Discrete predictive control and estimation of linear distributed pa-rameter systems
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题目：Discrete predictive control and estimation of linear distributed pa-rameter systems
报告人：Stevan Dubljevic
University of Alberta, Edmonton, Canada
报告时间： 12月12日 上午10：00-11：00
Abstract:
Distributed parametersystems (DPS) are ubiquitously present as models of fundamental conservationlaws and in process control, manufacturing, transport systems and/or human society. The major drawbackof DPS models is that they take form of partial differential equationscontaining higher order derivatives in space and time. The complexity of apartial differential equation (PDE) in the case of linear PDE modelslies in necessity of modellers to account for model spatial characteristics byan approximating underlying model through some spatial approximation arriving toa finite dimensional model representation amenable for subsequent control,observer and/or monitoring device design.
This work providesfoundation for systematic development of modelling framework for a linear DPS system whichuses a finite and low dimensional setting for the controller/observer/estimatordesign without application of any spatial approximation or order reduction. Inparticular, we are interested in formulating control design methodology for ageneral class of linear DPSsystems which in this work account for an optimal constrained optimization based setting.In addition to classical chemical process systems, we also address wave andbeam equation system which accounts for a large class of distributed parametersystems. In this work, the discrete model of a distributed parameter system isobtained by using energy preserving Cayley-Tustin discretization. Discrete DPSmodels are low dimensional, energy preserving and do not dissipate numerically.In particular, discrete setting is amenable to an explicit, economic and/or aclassical model predictive control setting realization, with emphasize on thedifferent slight variations in realization of constrained finite dimensionalcontrollers. Having this in mind, the model predictive control is designed by utilizing standard optimalcontrol law with input or/and state/output constraints. The issues ofstabilization, optimality and constrained stabilization are addressed for aninfinite-dimensional system in this work. In addition, we also address thestate estimation in this setting which allows practitioners to extend freelyfinite dimensional concepts to the PDE models. Finally, the controller performance is assessed bynumerical simulation with application on different distributed parameter systems.
Biography:
Dr. Stevan Dubljevic received his Ph.D. in 2005 from the HenrySamueli School of Engineering and Applied Science at University of California in LosAngeles (UCLA), M.S. degree (2001) from the Texas A&M University (Texas),and the B.Sc. degree (1997) from the Belgrade University (Serbia). He heldindependent post-doctoral researcher position at the Cardiology Division of theUCLA's David Geffen School of Medicine(2006-2009). He is the recipient of theAmerican Heart Association (AHA) Western States Affiliate Post-doctoral Grant Award (2007-2009) and the recipient of theO. Hugo Schuck Award for Applications, from American Automatic Control Council(AACC) 2007. His research interests include systems engineering, with theemphasis on model predictive control of distributed parameter systems, dynamicsand optimization of material and chemical process operations, computational modelling andsimulation of biological systems (cardiac electrophysiological systems) and biomedical engineering. He is thereviewer for the IEEE Transaction on Automatic Control, IEEE Transaction onControl Systems Technology, Automatica, Industrial & Engineering Chemical Research, International Journal ofSystem Science, American Control Conference, Conference on Decision andControl, and program coordinator for theAIChE Annual Meetings (2014).
联系人：郜传厚（gaochou@zju.edu.cn ）