Peter J. Seiler

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Probabilistic Performance Analysis of Fault Diagnosis Schemes The dissertation explores the problem of rigorously quantifying the performance of a fault diagnosis scheme in terms of probabilistic performance metrics. Typically, when the performance of a fault diagnosis scheme is of utmost importance, physical redundancy is used to create a highly reliable(More)
LPVTools is a MATLAB toolbox that is being developed to perform gain-scheduled Linear Parameter-Varying (LPV) control of aeroservoelastic systems. This paper outlines the LPV modeling, analysis and controller synthesis features of this toolbox. The features of the toolbox are illustrated by an application example, where a grid-based LPV model is developed(More)
This paper investigates the use of disturbance models in the design of wind turbine individual pitch controllers. Previous work has used individual pitch control and disturbance models with the Multiblade Coordinate Transformation to design controllers that reduce the blade loads at the frequencies associated with the rotor speed. This paper takes a similar(More)
— Determining the induced L2 norm of a linear, parameter-varying (LPV) system is an integral part of many analysis and robust control design procedures. In general, this norm cannot be determined explicitly. Most prior work has focused on efficiently computing upper bounds for the induced L2 norm. This paper presents a complementary algorithm to compute(More)
Adaptive control has the potential to improve performance and reliability in aircraft. Implementation of adaptive control on commercial and military aircraft requires verification and validation of the control system's robustness to modeling error, uncertainty, and time delay. Currently, there is a lack of tools available to rigorously analyze the(More)
LPVTools is a toolbox aimed at helping users design parameter dependent control systems using the Linear Parameter-Varying framework (LPV). LPVTools contains data structures to represent LPV systems in MATLAB® and Simulink®, and a collection of functions and tools for model reduction, analysis, synthesis and simulation in the LPV framework.
Structural health monitoring of wind turbine blade mechanical performance can inform maintenance decisions, lead to reduced down time and improve the reliability of wind turbines. Wireless, self-powered strain gages and accelerometers have been proposed to transmit blade data to a monitoring system located in the nacelle. Each sensor node is powered by a(More)