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dc.contributor.authorAl Ramadan, Hussainen
dc.description.abstractStochastic Model Predictive Control (SMPC) has been the focus of intense research in the last years. The objective of SMPC is to design control systems corrupted by stochastic noise. Existing solutions include the implementation of Robust MPC schemes, where the worst case scenario is taken into account. As this approach often results in conservative and expensive solutions, a number of researchers have addressed the need for more optimistic and realistic tailored control schemes that can handle stochastic process behaviour. SMPC gives practitioners a tool to take most realization of uncertainty in the controller design, while guaranteeing feasibility and stability. The work presented in this thesis focuses on developing an SMPC algorithm for nonlinear systems with additive stochastic uncertainty. The scheme is carried out in a fast sampled-data approach, which yields an advantageous computational load. We develop a sampled-data scheme for deterministic systems through sampling control moves. Two sampled-data schemes have been proposed and examined in benchmark simulations including a CSTR system. The first scheme considers a hybrid control system, where a continuous-time flow control occurs during a fine time grid, and a discrete-time jump control looks after jumps in a coarse time grid. The second scheme is a complete sampled-data system through discrete control moves throughout the two time grids. It was shown that the second control scheme succeeded in reducing the cost function along with a huge reduction in CPU time for all the simulations. Secondly, the latter sampled-data scheme was modified to handle stochastic noise through the inclusion of an expected cost and probabilistic constraints. The resulting control can handle moderate noise signals and produce much better results than conventional nonlinear SMPC schemes. The proposed stochastic sampled-data MPC scheme is designed to tackle nonlinear dynamical systems with moderate noise signals with a large precision and low CPU time.en
dc.relation.ispartofseriesCanadian thesesen
dc.rightsCC0 1.0 Universalen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectModel Predictive Controlen
dc.subjectNonlinear Systemsen
dc.subjectConstraint Tighteningen
dc.titleStochastic Sampled-data Model Predictive Control for Nonlinear Systemsen
dc.contributor.supervisorGuay, Martinen
dc.contributor.departmentChemical Engineeringen's University at Kingstonen

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CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal