More Web Proxy on the site http://driver.im/

article

A Novel Adaptive NARMA-L2 Controller Based on Online Support Vector Regression for Nonlinear Systems

Authors:

Gülay Öke GünelAuthors Info & Claims

Neural Processing Letters, Volume 44, Issue 3

Pages 857 - 886

https://doi.org/10.1007/s11063-016-9500-7

Published: 01 December 2016 Publication History

Abstract

In this study, a novel nonlinear autoregressive moving average (NARMA)-L2 controller based on online support vector regression (SVR) is proposed. The main idea is to obtain a SVR based NARMA-L2 model of a nonlinear single input single output system (SISO) by decomposing a single SVR which estimates the nonlinear autoregressive with exogenous inputs (NARX) model of the system. Consequently, using the obtained SVR-NARMA-L2 submodels, a NARMA-L2 controller is designed. The performance of the proposed SVR based NARMA-L2 controller has been evaluated by simulations carried out on a bioreactor system, and the results show that the SVR based NARMA-L2 model and controller attain good modelling and control performances. Robustness of the controller in the case of system parameter uncertainty and measurement noise have also been examined.

References

[1]

Narendra KS, Mukhopadhyay S (1997) Adaptive control using neural networks and approximate models. IEEE Trans Neural Netw 8(3):475---485.

Digital Library

[2]

Majstorovic M, Nikolic I, Radovic J, Kvascev G (2008) Neural network control approach for a two-tank system. In: 9th Symposium on neural network applications in electrical engineering (NEUREL 2008), Belgrade, Serbia

[3]

Pedro JO, Nyandoro OTC, John S (2009) Neural network based feedback linearisation slip control of an anti-lock braking system. In: Asian control conference (ASCC 2009), Hong Kong, China

[4]

De Jesus O, Pukrittayakamee A, Hagan MT (2001) A comparison of neural network control algorithms. In: International joint conference on neural networks (IJCNN'01), Washington, DC

[5]

Pukrittayakamee A, De Jesus O, Hagan MT (2002) Smoothing the control action for NARMA-L2 controllers. In: 45th Midwest symposium on circuits and systems (MWSCAS 2002), Tulsa, OK

[6]

Hagan MT, Demuth HB, De Jesus O (2002) An introduction to the use of neural networks in control systems. Int J Robust Nonlinear Control 12(11):959---985.

[7]

Wahyudi M, Mokri SS, Shafie AA (2008) Real time implementation of NARMA L2 feedback linearization and smoothed NARMA L2 controls of a single link manipulator. In: International conference on computer and communication engineering, Kuala Lumpur, Malaysia

[8]

Akbarimajd A, Kia S (2010) NARMA-L2 controller for 2-DoF underactuated planar manipulator. In: International conference on control, automation, robotics and vision (ICARCV 2010), Singapore

[9]

Vesselenyi T, Dzitac S, Dzitac I, Manolescu MJ (2007) Fuzzy and neural controllers for a pneumatic actuator. Int J Comput Commun Control 2(4):375---387

[10]

Efe MO, Kaynak O (2000) A comparative study of soft-computing methodologies in identification of robotic manipulators. Robot Auton Syst 30(3):221---230

[11]

Efe MO, Kaynak O (1999) A comparative study of neural network structures in identification of nonlinear systems. Mechatronics 9(3):287---300.

[12]

Denai MA, Palis F, Zeghbib A (2004) ANFIS based modelling and control of non-linear systems: a tutorial. In: IEEE international conference on systems, man and cybernetics

[13]

Gretton A, Doucet A, Herbrich R, Rayner PJW, Scholkopf B (2001) Support vector regression for black-box system identification. In: IEEE workshop on statistical signal processing (SSP 2001), Singapore

[14]

Rong HN, Zhang GX, Zhang CF (2005) Application of support vector machines to nonlinear system identification. In: International symposium on autonomous decentralized systems (ISADS 2005), Chengdu, China

[15]

Suykens JAK (2001) Nonlinear modelling and support vector machines. In: IEEE instrumentation and measurement technology conference (IMTC 2001), Budapest, Hungary

[16]

Ucak K, Oke G (2011) Adaptive PID controller based on online LSSVR with kernel tuning. In: International symposium on innovations in intelligent systems and applications (INISTA 2011), Istanbul, Turkey

[17]

Iplikci S (2010) A comparative study on a novel model-based PID tuning and control mechanism for nonlinear systems. Int J Robust Nonlinear Control 20(13):1483---1501.

[18]

Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988---999.

