Subspace like identification incorporating prior information

Trnka, Pavel; Havlena, Vladimı́r

doi:10.1016/j.automatica.2008.12.005

Cited by 47 publications

(50 citation statements)

References 9 publications

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“…Prior knowledge about steady state gain was in this case selected as follows: These 4SID methods come, in general, from robust combined deterministic and stochastic algorithm as introduced in [15]. Moreover two methods from [9] are mentioned for comparison: i) 4SID -version without changes, ii) 4SID+PI -Steady state gain was included using (22). Matrix D is not set to zero, iii) 4SID-D -Matrix D is set to zero but steady state gain is not included, iv) 4SID-D+PI -Both types of PI information, i.e.…”

Section: Identification Resultsmentioning

confidence: 99%

“…Previous works such as [12] count with single-input single-output (SISO) system only. Even [9] using Bayesian framework approach did not present method which would treat MIMO system in a satisfactory manner. This paper, in contrary, presents a new algorithm of incorporation prior information, which is builtin directly into system matrices B and D and does not make use of the covariance matrix.…”

Section: Motivationmentioning

confidence: 90%

“…This problem is treated in [9], but favorable results are given only for multiple input single output (MISO) systems, because presented algorithm is based on structured weighted lower rank approximation (SWLRA) [23] which provides optimal solution only for MISO systems. However, at least suboptimal algorithm is presented in [9], but the level of optimality is not guaranteed.…”

Section: Incorporation Of Prior Informationmentioning

confidence: 99%

“…using regularization can be found in [6], [7] or formulated as constrained optimization in [8]. Black-box identification, such as subspace identification methods, rely only on experimental data, that is, they may result in biased models [9], or fail in giving a proper model (this problem is addressed in [10], [11]). …”

Section: Motivationmentioning

confidence: 99%

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Subspace identification of poorly excited industrial systems

Prívara

Cigler

Váňa

et al. 2010

49th IEEE Conference on Decision and Control (CDC)

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Most of the industrial applications are multipleinput multiple-output (MIMO) systems, that can be be identified using 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 vast MIMO systems -subspace identification methods. These methods, however, as all statistical methods, need data of certain quality, i.e. excitation of corresponding order, no data corruption, etc. Nevertheless, the combination of statistical methods and physical knowledge of the system could significantly improve system identification. This paper presents a new algorithm which provides remedy to insufficient data quality of certain kind through incorporating of prior information, e.g. known static gain or input-output feedthrough. The presented algorithm naturally extends classical subspace identification algorithms, that is, it adds extra equations into the computation of system matrices. The performance of the algorithm is shown on a case study and compared to current methods, where the model is used for an MPC control of a large building heating system.Index Terms-Subspace methods; Identification for control I. MOTIVATIONWith 39 %, buildings contribute significantly to total energy usage in 2005, as stated by the U. S. Energy Information Administration [1]. This poses strong motivation for creation of advanced and energy saving HVAC (Heating, Ventilating, and Air Conditioning) systems [2]. Significant amount of energy can be saved using predictive control strategies (see project OptiControl 1 ) compared to the conventional strategies. Widely used control strategy, weathercompensated control, can lead to poor energy management or reduced thermal comfort even if properly set up, because it utilizes current outside temperatures only. In case of sharp change of weather, there is an improper control action due to the energy accumulation in large buildings, resulting in over-or underheating of the building. Even though HVAC control systems have been improved significantly during recent years, predictive controller described in [3] introduces a different approach to the heating system control design. There is, however, a crucial condition for the successful control, that is, properly identified model of the system. Model identification can be performed by variety of methods, physical modeling or statistical approach among others. This paper presents incorporation of prior information into subspace identification methods. These methods originally emerged as a conjunction of linear algebra, geometry and S. Prívara, J. Cigler, Z. Váňa, L. Ferkl and M.Šebek are with Department of Control Engineering, Faculty of Electrical Engineering of Czech Technical University in Prague, Karlovo náměstí 13, Czech Republic samuel.privara@fel.cvut.cz 1 http://www.opticontrol.ethz.ch/ system theory and compared to the classical identification methods [4], they provide user with several advantages such as numerical robustness, natu...

