Print Email Facebook Twitter Robust optimal sensor placement for optimal estimation Title Robust optimal sensor placement for optimal estimation: A framework and application to adaptive optics Author de Groot, Sjoerd (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Delft Center for Systems and Control) Contributor Verhaegen, M.H.G. (mentor) Doelman, R. (graduation committee) Ferrari, Riccardo M.G. (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering | Systems and Control Date 2018-10-24 Abstract The goal of this thesis is to present a framework which can be used to optimize the observer and controller performance by placement of sensors and actuators on a continuous domain. The framework provides choices of model structure, cost functions and optimization methods to perform the placement. Systems which would be suitable for the use of the framework are for example adaptive optics systems, active dampening of windmill blades or climate control systems in buildings. To demonstrate the use of the framework, two applications are presented. Both applications are sensor placement for an adaptive optics system. The first implementation uses a Linear Time Invariant (LTI) state space model and uses a Vector Autoregressive (VAR) approach for identification. The second implementation uses a Linear Parameter Varying (LPV) state space model with VAR identification approach. The second implementation is meant to demonstrate how the placement can be made more robust to changing conditions. Finally we show how parametrization of the sensor locations can be used to gain insight into what properties an optimal placement exhibits. Subject ControlOptimizationAdaptive Optics To reference this document use: http://resolver.tudelft.nl/uuid:7fe5a511-81c9-44e5-90ba-a2b479011077 Part of collection Student theses Document type master thesis Rights © 2018 Sjoerd de Groot Files PDF Msc_Thesis_Sjoerd_de_Groo ... _FINAL.pdf 2.06 MB Close viewer /islandora/object/uuid:7fe5a511-81c9-44e5-90ba-a2b479011077/datastream/OBJ/view