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International Journal of Control Volume 62, 1995 - Issue 1
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Original Articles

Nonlinear system identification using neural state space models, applicable to robust control design

JOHAN A. K. SUYKENS Katholieke Universiteit Leuven, Department of Electrical Engineering, ESAT-S1STA, Kardinaal Mercierlaan 94, B-3001, Leuven (Heverlee), Belgium Phone: Tel: 32/16/32 11 11; Fax: Tel: 32/16/32 11 11; E-mail: email:[email protected]
,
BART L. R. DE MOOR Katholieke Universiteit Leuven, Department of Electrical Engineering, ESAT-S1STA, Kardinaal Mercierlaan 94, B-3001, Leuven (Heverlee), Belgium Phone: Tel: 32/16/32 11 11; Fax: Tel: 32/16/32 11 11; E-mail: email:[email protected]
&
JOOS VANDEWALLE Katholieke Universiteit Leuven, Department of Electrical Engineering, ESAT-S1STA, Kardinaal Mercierlaan 94, B-3001, Leuven (Heverlee), Belgium Phone: Tel: 32/16/32 11 11; Fax: Tel: 32/16/32 11 11; E-mail: email:[email protected]
Pages 129-152 | Received 15 Dec 1992, Published online: 24 Feb 2007

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MUKUL AGARWAL. (1997) Combining neural and conventional paradigms for modelling,prediction and control. International Journal of Systems Science 28:1, pages 65-81.
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Articles from other publishers (65)

Hong-Wei Li, Yi-Qing Ni, You-Wu Wang, Zheng-Wei Chen, En-Ze Rui & Zhao-Dong Xu. (2024) Modeling of forced-vibration systems using continuous-time state-space neural network. Engineering Structures 302, pages 117329.
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Jan H. Hoekstra, Bence Cseppento, Gerben I. Beintema, Maarten Schoukens, Zsolt Kollár & Roland Tóth. (2023) Computationally Efficient Predictive Control Based on ANN State-Space Models. Computationally Efficient Predictive Control Based on ANN State-Space Models.
Gerben I. Beintema, Maarten Schoukens & Roland Tóth. (2023) Deep subspace encoders for nonlinear system identification. Automatica 156, pages 111210.
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Jérémy Pinguet, Philippe Feyel & Guillaume Sandou. (2023) A data-based neural controller training method with tunable stability margin using multi-objective optimization. IFAC-PapersOnLine 56:2, pages 3092-3099.
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Rishi Ramkannan, Gerben I. Beintema, Roland Tóth & Maarten Schoukens. (2023) Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach. IFAC-PapersOnLine 56:2, pages 5146-5151.
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Arash Yoosefdoost, Syeda Manjia Tahsien, S. Andrew Gadsden, William David Lubitz & Mitra Kaviani. 2023. Proceedings of the Canadian Society of Civil Engineering Annual Conference 2022. Proceedings of the Canadian Society of Civil Engineering Annual Conference 2022 1071 1099 .
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Alexandre Hache, Maxime Thieffry, Mohamed Yagoubi & Philippe Chevrel. (2022) Control-Oriented Neural State-Space Models for State-Feedback Linearization and Pole Placement. Control-Oriented Neural State-Space Models for State-Feedback Linearization and Pole Placement.
Aowabin Rahman, Jan Drgona, Aaron Tuor & Jan Strube. (2022) Neural Ordinary Differential Equations for Nonlinear System Identification. Neural Ordinary Differential Equations for Nonlinear System Identification.
Adil Brouri, Laila Kadi & Kenza Lahdachi. (2021) Identification of nonlinear system composed of parallel coupling of Wiener and Hammerstein models. Asian Journal of Control 24:3, pages 1152-1164.
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Pil Rip Jeon, Moo Sun Hong & Richard D. Braatz. (2022) Compact neural network modeling of nonlinear dynamical systems via the standard nonlinear operator form. Computers & Chemical Engineering 159, pages 107674.
