Title: Model Predictive control and estimation of linear transport-reaction systems
Stevan Dubljevic
University of Alberta, Edmonton, Canada
Abstract:
Distributed parameter systems (DPS) are ubiquitously present as models of fundamental conservation laws and in process control, manufacturing, transport systems and/or human society. The major drawback of DPS models is that they take form of partial differential equations containing higher order derivatives in space and time. The complexity of a partial differential equation (PDE) in the case of linear PDE models lies in necessity of modelers to account for model spatial characteristics by an approximating underlying model through some spatial approximation arriving to a finite dimensional model representation amenable for subsequent control, observer and/or monitoring device design.
This work provides foundation for systematic development of modelling framework for a linear DPS system which uses a finite and low dimensional setting for the controller/observer/estimator design without application of any spatial approximation or order reduction. In particular, we are interested in formulating control design methodology for a general class of linear DPS systems which in this work account for an optimal constrained optimization-based setting.
In addition to classical chemical process systems, we also address wave and beam equation system which accounts for a large class of distributed parameter systems. In this work, the discrete model of a distributed parameter system is obtained by using energy preserving Cayley-Tustin discretization. Discrete DPS models are low dimensional, energy preserving and do not dissipate numerically. In particular, discrete setting is amenable to an explicit, economic and/or a classical model predictive control setting realization, with emphasize on the different slight variations in realization of constrained finite dimensional controllers. Having this in mind, the model predictive control is designed by utilizing standard optimal control law with input or/and state/output constraints. The issues of stabilization, optimality and constrained stabilization are addressed for an infinite-dimensional system in this work. In addition, we also address the state estimation in this setting which allows practitioners to extend freely finite
dimensional concepts to the PDE models. Finally, the controller performance is assessed by numerical simulation with application on different distributed parameter systems.
报告人：Stevan Dubljevic，目前在加拿大阿尔伯塔大学担任副教授，研究兴趣包括分布式参数系统控制和模型预测控制、材料和化学过程的动力学和优化。在Automatica、JPC等控制领域顶级期刊上发表论文50余篇，2012年以来承担12项科研项目，包括加拿大NSERC项目10项，与企业联合开发的项目2项。2005年在加州大学洛杉矶分校获得博士学位。曾在加州大学洛杉矶分校David Geffen医学院心脏病学系担任独立博士后研究员（2006-2009）。他曾获美国心脏协会（AHA）西方国家联盟博士后奖学金（2007-2009），2007年获得美国自动控制委员会（AACC）O. Hugo Schuck应用技术获，2012年获得加拿大化学工程师学会的DG Fisher奖。
时 间：2018年12月11日（周二）
上午10:30~11:30
地 点：东华大学二号学院楼224室
主持人：张义红副研究员
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信息科学与技术学院
数字化纺织服装技术教育部工程研究中心
2018-12-10