Uncertainty model unfalsification with simulation

Woodley, Bruce R.; Kosut, Robert L.; How, Jonathan P.

doi:10.1109/acc.1998.688353

Cited by 11 publications

(3 citation statements)

References 4 publications

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“…Model unfalsification does not get much appeal in practice because of its high computational burden (Woodley (2001)). There are many implementations available for model unfalsificaiton (Kosut & Anderson (1997); Agnoloni & Mosca (2003); Tsao et al (2003); Wodoley et al (1999); Safonov (2003); Tsao & Safonov (2001); Woodley et al (1998); Cabral & Safonov (2004)). Wang et al suggested a direct adaptive controller based on model unfalsification with the assumption that there would be a controller in the given set that would satisfy the control requirements for a particular plant ).…”

Section: Acknowledgementmentioning

confidence: 99%

Robotics Automation and Control

Pecherková¹,

Flídr²,

Duník³

2008

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This book was conceived as a gathering place of new ideas from academia, industry, research and practice in the fields of robotics, automation and control. The aim of the book was to point out interactions among the various fields of interests in spite of diversity and narrow specializations which prevail in the current research.We believe that the resulting collection of papers fulfills the aim of the book. The book presents twenty four chapters in total. The scope of the topics presented in the individual chapters ranges from classical control and estimation problems to the latest artificial intelligence techniques. Moreover, whenever possible and appropriate, the proposed solutions and theories are applied to real-world problems.The common denominator of all included chapters appears to be a synergy of various specializations. This synergy yields deeper understanding of the treated problems. Each new approach applied to a particular problem, may enrich and inspire improvements of already established approaches to the problem.We would like to express our gratitude to the whole team who made this book possible. We hope that this book will provide new ideas and stimulation for your research.

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Section: Acknowledgementmentioning

confidence: 99%

Robotics Automation and Control

Pecherková¹,

Flídr²,

Duník³

2008

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“…This work can be extended to a complete implementation of a model-free control system such as the one suggested by Woodley et al. One of the challenges in the actual implementation is determination of uncertainty block ∆ for the given system using techniques such as model unfalsification but without excessive overload of high computations (Woodley et al (1998), Paul B. Brugarolas (2004)).…”

Section: Future Workmentioning

confidence: 99%

FEASIBILITY OF SUBSPACE IDENTIFICATION FOR BIPEDS - An Innovative Approach for Kino-Dynamic Systems

Saleem¹,

Sultan²

2007

Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics

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“…A variety of techniques have been proposed for adaptively identifying robust control laws from experimental data [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. There are two main approaches, indirect and direct.…”

Section: Introductionmentioning

confidence: 99%

Learning about dynamical systems via unfalsification of hypotheses

Brugarolas

Safonov

2004

Intl J Robust & Nonlinear

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SUMMARYThis paper examines the problem of learning behaviours of a dynamical system from experimental data via unfalsification of hypotheses within the behavioural approach to system theory of Willems. Behaviours of the dynamic systems are postulated as hypotheses and then tested against experimental data. A simple and concise condition for falsification of hypotheses by experimental data in terms of a kernel is presented. The approach is applicable both to learning models for a plant and to adapting controllers to satisfy performance and robustness goals.

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Uncertainty model unfalsification with simulation

Cited by 11 publications

References 4 publications

Robotics Automation and Control

Robotics Automation and Control

FEASIBILITY OF SUBSPACE IDENTIFICATION FOR BIPEDS - An Innovative Approach for Kino-Dynamic Systems

Learning about dynamical systems via unfalsification of hypotheses

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