Zdeněk Váňa

System identification in frequency domain using wavelets: Conceptual remarks

Abstract Model identification plays a central role in any activity associated with process operations. With control being done on different levels, different models are required for the same plant, each for a different range of dynamics. Besides that most identification methods apply to the linear models, they also do not allow for selecting a frequency range. Wavelet-based methods have the intrinsic ability to select time and frequency windows, and, to some extent, are also applicable to non-linear processes. The paper presents an approach, in which the wavelet transform is employed for system identification enabling the selection of the particular frequency range of interest. We will show the use of some wavelet filters with a property of superior selectivity in the frequency domain and having compact support in the time domain, which, in turn, influences an accurate implementation. These properties provide us with a possibility of the measured data analysis in the frequency domain without any loss of information. Selection of a proper filter allows us to identify the system on a desired frequency range, or to identify a number of systems for distinct frequency ranges. This is specifically convenient for the systems with dominant modes, such as singularly perturbed systems. The possibility of selection of the specific frequency range can be utilized for application-based identification such as control, when only a limited frequency range is required. We conclude with a case study, where the proposed algorithms are tested and results are presented.

Role of MPC in Building Climate Control

Low energy buildings have attracted a lot of attention in past decades. Recent research is dedicated mainly to optimization of building construction and alternative energy sources. We provide a different approach to the energy-consumption and energy-cost optimization. A generic concept of minimizing energy consumption using current energy sources making use of advanced control techniques is presented. Model Predictive Controller (MPC) presented in this article makes use of both weather forecast and thermal model of a building to control inside temperature. This, by sharp contrast to conventional control strategies such as heating-curve (HC) or rule-based controllers (RBC), enables utilization of thermal capacity of the building. The inside temperature can be maintained at desired levels independent of the outside weather conditions using modified formulation of MPC.

Incorporation of system steady state properties into subspace identification algorithm

Most of the industrial applications 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 of the vast MIMO systems – subspace identification 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. This paper 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 classical subspace identification algorithms, that is, it adds extra equations into the computation of the system matrices. The performance of the algorithm is shown on a case study and compared to the current methods, where the model is used for an MPC control of a large building heating system.

System identification using wavelet analysis

Abstract System identification (SID) plays a central role in any activity associated with process operations. With control being done on different levels, different models are required for the same plant each for a different range of dynamics. Besides that most identification methods apply to the linear models, they also do not allow for selecting a frequency range. Wavelet methods have the ability to select the time and frequency windows and are also applicable to the nonlinear processes. The paper presents an approach, in which wavelet transform is used for SID enabling selection of the particular frequency range. Even though, the wavelet transform as a tool is known for a long time and has a number of desirable properties, it is not frequently used in the applications.

Building modeling: on selection of the model complexity for predictive control

Abstract Model Predictive Control has become a wide-spread solution in many industrial applications and is gaining ground in the field of energy management of the buildings. A model with good prediction properties is an ultimate condition for good performance of the predictive controller. In this paper grey box modeling and model predictive control relevant identification are used for construction of candidate models of a building. We introduce a two-stage iterative procedure for model selection: in the first stage a minimum set of disturbance inputs is formed such that the resulting model is the best with respect to defined criterion; then the second stage comprises addition of states to obtain a final minimum parameter set maximizing the model quality. The procedure stops when it makes no sense to select more complex model as it brings no more quality improvements. Finally a case study is provided where all the above mentioned approaches are investigated.