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Title: Randomized algorithms for control of uncertain systems with application to hand disk drives
Keywords: Randomized Algorithms, Uncertainty, Robust Optimization, Monte Carlo Simulation, Statistical Learning Theory
Issue Date: 13-Jan-2014
Source: MOHAMMADREZA CHAMANBAZ (2014-01-13). Randomized algorithms for control of uncertain systems with application to hand disk drives. ScholarBank@NUS Repository.
Abstract: The presence of ?uncertainty? in dynamical systems is inevitable. Different imperfections such as manufacturing tolerances, different raw materials and slight change in the environmental condition of the production line contribute to slight difference in the dynamics over a batch of products. In robust control, this difference is modeled as parametric and non-parametric (dynamic) uncertainties. Dynamic uncertainty can be handled efficiently using ?- theory however, coming to parametric uncertainty, most deterministic approaches suffer from conservatism and computational complexity. Motivated by this, in the present thesis we propose two classes of randomized algorithms: i) Sequential randomized algorithms for solving uncertain convex optimization problems and ii) Randomized algorithms for solving uncertain linear and bilinear matrix inequalities using statistical learning theory. The effectiveness of the developed algorithms is showed through extensive simulations regarding the track following control of hard disk drives affected by multiple parametric uncertainties.
Appears in Collections:Ph.D Theses (Open)

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