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Controllers Designed Directly From Data<\/h1><\/span>
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Controllers Designed Directly From Data<\/h1><\/span>
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We are a research group at the Federal University of Rio Grande do Sul (UFRGS), Brazil, developing theory, methods and software for data-driven control and system identification. <\/p>\n<\/div><\/div><\/div>

Our research<\/span><\/a><\/div>
Publications<\/span><\/a><\/div><\/div><\/div>
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Our research<\/span><\/a><\/div>
Publications<\/span><\/a><\/div><\/div><\/div><\/div><\/div>
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From measured data to a working controller.<\/h2><\/span>
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Data-driven methods tune a controller from input\u2013output data collected on the process, without first building a detailed model of the plant. Our work covers the theory behind these methods, the design of experiments that produce informative data, and their application to real systems.<\/p>\n<\/div>

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Virtual Reference Feedback Tuning<\/h3><\/span>
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One-shot tuning from a single batch of data, extended to multivariable, non-minimum phase, cascade and disturbance rejection problems. <\/p>\n<\/div><\/div><\/div>

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Optimal Controller Identification<\/h3><\/span>
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Controller design cast as a prediction error identification problem, bringing the statistical tools of identification to data-driven control. <\/p>\n<\/div><\/div><\/div>

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Identification & Experiment Design<\/h3><\/span>
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Identifiability, input design and regularized estimation, from single loops to dynamic networks. <\/p>\n<\/div><\/div><\/div>

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Interested In Working With Us?<\/h2><\/span>
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Students and collaborators in data-driven control and system identification are welcome to get in touch. <\/p>\n<\/div>

Contact us<\/span><\/a><\/div><\/div><\/div><\/div><\/div>
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Publications<\/h2><\/span>
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49 journal articles, 59 conference papers, 2 books and 25 PhD and master’s theses since 2006.<\/p>\n<\/div><\/div><\/div>

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Published In Automatica, IEEE Transactions And Annual Reviews In Control<\/h2><\/span>
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See all publications<\/span><\/i><\/a><\/div><\/div><\/div>

Our papers appear in Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, Annual Reviews in Control and the Journal of Process Control, among others. Author versions are available for download whenever the publisher allows it.<\/p>\n<\/div>

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OPEN-SOURCE SOFTWARE<\/p>\n<\/div>

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pyvrft: Virtual Reference Feedback Tuning in Python<\/h2><\/span>
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A Python package implementing the VRFT method, published in SoftwareX (2020). <\/p>\n<\/div><\/div><\/div>

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APPLICATIONS<\/p>\n<\/div>

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From power electronics to process control.<\/h2><\/span>
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Our methods have been applied to DC\u2013DC converters, uninterruptible power supplies, wide-area damping of power systems, wireless industrial networks and multivariable pilot plants. <\/p>\n<\/div><\/div><\/div>

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Data-driven Controller Design: The H2<\/sub> Approach<\/h2><\/span>
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A. S. Bazanella, L. Campestrini and D. Eckhard (Springer, 2012). Direct methods (VRFT) and iterative methods (IFT, DFT, CbT) are put under a common theoretical framework, with guidelines for the choice of reference model, experimental conditions and optimization method. <\/p>\n<\/div>

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