Abstract
The optimal input signal design is a procedure of generating an informative excitation signal to extract the model parameters with maximum accuracy during the estimation process. Non-integer order calculus is a very useful tool, which is often utilized for modeling and control purposes. In the paper, we present a novel optimal input formulation and a numerical scheme for fractional order LTI system identification. The Oustaloup recursive approximation (ORA) method is used to determine the fractional order differentiation in an integer order state-space form. Then, the presented methodology is adopted to obtain an optimal input signal for fractional order system identification from the order interval \(0.5 \le \alpha \le 2.0\). The fundamental step in the presented method was to reformulate the problem into a similar fractional optimal input design problem described by Lagrange formula with the set of constraints. The methodology presented in the paper was verified using a numerical example, and the computational results were discussed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Monje, C.A., Chen, Y., Vinagre, B., Xue, D., Feliu, V.: Fractional Orders Systems and Controls: Fundamentals and Applications. Advances in Industrial Control. Springer, London (2010). https://doi.org/10.1007/978-1-84996-335-0
Chen, Y., Petráš, X.D.: Fractional order control - a tutorial. In: Proceedings of ACC 2009, American Control Conference, pp. 1397–1411 (2009)
Torvik, P.J., Bagley, R.L.: On the appearance of the fractional derivative in the behaviour of real materials. Trans. ASME’84 51(4), 294–298 (1984). https://doi.org/10.1115/1.3167615
Oustaloup, A., Levron, F., Mathieu, B., Nanot, F.: Frequency-band complex noninteger differentiator: characterization and synthesis. IEEE Trans. Circ. Syst. Fundam. Theory Appl. 47(1), 25–40 (2000). https://doi.org/10.1109/81.817385
Miller, K., Ross, B.: An Introduction to the Fractional Calculus and Fractional Differential Equations. Wiley, Hoboken (1993)
Magin, R.L.: Fractional Calculus in Bioengineering. Begell House Publishers, Danbury (2006)
Hilfer, R.: Applications of Fractional Calculus in Physics. World Scientific, Singapore (2000). https://doi.org/10.1142/3779
West, B., Bologna, M., Grigolini, P.: Physics of Fractal Operators. Springer, New York (2003). https://doi.org/10.1007/978-0-387-21746-8
Petras, I.: Fractional-Order Nonlinear Systems. Springer, New York (2011). https://doi.org/10.1007/978-3-642-18101-6_4
Podlubny, I.: Fractional Differential Equations. Academic Press, San Diego (1999). https://doi.org/10.1155/2013/802324
Valério, D., Costa, J.: An Introduction to Fractional Control. IET, London (2013)
Sheng, H., Chen, Y.D., Qiu, T.S.: Fractional Processes and Fractional-Order Signal Processing. Springer, London (2012). https://doi.org/10.1007/978-1-4471-2233-3
Mozyrska, D., Torres, D.F.M.: Modified optimal energy and initial memory of fractional continuous-time linear systems. Sig. Process. 91(3), Special Issue: SI, 379–385 (2011). https://doi.org/10.1016/j.sigpro.2010.07.016
Monje, C., Vinagre, B., Feliu, V., Chen, Y.: Tuning and autotuning of fractional order controllers for industry applications. Control Eng. Pract. 16(7), 798–812 (2008). https://doi.org/10.1016/j.conengprac.2007.08.006
Kalaba, R., Spingarn, K.: Control, Identification, and Input Optimization. Plenum Press, New York (1982)
Ljung, L.: System Identification: Theory for the User. Prentice Hall Inc., Upper Saddle River (1999)
Hussain, M.: Review of the applications of neural networks in chemical process control-simulation and on-line implementation. Artif. Intell. Eng. 13, 55–68 (1999). https://doi.org/10.1016/S0954-1810(98)00011-9
Gevers, M., Ljung, L.: Optimal experiment designs with respect to the intended model application. Automatica 22(5), 543–554 (1986)
Bombois, X., Scorletti, G., Gevers, M., Van den Hof, P.M.J., Hildebrand, R.: Least costly identification experiment for control. Automatica 42(10), 1651–1662 (2006). https://doi.org/10.1016/j.automatica.2006.05.016