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Section: Identification Resultsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 90%

Section: Incorporation Of Prior Informationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

See 2 more Smart Citations

Subspace identification of poorly excited industrial systems

Prívara

Cigler

Váňa

et al. 2010

49th IEEE Conference on Decision and Control (CDC)

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“…One way which is still not full discovered 4 is incorporating of prior information. This approach make use of Bayesian approach [20] to the system identification and changes nature of the subspace identification method towards grey-box identification.…”

Section: Remarks To Future Developmentmentioning

confidence: 99%

Model predictive control of buildings: The efficient way of heating

Ferkl

Široký

Prívara

2010

2010 IEEE International Conference on Control Applications

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The implicit model predictive control based on models identified by subspace identification methods was implemented and tested on a large university building. The control was improved by incorporating the weather prediction into the model. The performance of said controller was estimated in an experiment, wherein two almost identical building blocks were compared -one controlled by the model predictive control, and the other one by the existing weather-compensated heating controller. The model predictive control achieved energy consumption lower by approximately 10 %. Based on the positive results, an implementation was developed, which is suitable for commercial applications.

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Adaptive control and signal processing literature survey (no. 13)

2009

Adaptive Control & Signal

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In order to keep readers up to date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Leonardo Giovanini at lgiovanini@fich. unl.edu.ar. ADAPTIVE SYSTEMSAl-Shyoukh I, Shamma J. Switching supervisory control using calibrated forecasts. IEEE Transactions on Automatic Control 2009; 54(4):705-716. Huebsch J, Sandoval L, Budman H. Tuning an adaptive controller using a robust control approach. International Journal of Control 2009; 82(5): 894-909. Lee J, Lee J. An approximate dynamic programming based approach to dual adaptive control. Journal of Process Control 2009; 19(5):859-864. Sato M. Robust model-following controller design for LTI systems affected by parametric uncertainties: a design example for aircraft motion. International Journal of Control 2009; 82(4):689-704. Patel V, Cao C, Hovakimyan N, Wise K, Lavretsky E. L 1 adaptive controller for tailless unstable aircraft in the presence of unknown actuator failures. International Journal of Control 2009; 82(4): 705-720. Wang J, Qu Z. Robust adaptive control of a class of nonlinearly parameterised time-varying uncertain systems. IET Control Theory and Applications 2009; 3(6):617-630. Xie D, Wu Y. Stabilisability of switched linear systems with time-varying delay in the detection of switching signals. IET Control Theory and Applications 2009; 3(4):404-410. Xie D, Wang Q, Wu Y. Average dwell-time approach to L2 gain control synthesis of switched linear systems with time delay in detection of switching signal. Hespanha J. Robust filtering for deterministic systems with implicit outputs. Systems and Control Letters 2009; 58(4):263-270. Belkoura L, Richard J-P, Fliess M. Parameters estimation of systems with delayed and structured entries. Automatica 2009; 45(5):1117-1125. Bhaumik S, Sadhu S, Ghoshal T. Risk-sensitive formulation of unscented Kalman filter. IET Control Theory and Applications 2009; 3(4):375-382. Cavazos-Cadena R, Hernández-Hernández D. Necessary, and sufficient conditions for a solution to the risk-sensitive Poisson equation on a finite state space. Systems and Control Letters 2009; 58(4): 254-258. Kadlec P, Gabrys B, Strandt S. Data-driven soft sensors in the process industry. Computers and Chemical Engineering 2009; 33(4):795-814. Kallapur A, Petersen I, Anavatti S. A discrete-time robust extended Kalman filter for uncertain systems with sum quadratic constraints.

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Subspace like identification incorporating prior information

Cited by 47 publications

References 9 publications

Subspace identification of poorly excited industrial systems

Subspace identification of poorly excited industrial systems

Model predictive control of buildings: The efficient way of heating

Adaptive control and signal processing literature survey (no. 13)

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