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Input-output data-driven control through dissipativity learning | IEEE Conference Publication | IEEE Xplore
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Input-output data-driven control through dissipativity learning


Abstract:

Data-driven control offers an alternative to traditional model-based. Most present data-driven control strategies either involve model identification or need to assume av...Show More

Abstract:

Data-driven control offers an alternative to traditional model-based. Most present data-driven control strategies either involve model identification or need to assume availability of state information. In this work, we develop an input-output data-driven control method through dissipativity learning. Specifically, the learning of the subsystems' dissipativity property using one-class support vector machine (OC-SVM) is combined with the controller design to minimize an upper bound of the L2-gain. The data-driven controller synthesis problem is then formulated as quadratic-semidefinite programming with linear and multilinear constraints, solved via the alternating direction method of multipliers (ADMM). The proposed method is illustrated with a polymerization reactor.
Date of Conference: 10-12 July 2019
Date Added to IEEE Xplore: 29 August 2019
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ISSN Information:

Conference Location: Philadelphia, PA, USA

References

References is not available for this document.