Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The model predictive control (MPC) technique has been widely applied in a large number of industrial plants. Optimal input design should guarantee acceptable model parameter estimates while still providing for low experimental effort. The goal of this work is to investigate an application-oriented identification experiment that satisfies the performance objectives of the implementation of the model. A- and D-optimal input signal design methods for a non-linear liquid two-tank model are presented in this paper. The excitation signal is obtained using a finite impulse response filter (FIR) with respect to the accepted application degradation and the power constraint. The MPC controller is then used to control the liquid levels of the double tank system subject to the reference trajectory. The MPC scheme is built based on the linearized and discretized model of the system to predict the system’s succeeding outputs with reference to the future input signal. The novelty of this model-based method consists in including the experiment cost in input design through the objective function. The proposed framework is illustrated by means of numerical examples, and simulation results are discussed.
Rocznik
Tom
Strony
883--891
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
autor
- Bialystok University of Technology, Faculty of Computer Science, Wiejska 45A, 15-351 Bialystok, Poland
autor
- Bialystok University of Technology, Faculty of Electrical Engineering, Wiejska 45D, 15-351 Bialystok, Poland
Bibliografia
- [1] R. Kalaba and K. Spingarn, Control, identification, and input optimization, Softcover reprint of the original 1st ed., 1982, Springer US, pp. 225–279 and 281–341, 2012.
- [2] A.C. Atkinson, A.N. Donev, and R.D. Tobias, Optimum Experimental Designs, with SAS, Oxford Univ. Press, Oxford, pp. 119–147, 2007.
- [3] H. Jansson, Experiment Design with Applications in Identification for Control (PhD Thesis), Royal Institute of Technology (KTH), Stockholm, Sweden, 2004.
- [4] M. Gevers, “Identification for control: From the early achievements to the revival of experiment design”, Eur. J. Control 11 (4), 335–352 (2005).
- [5] J. Schoukens and L. Ljung, “Nonlinear System Identification. A User-Oriented Roadmap”, IEEE Control Syst. Mag. 39 (6), 28–99, (2019).
- [6] M. Annergren, C.A.A. Larsson, H. Hjalmarsson, X. Bombois, and B. Wahlberg, “Application-oriented input design in system identification: Optimal input design for control”, IEEE Control Syst. Mag. 37 (2), 31–56 (2017).
- [7] G.G. Yin, S. Kan, and L.Y. Wang, “Identification Error Bounds and Asymptotic Distributions for Systems with Structural Uncertainties”, J. Syst. Sci. Complex. 19 (1), 22–35 (2006).
- [8] M. Milanese amd M. Taragna, “H∞ set membership identification: A survey”, Automatica 41 (12), 2019–2032 (2005).
- [9] S.P. Bhattacharyya, “Robust control under parametric uncertainty: An overview and recent results”, Annu. Rev. Control 44, 45–77 (2017).
- [10] X. Bombois, M. Gevers, G. Scorletti, and B. Anderson, “Robustness analysis tools for an uncertainty set obtained by prediction error identification”, Automatica 37 (10), 1629–1636 (2001).
- [11] C.R. Rojas, J.C. Agüero, J.S. Welsh, and G.C. Goodwin, “On the equivalence of least costly and traditional experiment design for control”, Automatica 44 (11), 2706–2715 (2008).
- [12] X. Bombois, G. Scorletti, M. Gevers, P.M.J. Van den Hof, and R. Hildebrand, “Least costly identification experiment for control”, Automatica 42 (10), 1651–1662 (2006).
- [13] L. Ljung, H. Hjalmarsson, and H. Ohlsson, “Four Encounters with System Identification”, Eur. J. Control 17 (5-6), 449–471 (2011).
- [14] S. Narasimhan and R. Rengaswamy, “Plant friendly input design: Convex relaxation and quality”, IEEE Trans. Automat. Control 56, 1467–1472 (2011).
- [15] W. Jakowluk, “Free Final Time Input Design Problem for Robust Entropy-Like System Parameter Estimation”, Entropy 20 (7), 528 (2018).
- [16] W. Jakowluk, “Design of an optimal input signal for plant-friendly identification of inertial systems”, Prz. Elektrotechniczny 85 (6), 125–129 (2009).
- [17] A. Kumar, M. Nabil, and S. Narasimhan, “Economical and Plant Friendly Input Design for System Identification”, In: Proc. 2014 European Control Conference (ECC), Strasbourg, France, 732–737 (2014).
