Minimax Adaptive Control and Estimation
Minimax adaptiv reglering och estimering
Författare
Summary, in English
This thesis presents five papers on minimax adaptive control and estimation. Minimax adaptive estimation is a framework for output prediction and state estimation that provides a priori computable performance bounds for esti- mators. Minimax adaptive controllers ensure that the closed loop has finite gain, maintaining stability and performance under model class uncertainty.
The contributions of these papers are as follows: Paper I: Presents a min- imax optimal output prediction algorithm for linear systems with parameter uncertainty. Paper II: Proposes an algorithm to compute performance bounds for minimax adaptive estimators. Paper III: Develops a minimax suboptimal adaptive controller for scalar linear systems with noisy measurements. Paper IV: Introduces a class of nonlinear systems for which minimax dual control admits a finite-dimensional sufficient statistic, builds dynamic programming theory around this class, and designs an adaptive controller for stabilizing an integrator from absolute-value measurements. Paper V: Provides a unified framework for state-feedback and output-feedback minimax adaptive control and methods for synthesizing suboptimal controllers. Complementing these theoretical contributions are two software artifacts: one for adaptive control and the other for adaptive estimation.
The contributions apply to simple systems that represent components of larger systems, marking a step towards automating controller synthesis and maintenance for critical infrastructures.
The contributions of these papers are as follows: Paper I: Presents a min- imax optimal output prediction algorithm for linear systems with parameter uncertainty. Paper II: Proposes an algorithm to compute performance bounds for minimax adaptive estimators. Paper III: Develops a minimax suboptimal adaptive controller for scalar linear systems with noisy measurements. Paper IV: Introduces a class of nonlinear systems for which minimax dual control admits a finite-dimensional sufficient statistic, builds dynamic programming theory around this class, and designs an adaptive controller for stabilizing an integrator from absolute-value measurements. Paper V: Provides a unified framework for state-feedback and output-feedback minimax adaptive control and methods for synthesizing suboptimal controllers. Complementing these theoretical contributions are two software artifacts: one for adaptive control and the other for adaptive estimation.
The contributions apply to simple systems that represent components of larger systems, marking a step towards automating controller synthesis and maintenance for critical infrastructures.
Publiceringsår
2024
Språk
Engelska
Fulltext
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Dokumenttyp
Doktorsavhandling
Förlag
Department of Automatic Control, Lund University
Ämne
- Control Engineering
Aktiv
Published
Projekt
- Scalable Control using Learning and Adaptation
- Scalable Control of Interconnected Systems
ISBN/ISSN/Övrigt
- ISBN: 978-91-8104-167-5
- ISBN: 978-91-8104-168-2
Försvarsdatum
11 oktober 2024
Försvarstid
09:15
Försvarsplats
Lecture hall A, building M, Ole Römers väg 1
Opponent
- Jack Umenberger (Senior Research Fellow, Dr)