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Learning-based Robust Control: Guaranteeing Stability while Improving Performance Felix Berkenkamp and Angela P. Schoellig Abstract— To control dynamic systems, modern control theory relies on accurate mathematical mod
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Document Date: 2014-08-23 16:08:05

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City

Zurich / Cambridge / Learning / New York / /

Company

Pearson / Robust Control / Neural Networks / MIT Press / IEEE Control Systems / John Wiley & Sons / /

Country

Switzerland / /

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Facility

University of Toronto Institute / /

IndustryTerm

matrix product / online learning / nonlinear systems / line search / online applications / bias-free online system identification / online setting / robotics applications / linear systems / dynamical systems / online identification / online measurements / control law / /

Organization

University of Toronto Institute for Aerospace Studies / World Congress / MIT / U.S. Securities and Exchange Commission / Society for Industrial and Applied Mathematics / /

Person

Van Den Hof / E. F. Camacho / R. J. Schrama / M. V. Kothare / V / Angela P. Schoellig / C. B. Alba / Felix Berkenkamp / /

Position

system model / H∞ statefeedback controller / learning controller / model predictive controller / Model / linear controller / Update controller / possible controller / aggressive controller / controller / discrete-time controller / /

ProgrammingLanguage

R / /

Technology

GPS / Machine learning / simulation / /

URL

http /

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