Stability is a fundamental concept in control theory, as it ensures that system responses remain bounded, guaranteeing safe operation. Conventional methods for stability analysis are based on parametric system models represented mainly by transfer functions or state-space equations. More recently, data-driven, non-parametric models have gained significant attention in research. These methods use raw input-output data to model systems without explicit parametric descriptions, offering advantages for complex or poorly understood systems. However, existing stability conditions for this model primarily apply to linear time-invariant systems, and extending them to time-varying systems remains an open challenge. This project aims to explore and develop stability conditions for time-varying systems modeled using data-driven approaches. The methods will be validated on a numerical case study.
Stability is a fundamental concept in control theory, as it ensures that system responses remain bounded, guaranteeing safe operation. Conventional methods for stability analysis are based on parametric system models represented mainly by transfer functions or state-space equations. More recently, data-driven, non-parametric models have gained significant attention in research. These methods use raw input-output data to model systems without explicit parametric descriptions, offering advantages for complex or poorly understood systems. However, existing stability conditions for this model primarily apply to linear time-invariant systems, and extending them to time-varying systems remains an open challenge. This project aims to explore and develop stability conditions for time-varying systems modeled using data-driven approaches. The methods will be validated on a numerical case study.
- Learn about data-driven, non-parametric models based on behavioral systems theory
- Understand notions of stability for linear time-varying systems
- Develop conditions on data-driven models of time-varying systems that ensure stability
- Validate the conditions on a numerical case study
- Learn about data-driven, non-parametric models based on behavioral systems theory - Understand notions of stability for linear time-varying systems - Develop conditions on data-driven models of time-varying systems that ensure stability - Validate the conditions on a numerical case study
Please send your cover letter and CV including transcript of records in PDF format via email to Andras Sasfi (asasfi@ethz.ch) and Dr. Jaap Eising (jeising@control.ee.ethz.ch). We look forward to your application
Please send your cover letter and CV including transcript of records in PDF format via email to Andras Sasfi (asasfi@ethz.ch) and Dr. Jaap Eising (jeising@control.ee.ethz.ch). We look forward to your application
