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 MoreMetadata
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.
Published in: 2019 American Control Conference (ACC)
Date of Conference: 10-12 July 2019
Date Added to IEEE Xplore: 29 August 2019
ISBN Information: