Fractional-order Hammerstein state-space modeling of nonlinear dynamic systems from input–output measurements

ISA Transactions - Tập 96 - Trang 177-184 - 2020
Mohammad-Reza Rahmani1, Mohammad Farrokhi2
1School of Electrical Engineering, Iran University of Science and Technology, Tehran, 1684613114, Iran
2School of Electrical Engineering, Center of Excellence for Modeling and Control of Complex Systems, Iran University of Science and Technology, Tehran 1684613114, Iran

Tài liệu tham khảo

Giri, 2010 Tang, 2014, Identification of nonlinear system using extreme learning machine based Hammerstein model, Commun Nonlinear Sci Numer Simul, 19, 3171, 10.1016/j.cnsns.2013.12.006 Gotmare, 2014, Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model, Expert Syst Appl, 42, 2538, 10.1016/j.eswa.2014.10.040 Liu, 2014, Modeling of a class of nonlinear dynamic system, Sens Transducers, 169, 153 Hu, 2014, Least squares based iterative identification algorithms for input nonlinear controlled autoregressive systems based on the auxiliary model, Nonlinear Dynam, 76, 777, 10.1007/s11071-013-1168-1 Hong, 2014, Single-carrier frequency domain equalisation for hammerstein communication systems using complex-valued neural networks, IEEE Trans Signal Process, 62, 4467, 10.1109/TSP.2014.2333555 Zhao, 2014, Hammerstein identification of supercharged boiler superheated steam pressure using Laguerre-Fuzzy model, Int J Heat Mass Transfer, 70, 33, 10.1016/j.ijheatmasstransfer.2013.10.056 Sun, 2013, A novel APSO-aided maximum likelihood identification method for Hammerstein systems, Nonlinear Dynam, 73, 449, 10.1007/s11071-013-0800-4 Deng, 2014, Newton Iterative identification method for an input nonlinear finite impulse response system with moving average noise using the key variables separation technique, Nonlinear Dynam, 76, 1195, 10.1007/s11071-013-1202-3 Zhai, 2006, Continuous-time hammerstein and wiener modeling under second-order static nonlinearity for periodic process signals, Comput Chem Eng, 31, 1, 10.1016/j.compchemeng.2006.04.003 Garnier, 2014, The advantages of directly identifying continuous-time transfer function models in practical applications, Internat J Control, 87, 1319, 10.1080/00207179.2013.840053 Zhou, 2017, Hierarchical recursive least squares parameter estimation of non-uniformly sampled hammerstein nonlinear systems based on Kalman filter, J Franklin Inst B, 354, 4231, 10.1016/j.jfranklin.2017.02.010 Wang, 2015, Joint estimation of states and parameters for an input nonlinear state-space system with colored noise using the filtering technique, Circuits Systems Signal Process Ding, 2016, Kalman state filtering based least squares iterative parameter estimation for observer canonical state space systems using decomposition, J Comput Appl Math, 301, 135, 10.1016/j.cam.2016.01.042 Fu, 2013, Robust on-line nonlinear systems identification using multilayer dynamic neural networks with two-time scales, Neurocomputing, 113, 16, 10.1016/j.neucom.2012.11.041 Janakiraman, 2013, A lyapunov based stable online learning algorithm for nonlinear dynamical systems using extreme learning machines, 1 Wu, 2015, Identification and control of a fuel cell system in the presence of time-varying disturbances, Ind Eng Chem Res, 54, 7141, 10.1021/acs.iecr.5b01783 Wang, 2015, Identification of chaotic system using Hammerstein-ELM model, Nonlinear Dynam, 81, 1081, 10.1007/s11071-015-2050-0 Salhi, 2015, A recursive parametric estimation algorithm of multivariable nonlinear systems described by Hammerstein mathematical models, Appl Math Model, 39, 4951, 10.1016/j.apm.2015.03.050 Rahmani, 2017, Robust identification of neuro-fractional-order hammerstein systems, 27 Wang, 2015, Recursive parameter and state estimation for an input nonlinear state space system using the hierarchical identification principle, Signal Process, 117, 208, 10.1016/j.sigpro.2015.05.010 Rahmani, 2017, Rahmani m-r farrokhi m nonlinear dynamic system identification using neuro-fractional-order Hammerstein model, Trans Inst Meas Control Rahmani, 2017, Identification of neuro-fractional hammerstein systems: a hybrid frequency-/time-domain approach, Soft Comput, 1 Oldham, 1974 Narang, 2011, Continuous-time model identification of fractional-order models with time delays, IET Control Theory Appl, 5, 900, 10.1049/iet-cta.2010.0718 Aoun, 2002, System identification using fractional Hammerstein models Zhao, 2014, Complete parametric identification of fractional order hammerstein systems, 1 Liao, 2012, Subspace identification for fractional order hammerstein systems based on instrumental variables, Int J Control Autom Syst, 10, 947, 10.1007/s12555-012-0511-5 Hammar, 2015, Fractional hammerstein system identification using particle swarm optimization, 1 Ivanov, 2015, Identification discrete fractional order hammerstein systems, 1 Pintelon, 2012 Podlubny, 1999 Li, 2009, Mittag–Leffler Stability of fractional order nonlinear dynamic systems, Automatica, 45, 1965, 10.1016/j.automatica.2009.04.003 Djamah, 2009, State space realization of fractional order systems, 37 Pintelon, 2008, Frequency-domain approach to continuous-time system identification: Some practical aspects, 215 Aguila-Camacho, 2014, Lyapunov functions for fractional order systems, Commun Nonlinear Sci Numer Simul, 19, 2951, 10.1016/j.cnsns.2014.01.022 Kudva, 1973, Synthesis of an adaptive observer using Lyapunov’s direct method, Internat J Control, 18, 1201, 10.1080/00207177308932593 Delavari, 2011, Stability analysis of Caputo fractional-order nonlinear systems revisited, Nonlinear Dynam, 67, 2433, 10.1007/s11071-011-0157-5 Duarte-Mermoud, 2015, Using general quadratic Lyapunov functions to prove Lyapunov uniform stability for fractional order systems, Commun Nonlinear Sci Numer Simul, 22, 650, 10.1016/j.cnsns.2014.10.008 Wang, 2017, Fractional order Barbalat’s lemma and its applications in the stability of fractional order nonlinear systems, Math Model Anal, 22, 503, 10.3846/13926292.2017.1329755 Er-Wei, 2003, Frequency domain identification of Hammerstein models, IEEE Trans Automat Control, 48, 530, 10.1109/TAC.2003.809803 Poinot, 2004, Identification of fractional systems using an output-error technique, Nonlinear Dynam, 38, 133, 10.1007/s11071-004-3751-y Tang, 2016, A changing forgetting factor RLS for online identification of nonlinear systems based on ELM–Hammerstein model, Neural Comput Appl, 1 Yan, 2011, Robust model predictive control of nonlinear affine systems based on a two-layer recurrent neural network, 24 Juang, 2007, Recurrent fuzzy network design using hybrid evolutionary learning algorithms, Neurocomputing, 70, 3001, 10.1016/j.neucom.2006.08.010 De Moor, 1997, DAISY: A database for identification of systems, J A, 4