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Project
Modeling of complex time-varying dynamical systems. (FWOAL599)
Modelling dynamical systems from noisy observations is a basic and crucial step in many disciplines of science. In quite some applications the dynamical behaviour of the system changes with time which complicates its analysis and modelling. Think, for example, of system dynamics changes due to
* varying set points (e.g. in flight flutter dynamics),
* external influences causing system parameter variations (e.g. temperature changes, variations of the length of the gripper arm of a pick-and-place robot),
* aging/mortification in biological systems (e.g. the in-vitro analysis of the ear dynamics).
The key contribution of this research is the development of identification algorithms for the accurate modelling of complex time-varying dynamical systems from noisy input-output observations (= errors-in-variables problem), keeping in mind the intended application (physical interpretation, prediction, or control). The final result is a validated model with reliable error bounds. Three practical applications will be studied in detail
* modelling of pick-and-place robot with flexible extendable gripper arm for accurate point-to-point control,
* modelling of flight flutter for physical insight and for flutter control,
* modelling of the ear dynamics via in-vitro measurements for better understanding its functioning.
This implies a proper combination of the skills and specialized knowledge of the experts in the field with the system identification theory.
* varying set points (e.g. in flight flutter dynamics),
* external influences causing system parameter variations (e.g. temperature changes, variations of the length of the gripper arm of a pick-and-place robot),
* aging/mortification in biological systems (e.g. the in-vitro analysis of the ear dynamics).
The key contribution of this research is the development of identification algorithms for the accurate modelling of complex time-varying dynamical systems from noisy input-output observations (= errors-in-variables problem), keeping in mind the intended application (physical interpretation, prediction, or control). The final result is a validated model with reliable error bounds. Three practical applications will be studied in detail
* modelling of pick-and-place robot with flexible extendable gripper arm for accurate point-to-point control,
* modelling of flight flutter for physical insight and for flutter control,
* modelling of the ear dynamics via in-vitro measurements for better understanding its functioning.
This implies a proper combination of the skills and specialized knowledge of the experts in the field with the system identification theory.
Date:1 Jan 2011 → 31 Dec 2014
Keywords:Automatic Measurement Systems, Nonlinear Modelling, Medical Physics, Fibre Optic, Parameter Estimation, Microwaves, Instrumentation, Underwater Acoustics, System Identification, Telecommunications, Electrical Measurements, Nonlinear Measurements, Electromagnetism
Disciplines:Electrical and electronic engineering, Mathematical sciences and statistics, (Bio)medical engineering