Digital Library

[19]

Wanfeng S, Shengdun Z, Yajing S (2008) Adaptive PID controller based on online LSSVM identification. In: IEEE/ASME international conference on advanced intelligent mechatronics (AIM 2008), Xian, China

[20]

Zhao J, Li P, Wang Xs (2009) Intelligent PID controller design with adaptive criterion adjustment via least squares support vector machine. In: 21st Chinese control and decision conference (CCDC 2009), Guilin, China

Digital Library

[21]

Yuan XF, Wang YN, Wu LH (2008a) Composite feedforward---feedback controller for generator excitation system. Nonlinear Dyn 54(4):355---364.

[22]

Takao K, Yamamoto T, Hinamoto T (2006) A design of PID controllers with a switching structure by a support vector machine. In: 2006 IEEE international joint conference on neural network (IJCNN), Vancouver, Canada

[23]

Liu XJ, Yi JQ, Zhao DB (2005) Adaptive inverse control system based on least squares support vector machines. In: 2nd international symposium on neural networks (ISNN 2005), Chongqing, China

Digital Library

[24]

Wang H, Pi DY, Sun YX (2007) Online SVM regression algorithm-based adaptive inverse control. Neurocomputing 70(4---6):952---959.

Digital Library

[25]

Yuan XF, Wang YN, Wu LH (2008b) Adaptive inverse control of excitation system with actuator uncertainty. Neural Process Lett 27(2):125---136.

Digital Library

[26]

Zhao ZC, Liu ZY, Xia ZM, Zhang JG (2012) Internal model control based on LS-SVM for a class of nonlinear process. In: International conference on solid state devices and materials science (SSDMS), Macao, China

[27]

Zhong WM, Pi DY, Sun YX, Xu C, Chu SZ (2006) SVM based internal model control for nonlinear systems. In: 3rd International symposium on neural networks (ISNN 2006), Chengdu, China

Digital Library

[28]

Datta A, Ochoa J (1996) Adaptive internal model control: design and stability analysis. Automatica 32(2):261---266.

Digital Library

[29]

Sun CY, Song JY (2007) An adaptive internal model control based on LS-SVM. In: International symposium on neural networks (ISNN 2007), Nanjing, China

Digital Library

[30]

Wang YN, Yuan XF (2008) SVM approximate-based internal model control strategy. Acta Autom Sin 34(2):172---179.

[31]

Iplikci S (2006a) Online trained support vector machines-based generalized predictive control of non-linear systems. Int J Adapt Control Signal Process 20(10):599---621.

[32]

Iplikci S (2006b) Support vector machines-based generalized predictive control. Int J Robust Nonlinear Control 16(17):843---862.

[33]

Camacho EF (1993) Constrained generalized predictive control. IEEE Trans Autom Control 38(2):327---332.

[34]

Clarke DW, Mohtadi C (1989) Properties of generalized predictive control. Automatica 25(6):859---875.

Digital Library

[35]

Camacho EF, Bordons C (1999) Model predictive control. Springer, New York

Digital Library

[36]

Clarke DW, Mohtadi C, Tuffs PS (1987) Generalized predictive control--Part I. The basic algorithm. Automatica 23(2):137---148.

Digital Library

[37]

Zhiying D, Xianfang W (2008) Nonlinear generalized predictive control based on online SVR. In: 2nd International symposium on intelligent information technology application, Shanghai, China

Digital Library

[38]

Shin J, Kim HJ, Park S, Kim Y (2010) Model predictive flight control using adaptive support vector regression. Neurocomputing 73(4---6):1031---1037.

Digital Library

[39]

Abu-Rub H, Awwad A (2009) Artificial neural networks and fuzzy logic based control of AC motors. In: IEEE international electric machines and drives conference (IEMDC 2009), Miami, FL

[40]

Norgaard M, Ravn O, Poulsen NK, Hansen LK (2000) Neural networks for modelling and control of dynamic systems. Springer, London

Digital Library

[41]

Ng GW (1997) Application of neural networks to adaptive control of nonlinear systems. In: UMIST control systems centre series, 4 (Book 4), Research Studies Press Ltd, Taunton, UK

[42]

Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273---297.

[43]

Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. In: Annual conference on neural information processing systems (NIPS), Denver, CO

Digital Library

[44]

Vapnik V, Golowich SE, Smola A (1997) Support vector method for function approximation, regression estimation, and signal processing. In: Annual conference on neural information processing systems (NIPS), Denver, CO

Digital Library

[45]

Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199---222.

