Input-output data-driven control through dissipativity learning

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 $L_{2}$ -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.