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Mohammad Fahim Shakib, Alexander Yu. Pogromsky, Alexey Pavlov & Nathan van de Wouw. (2022) Computationally efficient identification of continuous-time Lur’e-type systems with stability guarantees. Automatica 136, pages 110012.
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Maarten Schoukens. (2021) Improved Initialization of State-Space Artificial Neural Networks. Improved Initialization of State-Space Artificial Neural Networks.
Anastasia Nikolakopoulou, Moo Sun Hong & Richard D. Braatz. (2021) Output Feedback Control and Observer Design for Dynamic Artificial Neural Networks. Output Feedback Control and Observer Design for Dynamic Artificial Neural Networks.
Hoang Hai Nguyen, Tim Zieger, Sandra C. Wells, Anastasia Nikolakopoulou, Richard D. Braatz & Rolf Findeisen. (2021) Stability Certificates for Neural Network Learning-based Controllers using Robust Control Theory. Stability Certificates for Neural Network Learning-based Controllers using Robust Control Theory.
Anastasia Nikolakopoulou, Moo Sun Hong & Richard D. Braatz. (2020) Feedback Control of Dynamic Artificial Neural Networks Using Linear Matrix Inequalities. Feedback Control of Dynamic Artificial Neural Networks Using Linear Matrix Inequalities.
Hoang Hai Nguyen, Janine Matschek, Tim Zieger, Anton Savchenko, Navid Noroozi & Rolf Findeisen. (2020) Towards nominal stability certification of deep learning-based controllers. Towards nominal stability certification of deep learning-based controllers.
Jun Xu, Qinghua Tao, Zhen Li, Xiangming Xi, Johan A.K. Suykens & Shuning Wang. (2020) Efficient hinging hyperplanes neural network and its application in nonlinear system identification. Automatica 116, pages 108906.
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Himanshu Kumar, Parul Arora & Bijaya Ketan Panigrahi. (2018) Wind Forecasting:Hybrid Statistical and Deep Neural Network Approaches. Wind Forecasting:Hybrid Statistical and Deep Neural Network Approaches.
Kwang-Ki K. Kim, Ernesto Ríos Patrón & Richard D. Braatz. (2018) Standard representation and unified stability analysis for dynamic artificial neural network models. Neural Networks 98, pages 251-262.
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Mina Ferizbegovic, Miguel Galrinho & Håkan Hjalmarsson. (2018) Nonlinear FIR Identification with Model Order Reduction Steiglitz-McBride. IFAC-PapersOnLine 51:15, pages 646-651.
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Maarten Schoukens & Koen Tiels. (2017) Identification of block-oriented nonlinear systems starting from linear approximations: A survey. Automatica 85, pages 272-292.
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Rishi Relan, Koen Tiels & Johan Schoukens. (2016) Dealing with Transients due to Multiple Experiments in Nonlinear System Identification**The authors are with the ELEC Department of the Vrije Universiteit Brussel (VUB),Brussels B-1050, Belgium. The corresponding author can be contacted at the ([email protected]). This work was supported in part by the IWT-SBO BATTLE 639, Fund for Scientific Research (FWO-Vlaanderen), by the Flemish Government (Methusalem), the Belgian Government through the Inter university Poles of Attraction (IAP VII) Program, and by the ERC advanced grant SNLSID, under contract 320378.. IFAC-PapersOnLine 49:13, pages 181-186.
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Mehmet Ölmez & Cüneyt Güzeliş. (2013) Exploiting Chaos in Learning System Identification for Nonlinear State Space Models. Neural Processing Letters 41:1, pages 29-41.
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Anna Marconato, Jonas Sjoberg, Johan A. K. Suykens & Johan Schoukens. (2014) Improved Initialization for Nonlinear State-Space Modeling. IEEE Transactions on Instrumentation and Measurement 63:4, pages 972-980.