Bombois, X., Hjalmarsson, H., Scorletti, G.: Identification for robust deconvolution filtering. Automatica 46(3), 577–584 (2010)
Rivera, D., Lee, H., Braun, M., Mittelmann, H.: Plant friendly system identification: a challenge for the process industries. In: Proceeding of the SYSID 2003, Rotterdam, The Netherlands, pp. 917–922 (2003)
Narasimhan, S., Rengaswamy, R.: Plant friendly input design: convex relaxation and quality. IEEE Trans. Autom. Control 56, 1467–1472 (2011)
Potters, M.G., Bombois, X., Forgione, M., Modén, P.E., Lundh, M., Hjalmarsson, H., Van den Hof, P.M.J.: Optimal experiment design in closed loop with unknown, nonlinear and implicit controllers using stealth identification. In: Proceedings of European Control Conference, Strasbourg, France, pp. 726–731 (2014)
Larsson, C.A., Rojas, C.R., Bombois, X., Hjalmarsson, H.: Experiment evaluation of model predictive control with excitation (MPC-X) on an industrial depropanizer. J. Process Control 31, 1–16 (2015)
Annergren, M., Larson, C.A., Hjalmarsson, H., Bombois, X., Wahlberg, B.: Application-oriented input design in system identification. Optimal input design for control. IEEE Control Syst. Mag. 37, 31–56 (2017)
Jakowluk, W.: Fractional-order linear systems modeling in time and frequency domains. In: 16th IFIP TC8 International Conference in Computer Information Systems and Industrial Management, pp. 502–513. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-59105-6_43
Jakowluk, W.: Optimal input signal design for a second order dynamic system identification subject to D-efficiency constraints. In: 14th IFIP TC8 International Conference in Computer Information Systems and Industrial Management, pp. 351–362. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-319-24369-6_29
Jakowluk, W.: Plant friendly input design for parameter estimation in an inertial system with respect to D-efficiency constraints. Entropy 16(11), 5822–5837 (2014). https://doi.org/10.3390/e16115822
Jakowluk, W.: Free final time input design problem for robust entropy-like system parameter estimation. Entropy 20(7), 528 (2018). https://doi.org/10.3390/e20070528
Mozyrska, D.: Multiparameter fractional difference linear control systems. Discret. Dyn. Nat. Soc. 2014, 8 (2014). https://doi.org/10.1155/2014/183782
Tricaud, C., Chen, Y.: Solving fractional order optimal control problems in Riots\(\_95\) a general-purpose optimal control problem solver. In: 3rd IFAC Workshop on Fractional Differentiation and Its Applications, Ankara, Turkey (2008)
Schwartz, A., Polak, E., Chen, Y.: Riots a MATLAB toolbox for solving optimal control problems. Version 1.0 for Windows (1997). http://www.schwartz-home.com/RIOTS/
Kaczorek, T.: Minimum energy control of fractional positive continuous-time linear systems using Caputo-Fabrizio definition. Bull. Pol. Acad. Sci. Tech. Sci. 65, 45–51 (2017). https://doi.org/10.1515/bpasts-2017-0006
Mozyrska, D., Torres, D.F.M.: Modified optimal energy and initial memory of fractional continuous-time linear systems. Sig. Process. 91, Special Issue: SI, 379–385 (2011). https://doi.org/10.1016/j.sigpro.2010.07.016
Acknowledgement
The present study was supported by a grant S/WI/3/18 from the Bialystok University of Technology and funded from the resources for research by the Ministry of Science and Higher Education.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Jakowluk, W. (2020). Design of an Optimal Input Signal for Parameter Estimation of Linear Fractional-Order Systems. In: Malinowska, A., Mozyrska, D., Sajewski, Ł. (eds) Advances in Non-Integer Order Calculus and Its Applications. RRNR 2018. Lecture Notes in Electrical Engineering, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-030-17344-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-17344-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17343-2
Online ISBN: 978-3-030-17344-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)