- [18] D.E. Rivera, H. Lee, and M.W. Braun, “’Plant-friendly’ system identification: A challenge for the process industries”, In: Proc. IFAC Symp. System Identification, Rotterdam, The Netherlands, 917–922 (2003).
- [19] A. Kumar and S. Narasimhan, “Robust plant friendly optimal input design”, In: 10th IFAC Symposium on Dynamics and Control of Process Systems, Mubai, India, 553–558 (2013).
- [20] E. Rafajłowicz and W. Rafajłowicz, “More safe optimal input signals for parameter estimation of linear systems described by ODE”, System modelling and optimization, IFIP AICT, vol. 443, Springer, Heidelberg, 267–277 (2014).
- [21] M. Hussain, “Review of the applications of neural networks in chemical process control–simulation and on–line implementation”, Artificial Intelligence in Engineering 13, 55–68 (1999).
- [22] C.A. Larsson, M. Annergren, H. Hjalmarsson, C.R. Rojas, X. Bombois, A. Mesbah, and P.E. Modén, “Model predictive control with integrated experiment design for output error systems”, In: Proc. European Control Conf., Zurich, Switzerland, 3790–3795 (2013).
- [23] J.M. Maciejowski, Predictive Control with Constraints, Englewood Cliffs, NJ: Prentice-Hall, pp. 108–115, 2002.
- [24] W. Jakowluk, “Design of a state estimation considering model predictive control strategy for a nonlinear water tanks process”, In: Computer Information Systems and Industrial Management. Lecture Notes in Computer Science, Springer, Cham, 11703, 457–468 (2019).
- [25] A. Oustaloup, F. Levron, B. Mathieu, and F. Nanot, “Frequency-band complex noninteger differentiator: characterization and synthesis”, IEEE Transactions on Circuits and Systems, Fundamental Theory and Applications 47 (1), 25–40 (2000).
- [26] M. Lewandowski and M. Orzyłkowski, “Fractional-order models: The case study of the supercapacitor capacitance measurement”, Bull. Pol. Ac.: Tech. 65 (4), 449–457 (2017).
- [27] W. Jakowluk, “Optimal input signal design for fractional-order system identification”, Bull. Pol. Ac.: Tech. 67 (1), 37–44 (2019).
- [28] W. Jakowluk, “Design of an optimal input signal for parameter estimation of linear fractional-order systems”, In: Advances in Non-Integer Order Calculus and Its Applications, Lecture Notes in Electrical Engineering, Springer, Cham, 559, 128–141 (2020).
- [29] R. Isermann and M. Münchhof, Identification of Continuous-Time Systems: Linear and Robust Parameter Estimation, Springer, pp. 453–499, 2011.
- [30] L. Ljung, System identification: Theory for the user, Prentice Hall, Inc., Upper Saddle River, New Jersey, USA, pp. 247–304, 1999.
- [31] M. Annergren and C.A. Larsson, “Moose2 – A toolbox for least-costly application-oriented input design”, SoftwareX 5, 96–100 (2016).
- [32] T. Iwasaki and S. Hara, “Generalized KYP lemma: unified frequency domain inequalities with design applications”, IEEE Trans. Autom. Control l50 (1), 41–59 (2005).
- [33] H. Hjalmarsson, “System identification of complex and structured systems”, Eur. J. Control 15 (3), 275–310 (2009).
- [34] M. Annergren and C.A. Larsson, MOOSE2: Model based optimal input signal design toolbox, version 2, 2015, Available at: www.kth.se/moose/
- [35] J. Löfberg, “YALMIP: A toolbox for modeling and optimization in MATLAB”, In Proc. Computer Aided Control System Design Conf., Taipei, Taiwan, 284–289 (2004).
- [36] K.C. Toh, M.J. Todd, and R H. Tütüncü, “On the Implementation and Usage of SDPT3 – A Matlab Software Package for Semidefinite-Quadratic-Linear Programming, Version 4.0”, In: M.F. Anjos, J.B. Lasserre (eds.), Handbook of Semidefinite, Conic and Polynomial Optimization, Springer, Boston, USA, 715–754 (2012).
- [37] L. Ljung, System identification toolbox: User’s guide, The Math-Works, Inc., Natick, MA, 2010.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-94af6d87-1c0e-420b-86db-611b068d6f61