Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Computing and Applications
Published in: Neural Computing and Applications 2/2010

01-03-2010 | KES 2008

Neural networks-based adaptive control for a class of nonlinear bioprocesses

Authors: Emil Petre, Dan Selişteanu, Dorin Şendrescu, Cosmin Ionete

Published in: Neural Computing and Applications | Issue 2/2010

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The paper studies the design and analysis of a neural adaptive control strategy for a class of square nonlinear bioprocesses with incompletely known and time-varying dynamics. In fact, an adaptive controller based on a dynamical neural network used as a model of the unknown plant is developed. The neural controller design is achieved by using an input–output feedback linearization technique. The adaptation laws of neural network weights are derived from a Lyapunov stability property of the closed-loop system. The convergence of the system tracking error to zero is guaranteed without the need of network weights convergence. The resulted control method is applied in a depollution control problem in the case of a wastewater treatment bioprocess, belonging to the square nonlinear class, for which kinetic dynamics are strongly nonlinear, time varying and not exactly known.
previous article Advances in design and application of neural networks
next article A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Isidori A (1995) Nonlinear control systems, 3rd edn. Springer-Verlag, BerlinMATH
2.
3.
Sastry S, Bodson M (1989) Adaptive control: stability, convergence and robustness. Prentice-Hall International Inc., Englewood Cliffs, NJMATH
4.
Ioannou PA, Sun J (1996) Robust adaptive control. PTR Prentice Hall, USAMATH
5.
Fidan B, Zhang Y, Ioannou PA (2005) Adaptive control of a class of slowly time varying systems with modelling uncertainties. IEEE Trans Autom Contr 50(6):915–920CrossRefMathSciNet
6.
Narendra KS, Parthasarathy K (1990) Identification and control of dynamical systems using neural networks. IEEE Trans Neural Netw 1:4–27CrossRef
7.
Wang Z, Zhang Y, Fang H (2008) Neural adaptive control for a class of nonlinear systems with unknown dead zone. J Neural Comput Appl 17(4):339–345CrossRef
8.
Funahashi K (1989) On the approximate realization of continuous mappings by neural networks. Neural Netw 2:183–192CrossRef
9.
Haykin S (1994) Neural networks. A comprehensive foundation. Macmillan Publishing Company, Englewood CliffsMATH
10.
Yasser M, Trisanto A, Lu J, Yahagi T (2006) A method of simple adaptive control for nonlinear systems using neural networks. IEICE Trans Fund Electron Commun Comput Sci E89-A(7):2009–2018
11.
Hayakawa T, Haddad WM, Hovakimyan N (2008) Neural network adaptive control for a class of nonlinear uncertain dynamical systems with asymptotic stability guarantees. IEEE Trans Neural Netw 19(1):80–89CrossRef
12.
Bastin G, Dochain D (1990) On-line estimation and adaptive control of bioreactors. Elsevier, Amsterdam
13.
Dochain D, Vanrolleghem P (2001) Dynamical modelling and estimation in wastewater treatment processes. IWA Publishing, London
14.
Boutalis YS, Kosmidou OI (2006) A feedback linearization technique by using neural networks: application to bioprocess control. J Comput Methods Sci Eng 6(5–6):269–282MATH
15.
Caraman S, Barbu M, Arinton E (2007) The linearizing control of a wastewater treatment process with the removal of two substrates. Ann Univ Craiova, Series: Autom Comput Electron Mech 4(31)(1):35–40
16.
Cakmakci M (2007) Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge. Bioprocess Biosyst Eng 30:349–357CrossRef
17.
Petre E, Selişteanu D, Şendrescu D (2008) Neural networks based adaptive control for a class of time varying nonlinear processes. In: Proceedings of the international conference on control, automation and systems—ICCAS 2008, Seoul, Korea, pp 1355–1360
18.
Selişteanu D, Petre E, Răsvan V (2007) Sliding mode and adaptive sliding mode control of a class of nonlinear bioprocesses. Int J Adapt Contr Signal Process 21(8–9):795–822MATHCrossRef
19.
20.
Fogelman S, Blumenstein M, Zhao H (2006) Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks. J Neural Comput Appl 15(3–4):197–203CrossRef
21.
Flaus JM, Boillereaux L (1997) Moving horizon state estimation for a bioprocess modelled by a neural network. Trans Inst Meas Contr 19(5):263–270CrossRef
22.
Glassey J, Ignova M, Ward AC, Montague GA, Morris AJ (1997) Bioprocess supervision: neural networks and knowledge based systems. J Biotechnol 52(3):201–205CrossRef
23.
Lant A, Willis MJ, Montague GA, Tham MT, Morris AJ (1990) A comparison of adaptive estimation with neural-based techniques for bioprocess application. Proc Am Contr Conf 3:2173–2178
24.
Karim MN, Rivera SL (1992) Use of recurrent neural networks for bioprocess identification in on-line optimization by micro-genetic algorithms. In: Proceeding of American control conference, pp 1931–1932
25.
Chorukova E, Simeonov I (2008) Neural and hybrid modelling of biotechnological process. Stud Inform Contr 17(3):305–314
26.
Kiran AU, Jana AK (2009) Control of continuous fed-batch fermentation process using neural network based model predictive controller. Bioprocess Biosyst Eng. Published online March 04, 2009
27.
Petre E, Selişteanu D, Şendrescu D, Ionete C (2008) Nonlinear and neural networks based adaptive control for a wastewater treatment bioprocess. In: Lovrek I, Howlett RJ, Jain LC (eds) 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems—KES2008, Zagreb, Croatia, In: KES 2008, Part II, LNAI 5178, Springer-Verlag, Berlin, pp 273–280
28.
Rovithakis GA, Christodoulou MA (1995) Direct adaptive regulation of unknown nonlinear dynamical systems via dynamic neural networks. IEEE Trans Syst Man Cyber 25(12):1578–1594CrossRef
29.
Sastry S (1999) Nonlinear systems: analysis, stability, and control. Springer-Verlag, New YorkMATH
30.
Nejjari F, Dahhou B, Benhammou A, Roux G (1999) Non-linear multivariable adaptive control of an activated sludge wastewater treatment process. Int J Adapt Contr Signal Process 13(5):347–365MATHCrossRef
Metadata
Title
Neural networks-based adaptive control for a class of nonlinear bioprocesses
Authors
Emil Petre
Dan Selişteanu
Dorin Şendrescu
Cosmin Ionete
Publication date
01-03-2010
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 2/2010
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-009-0284-9

Other articles of this Issue 2/2010

Neural Computing and Applications 2/2010 Go to the issue

Original Article

A reference-based blind source extraction algorithm

Editorial

Advances in design and application of neural networks

Original Article

Implementation of an LVQ neural network with a variable size: algorithmic specification, architectural exploration and optimized implementation on FPGA devices

Original Article

Parsimonious genetic programming for complex process intelligent modeling: algorithm and applications

KES 2008

A novel Euclidean quality threshold ARTMAP network and its application to pattern classification

KES 2008

Accelerated gradient learning algorithm for neural network weights update

Premium Partner

    Image Credits