sensors to quasilinear gains. Unlike the usual - Jacobian linearization - stochastic linearization is global. Using this approximation, QLC extends most of the linear control theory techniques to LPNI systems. A bisection algorithm for solving these equations is provided. In addition, QLC includes new problems, specific for the LPNI scenario. Examples include Instrumented LQR/LQG, in which the controller is designed simultaneously with the actuator and sensor, and partial and complete performance recovery, in which the degradation of linear performance is either contained by selecting the right instrumentation or completely eliminated by the controller boosting.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 6. Dezember 2010
• Englisch
• ISBN-13: 9781139180849
• Artikelnr.: 38277409

Autorenporträt

ShiNung Ching is a Postdoctoral Fellow at the Neurosciences Statistics Research Laboratory at MIT, where he has been since completing his Ph.D. in Electrical Engineering at the University of Michigan. His research involves a systems theoretic approach to anesthesia and neuroscience, looking to use mathematical techniques and engineering approaches - such as dynamical systems, modeling, signal processing and control theory - to offer new insights into the mechanisms of the brain. By combining physiology with technical tools, this 'systems neuroscience' approach to neurodynamics promises to suggest novel medical and pharmacological strategies and technologies.

Inhaltsangabe

1. Introduction
2. Stochastic linearization of LPNI systems
3. Analysis of reference tracking in LPNI systems
4. Analysis of disturbance rejection in LPNI systems
5. Design of reference tracking controllers for LPNI systems
6. Design of disturbance rejection controllers for LPNI systems
7. Performance recovery in LPNI systems
8. Proofs.