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Anna Marconato, Jonas Sjöberg, Johan Suykens & Johan Schoukens. (2012) Identification of the Silverbox Benchmark Using Nonlinear State-Space Models. IFAC Proceedings Volumes 45:16, pages 632-637.
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Kwang Ki Kevin Kim, Ernesto Rios Patron & Richard D. Braatz. (2011) Universal approximation with error bounds for dynamic artificial neural network models: A tutorial and some new results. Universal approximation with error bounds for dynamic artificial neural network models: A tutorial and some new results.
Johan Paduart, Lieve Lauwers, Jan Swevers, Kris Smolders, Johan Schoukens & Rik Pintelon. (2010) Identification of nonlinear systems using Polynomial Nonlinear State Space models. Automatica 46:4, pages 647-656.
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A. Alessandri, R. Bolla, M. Gaggero & M. Repetto. (2009) Modeling and Identification of Nonlinear Dynamics for Freeway Traffic by Using Information From a Mobile Cellular Network. IEEE Transactions on Control Systems Technology 17:4, pages 952-959.
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Yong Liu & J. Jim Zhu. (2008) Continuous-time nonlinear system identification using neural network. Continuous-time nonlinear system identification using neural network.
A. Alessandri, R. Bolla, M. Gaggero & M. Repetto. (2007) Identification of freeway traffic dynamics using fluid and black-box nonlinear models. Identification of freeway traffic dynamics using fluid and black-box nonlinear models.
Jan Bendtsen. (2007) A Right Coprime Factorization of Neural State Space Models. A Right Coprime Factorization of Neural State Space Models.
MEIQIN LIU. (2011) DYNAMIC OUTPUT FEEDBACK STABILIZATION FOR NONLINEAR SYSTEMS BASED ON STANDARD NEURAL NETWORK MODELS. International Journal of Neural Systems 16:04, pages 305-317.
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N. Yadaiah & G. Sowmya. (2006) Neural Network Based State Estimation of Dynamical Systems. Neural Network Based State Estimation of Dynamical Systems.
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Yanjun Shen, Bingwen Wang, Fangxin Chen & Liang Cheng. (2004) A New Multi-output Neural Model with Tunable Activation Function and its Applications. Neural Processing Letters 20:2, pages 85-104.
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Min Han, Zhiwei Shi & Wei Wang. 2004. Advances in Neural Networks - ISNN 2004. Advances in Neural Networks - ISNN 2004 200 205 .
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J.D. Bendtsen & K. Trangbaek. (2002) Robust quasi-LPV control based on neural state-space models. IEEE Transactions on Neural Networks 13:2, pages 355-368.
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Qing WU, Yongji WANG & Hong WANG. (2002) AN EXTENDED LINEARIZED NEURAL STATE SPACE BASED MODELING AND CONTROL. IFAC Proceedings Volumes 35:1, pages 975-980.
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Yangdong Pan, Su W Sung & Jay H Lee. (2001) Data-based construction of feedback-corrected nonlinear prediction model using feedback neural networks. Control Engineering Practice 9:8, pages 859-867.
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Chua-Liang Lin & Tsai-Yuan Lin. (2001) An H/sub ∞/ design approach for neural net-based control schemes. IEEE Transactions on Automatic Control 46:10, pages 1599-1605.
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R.B. Vilim, H.E. Garcia & F.W. Chen. (2001) An identification scheme combining first principle knowledge, neural networks, and the likelihood function. IEEE Transactions on Control Systems Technology 9:1, pages 186-199.
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A.G. Parlos, S.K. Menon & A. Atiya. (2001) An algorithmic approach to adaptive state filtering using recurrent neural networks. IEEE Transactions on Neural Networks 12:6, pages 1411-1432.
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M O T Cole, P S Keogh & C R Burrows. (2006) Fault-tolerant control of rotor/magnetic bearing systems using reconfigurable control with built-in fault detection. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 214:12, pages 1445-1465.