Digital Library

[46]

Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge

Digital Library

[47]

Martin M (2002) On-line support vector machine regression. In: 13th European conference on machine learning (ECML 2002), Helsinki, Finland

Digital Library

[48]

Ma J, Theiler J, Perkins S (2003) Accurate online support vector regression. Neural Comput 15(11):2683---2703

Digital Library

[49]

Luenberger DG, Ye Y (2008) Linear and nonlinear programming, 3rd edn. Springer, New York

[50]

Griva I, Nash SG, Sofer A (2009) Linear and nonlinear optimization, 2nd edn. SIAM, Philadelphia

[51]

Nocedal J, Wright SJ (1999) Numerical optimization. Springer, New York

[52]

Cauwenberghs G, Poggio T (2001) Incremental and decremental support vector machine learning. In: Annual neural information processing systems conference (NIPS), Denver, CO

Digital Library

[53]

Ungar LH (1990) Neural networks for control. In: Miller WT III, Sutton RS, Werbos PJ (eds) A bioreactor benchmark for adaptive network based process control. MIT Press, Cambridge, pp 387---402

Digital Library

[54]

Efe MO, Abadoglu E, Kaynak O (1999) A novel analysis and design of a neural network assisted nonlinear controller for a bioreactor. Int J Robust Nonlinear Control 9(11):799---815.

[55]

Efe MO (2007) Discrete time fuzzy sliding mode control of a biochemical process. In: 9th WSEAS international conference on automatic control, modeling and simulation (ACMOS'07), Istanbul, Turkey

Digital Library

Cited By

Uçak KGünel G(2024)Adaptive stable backstepping controller based on support vector regression for nonlinear systemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107533129:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107533
Uçak KGünel G(2021)Online Support Vector Regression Based Adaptive NARMA-L2 Controller for Nonlinear SystemsNeural Processing Letters10.1007/s11063-020-10403-853:1(405-428)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s11063-020-10403-8
Uçak KGünel G(2021)Model-free MIMO self-tuning controller based on support vector regression for nonlinear systemsNeural Computing and Applications10.1007/s00521-021-06194-133:22(15731-15750)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s00521-021-06194-1
Show More Cited By

A Novel Adaptive NARMA-L2 Controller Based on Online Support Vector Regression for Nonlinear Systems
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
  2. Modeling and simulation
    1. Model development and analysis

Recommendations

Online Support Vector Regression Based Adaptive NARMA-L2 Controller for Nonlinear Systems
Abstract
NARMA model is a simple and effective way to represent nonlinear systems, based on the NARMA model, NARMA-L2 controller is designed and has been successfully applied in the literature. Success of NARMA-L2 controller is directly related to the ...
Generalized self-tuning regulator based on online support vector regression

This paper introduces a novel generalized self-tuning regulator based on online support vector regression (OSVR) for nonlinear systems. The main idea is to approximate the parameters of an adaptive controller by optimizing the regression margin between ...
Model free adaptive support vector regressor controller for nonlinear systems
Abstract
In this study, a novel model free support vector regressor controller (MF-SVRcontroller) is introduced for nonlinear dynamical systems. For the adaptation mechanism, a model free closed-loop margin which is a function of tracking error ...
Highlights
- The gradient effects which occur in NN and ANFIS are vanished in SVR and global extremum is ensured.

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Neural Processing Letters

Neural Processing Letters Volume 44, Issue 3

December 2016

309 pages

ISSN:1370-4621

Issue’s Table of Contents

Copyright © Copyright © 2016 Springer Science+Business Media New York.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2016

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Uçak KGünel G(2024)Adaptive stable backstepping controller based on support vector regression for nonlinear systemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107533129:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107533
Uçak KGünel G(2021)Online Support Vector Regression Based Adaptive NARMA-L2 Controller for Nonlinear SystemsNeural Processing Letters10.1007/s11063-020-10403-853:1(405-428)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s11063-020-10403-8
Uçak KGünel G(2021)Model-free MIMO self-tuning controller based on support vector regression for nonlinear systemsNeural Computing and Applications10.1007/s00521-021-06194-133:22(15731-15750)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s00521-021-06194-1
Uçak KGünel G(2019)Model free adaptive support vector regressor controller for nonlinear systemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2019.02.00181:C(47-67)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1016/j.engappai.2019.02.001
Al-Dunainawi YAbbod MJizany A(2017)A new MIMO ANFIS-PSO based NARMA-L2 controller for nonlinear dynamic systemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2017.04.01662:C(265-275)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.engappai.2017.04.016

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents