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Neural Computing and Applications

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01-08-2014 | Original Article

Adaptive neural control and learning of affine nonlinear systems

Authors: Yuxiang Wu, Cong Wang

Published in: Neural Computing and Applications | Issue 2/2014

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Abstract

This paper presents deterministic learning from adaptive neural network control of affine nonlinear systems with completely unknown system dynamics. Thanks to the learning capability of radial basis function, neural network (NN), stable adaptive NN controller is designed for the unknown affine nonlinear systems. The designed adaptive NN controller is rigorously shown that learning of the unknown closed-loop system dynamics can be achieved during the stable control process because partial persistent excitation condition of some internal signals in the closed-loop system is satisfied. Subsequently, neural learning controller using the knowledge obtained from deterministic learning is constructed to achieve closed-loop stability and improve control performance. Numerical simulation is provided to show the effectiveness of the proposed control scheme.

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Forti M, Tesi A (1995) New conditions for global stability of neural networks with application to linear and quadratic programming problems. IEEE Trans Circuits Syst I Fundam Theory Appl 42(7):354–366CrossRefMATHMathSciNet

Chen TP (2001) Global exponential stability of delayed Hopfield neural networks. Neural Netw 14:977–980CrossRef

Shen Y, Wang J (2007) Noise-induced stabilization of the recurrent neural networks with mixed time-varying delays and markovian-switching parameters. IEEE Trans Neural Netw 18(6):1857–1862CrossRef

Hu C, Yu J, Jiang HJ, Teng ZD (2010) Exponential stabilization and synchronization of neural networks with time-varying delays via periodically intermittent control. Nonlinearity 23:2369–2391CrossRefMATHMathSciNet

Shen Y, Wang J (2009) Almost sure exponential stability of recurrent neural networks with markovian switching. IEEE Trans Neural Netw 20(5):840–855CrossRef

Zhang GD, Shen Y, Wang L (2013) Global anti-synchronization of a class of chaotic memristive neural networks with time-varying delays. Neural Netw 46:1–8CrossRef

Krstic M, Kanellakopoulos I, Kokotovic P (1995) Nonlinear and adaptive control design. Wiley, New York

Polycarpou MM, Mears MJ (1998) Stable adaptive tracking of uncertain systems using nonlinearly parameterized on-line approximators. Int J Control 70(3):363–384CrossRefMATHMathSciNet

Ge SS, Hang CC, Lee TH, Zhang T (2001) Stable adaptive neural network control. Kluwer, Norwell

10.

Tan KK, Huang SN, Lee TH (2004) Adaptive backstepping control for a class of nonlinear systems using neural network approximations. Int J Robust Nonlinear Control 14:643–664CrossRefMATHMathSciNet

11.

Hwang KS, Tan SW, Tsai MC (2003) Reinforcement learning to adaptive control of nonlinear systems. IEEE Trans Syst Man Cybern Part B Cybern 33(3):514–521CrossRef

12.

Kar I, Behera L (2009) Direct adaptive neural control for affine nonlinear systems. Appl Soft Comput 9:756–764CrossRef

13.

Wang C, Hill DJ (2006) Learning form neural control. IEEE Trans Neural Netw 17(1):130–146CrossRef

14.

Wang C, Hill DJ (2009) Deterministic learning theory for identification, recognition and control. CRC Press, Boca Raton

15.

Liu TF, Wang C, Hill DJ (2009) Learning from neural control of nonlinear systems in normal form. Syst Control Lett 58:633–638CrossRefMATHMathSciNet

16.

Wang C, Chen TR (2011) Rapid detection of small oscillation faults via deterministic learning. IEEE Trans Neural Netw 22(8):1284–1296CrossRef

17.

Dai SL, Wang C, Luo F (2012) Identification and learning control of ocean surface ship using neural networks. IEEE Trans Ind Inform 8(4):801–810CrossRef

18.

Wu YX, Yang M, Wang C (2012) Learning from output feedback adaptive neural control of robot. Control Decis 27(11):1740–1744MathSciNet

19.

Zeng W, Wang C (2012) Human gait recognition via deterministic learning. Neural Netw 35:92–102CrossRefMathSciNet

20.

Park J, Sandberg IW (1991) Universal approximation using radial-basis-function networks. Neural Comput 3:246–257CrossRef

21.

Powell MJD (1992) The theory of radial basis function approximation in 1990. In: Light WA (ed) Advances in numerical analysis II: wavelets, subdivision and radial basis functions. Clarendon Press, Oxford, pp 105–210

22.

Gorinevsky D (1995) On the persistency of excitation in radial basis function network identification of nonlinear systems. IEEE Trans Neural Netw 6(5):1237–1244CrossRef

23.

Kurdila AJ, Narcowich FJ, Ward JD (1995) Persistency of excitation in identification using radial basis function approximants. SIAMJ Control Optim 33(2):625–642CrossRefMATHMathSciNet

24.

Loría A, Panteley E (2002) Uniform exponential stability of linear time-varying systems: revisited. Syst Control Lett 47:13–24CrossRefMATH

25.

Ge SS, Hang CC, Zhang T (1999) Adirect method for robust adaptive nonlinear control with guaranteed transient performance. Syst Control Lett 37:275–284CrossRefMATHMathSciNet

26.

Slotine JJ, Li W (1991) Applied nonlinear control. Prentice Hall, Englewood CliffsMATH

27.

Khalil HK (2002) Nonlinear systems, 3rd edn. Prentice Hall, Upper Sanddle RiverMATH

28.

Lewis FL, Abdallah CT, Dawson DM (1993) Control of robot manipulators. Macmillan, New York

29.

Huang SJ, Lee JS (2000) A stable self-organizing fuzzy controller for robotic motion control. IEEE Trans Ind Electron 47(2):421–428CrossRef

Title: Adaptive neural control and learning of affine nonlinear systems
Authors: Yuxiang Wu
Cong Wang
Publication date: 01-08-2014
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 2/2014
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1488-6

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