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A.G. Parlos, O.T. Rais & A.F. Atiya. (2000) Multi-step-ahead prediction using dynamic recurrent neural networks. Neural Networks 13:7, pages 765-786.
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Yangdong Pan, Su W. Sung & Jay H. Lee. (2000) Nonlinear Dynamic Trend Modeling Using Feedback Neural Networks and Prediction Error Minimization. IFAC Proceedings Volumes 33:10, pages 827-832.
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A.F. Atiya & A.G. Parlos. (2000) New results on recurrent network training: unifying the algorithms and accelerating convergence. IEEE Transactions on Neural Networks 11:3, pages 697-709.
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J.A.K. Soykens, J. Vandewalle & B. De Moor. (1999) Lur'e systems with multilayer perceptron and recurrent neural networks: absolute stability and dissipativity. IEEE Transactions on Automatic Control 44:4, pages 770-774.
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J.M. Zamarreño & P. Vega. (1999) Neural predictive control. Application to a highly non-linear system. Engineering Applications of Artificial Intelligence 12:2, pages 149-158.
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M.A. Brdys & G.J. Kulawski. (1999) Dynamic neural controllers for induction motor. IEEE Transactions on Neural Networks 10:2, pages 340-355.
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L. Sun, W. Liu & A. Sano. (1999) Identification of a dynamical system with input nonlinearity. IEE Proceedings - Control Theory and Applications 146:1, pages 41-51.
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John Sum, Lai-wan Chan, Chi-sing Leung & Gilbert H. Young. (1998) Extended Kalman Filter–Based Pruning Method for Recurrent Neural Networks. Neural Computation 10:6, pages 1481-1505.
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Shaohua Tan, Yi Yu, Johan Suykens & Joos Vandewalle. 1998. Control and Dynamic System. Control and Dynamic System 383 433 .
H. Verrelst, K. Van Acker, J. Suykens, B. Motmans, B. De Moor & J. Vandewalle. (1998) Application of NLq Neural Control Theory to a Ball and Beam System. European Journal of Control 4:2, pages 148-157.
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Johan A.K. Suykens, Bart L.R. De Moor & Joos Vandewalle. (1997) NL q Theory: A Neural Control Framework with Global Asymptotic Stability Criteria. Neural Networks 10:4, pages 615-637.
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I. Kamwa, S. Martin & R. Grondin. (1997) Comparisons of artificial neural networks for on-line identification of a nonlinear multivariate electromechanical process. Comparisons of artificial neural networks for on-line identification of a nonlinear multivariate electromechanical process.
J.A.K. Suykens, B. De Moor & J. Vandewalle. (1997) Robust NL/sub q/ neural control theory. Robust NL/sub q/ neural control theory.
J.A.K. Suykens, J. Vandewalle & B.L.R. De Moor. (1997) NL/sub q/ theory: checking and imposing stability of recurrent neural networks for nonlinear modeling. IEEE Transactions on Signal Processing 45:11, pages 2682-2691.
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A. Alessandri & T. Parisini. (1997) Nonlinear modeling of complex large-scale plants using neural networks and stochastic approximation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 27:6, pages 750-757.
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P. Lindskog. 1997. Fuzzy Model Identification. Fuzzy Model Identification 3 50 .
J.A.K. Suykens & J. Vandewalle. (1995) On the identification of a chaotic system by means of recurrent neural state space models. On the identification of a chaotic system by means of recurrent neural state space models.
J.A.K. Suykens & J. Vandewalle. (1995) Global asymptotic stability criteria for multilayer recurrent neural networks with applications to modelling and control. Global asymptotic stability criteria for multilayer recurrent neural networks with applications to modelling and control.
J.A.K. Suykens & J. Vandewalle. (1995) Learning a simple recurrent neural state space model to behave like Chua's double scroll. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 42:8, pages 499-502.
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