Subspace identification of poorly excited industrial systems

Samuel Privara, Jiri Cigler, Zdenek Vana, Lukas Ferkl, Michael Sebek
2010 49th IEEE Conference on Decision and Control (CDC)  
Samuel Prívara F O R M This thesis is not written in a classic format but as a commentary to the attached journal papers. This format of the dissertation thesis is approved by the Dean of Faculty of Electrical Engineering by his 'Directive for dissertation theses defence" (Směrnice Děkana), Article 1. S U M M A R Y Climate changes, diminishing world supplies of the non-renewable fuels as well as economic aspects are probably the most driving factors of current effort to save the energy. As
more » ... ings account for about 40 % of global final energy use, an efficient building climate control can significantly contribute to the saving effort. Predictive building automation can be used to operate the buildings in energy and cost effective manner instead of conventional automation with minimum retrofitting requirements. Dynamic models (which must be simple, yet effective) are of crucial importance in predictive control approach. As the industrial experience has shown, the modeling and identification is the most timedemanding and costly part of the overall automation process. Many papers devoted to this topic actually model only the subsystems of a building. Some of them identify a building complex in reality as simple two-zones models. Others provide extremely detailed models resulting from the use of simulation software packages. These models, however, are not suitable for control as they are not in an explicit form. This thesis deals with the identification and modeling of the buildings resulting in a model suitable for the predictive control. A number of identification and modeling approaches is analyzed with respect to their suitability to use for predictive control of the buildings. Those that appear to be the promising candidates for the Model Predictive Control (MPC) are treated in detail. A novel approach combining a detailed modeling by a buildingdesign software with a black-box subspace identification is proposed. The uniqueness of the presented approach is not only in the size of the problem, but also in the way of getting the model and interconnecting several computational and simulation tools. As most of the industrial applications (as well as buildings) are Multiple-Input Multiple-Output (MIMO) systems that can be identified using the knowledge of the system's physics or from measured data employing statistical methods. Currently, there is the only class of statistical identification methods capable of handling the issue xi of the vast MIMO systems -Subspace State Space System Identification (4SID) methods. These methods, however, as all the statistical methods, need data of a certain quality, i.e. excitation of the corresponding order, no data corruption, etc. Nevertheless, combination of the statistical methods and a physical knowledge of the system could significantly improve system identification. The thesis presents a new algorithm which provides remedy to the insufficient data quality of a certain kind through incorporation of the prior information, namely a known static gain and an input-output feed-through. The presented algorithm naturally extends classic subspace identification algorithms, that is, it adds extra equations into the computation of the system matrices. For some kind of buildings, there is a possibility to take the advantage of using the physical structure. Hence, yet another class of modeling approaches, namely grey-box modeling techniques emerges. And as the objective is to have a good predictor on a horizon which is commensurate with the control, a natural choice is Model Predictive Control Relevant Identification (MRI). Some improvements to this identification methodology are suggested in this thesis as well. Finally, a problem of the model selection is addressed. Very often, there are far too many candidate inputs/states of the analyzed system and one has to decide which of them should be included to maximize the given quality criterion. The effective methodology for the selection of the inputs and states as well as the following model validation are proposed. The thesis is structured as follows: Chapter 2 presents motivation and provides comments on contributions of this work published in the papers which are available (for pdf version) in Chapter 3 by clicking the corresponding hyperlink. For online version, the hyperlink directs the reader to the list of references with the hyperlink to the internet address. The main results are outlined in Chapter 4 while Chapter 5 concludes the work and outlines directions of possible research. Chapter 6 summarizes the fulfilment of the objectives of the thesis. xii P U B L I C AT I O N S xiii I N T E R N AT I O N A L R E V I E W E D PA P E R S -W O S J. Cigler, S. Prívara, Z. Váňa, E. Žáčeková, and L. Ferkl. Optimization of predicted mean vote index within model predictive control framework: Computationally tractable solution. Energy and Buildings, 52(0):39 -49, 2012. Experimental analysis of model predictive control for an energy efficient building heating system. Applied Energy, 88(9):3079-3087, 2011.tion of predicted mean vote thermal comfort index within model predictive control framework. In 51th IEEE Conference on Decision and Control (CDC), December 2012a.
doi:10.1109/cdc.2010.5717585 dblp:conf/cdc/PrivaraCVFS10 fatcat:rj37yg4y4jbkhpranl4vc7sybu