Adaptive neural control with intercepted adaptation for time-delay saturated nonlinear systems

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• 作者：Shigen Gao ; Bin Ning ; Hairong Dong
• 关键词：Adaptive neural control ; Nonlinear system ; State delay ; Actuator saturation
• 刊名：Neural Computing & Applications
• 出版年：2015
• 出版时间：November 2015
• 年：2015
• 卷：26
• 期：8
• 页码：1849-1857
• 全文大小：981 KB
• 参考文献：1.Boulkroune A, Msaad M, Farza M (2011) Adaptive fuzzy controller for multivariable nonlinear state time-varying delay systems subject to input nonlinearities. Fuzzy sets syst 164(1):45-5MATH MathSciNet CrossRef
  2.Chen M, Ge SS (2013) Direct adaptive neural control for a class of uncertain nonaffine nonlinear systems based on disturbance observer. Cybern IEEE Trans on 43(4):1213-225CrossRef
  3.Cui G, Jiao T, Wei Y, song G, Chu Y (2014) Adaptive neural control of stochastic nonlinear systems with multiple time-varying delays and input saturation. Neural Comput Appl 25:779-91CrossRef
  4.Deng W, Li C, Lü J (2007) Stability analysis of linear fractional differential system with multiple time delays. Nonlinear Dyn 48(4):409-16MATH CrossRef
  5.Do K, Jiang ZP, Pan J (2002) Universal controllers for stabilization and tracking of underactuated ships. Syst Control Lett 47(4):299-17MATH MathSciNet CrossRef
  6.Gao S, Dong H, Chen Y, Ning B, Chen G, Yang X (2013) Approximation-based robust adaptive automatic train control: an approach for actuator saturation. IEEE Trans Intell Transp Syst 14(4):1733-742CrossRef
  7.Ge SS, Hong F, Lee TH (2003) Adaptive neural network control of nonlinear systems with unknown time delays. Autom Control, IEEE Trans on 48(11):2004-010MathSciNet CrossRef
  8.Ge SS, Hong F, Lee TH (2004) Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients. Syst Man Cybern Part B Cybern IEEE Trans on 34(1):499-16MathSciNet CrossRef
  9.Hong F, Ge SS, Lee TH (2005) Practical adaptive neural control of nonlinear systems with unknown time delays. Syst Man Cybern Part B Cybern IEEE Trans on 35(4):849-54CrossRef
  10.Hua M, Tan H, Chen J (2013) Delay-dependent h and generalized h 2 filtering for stochastic neural networks with time-varying delay and noise disturbance. Neural Comput Appl 25:613-24CrossRef
  11.Ioannou PA, Sun J (2012) Robust adaptive control. Courier Dover Publications, New York
  12.Jiang ZP, Lefeber E, Nijmeijer H (2001) Saturated stabilization and tracking of a nonholonomic mobile robot. Syst Control Lett 42(5):327-32MATH MathSciNet CrossRef
  13.Karason S, Annaswamy A (1994) Adaptive control in the presence of input constraints. Autom Control IEEE Trans on 39(11):2325-330MATH MathSciNet CrossRef
  14.Kosmatopoulos EB, Polycarpou MM, Christodoulou MA, Ioannou PA (1995) High-order neural network structures for identification of dynamical systems. Neural Netw IEEE Trans on 6(2):422-31CrossRef
  15.Li Z, Duan Z, Ren W, Feng G (2014) Containment control of linear multi-agent systems with multiple leaders of bounded inputs using distributed continuous controllers. Int J Robust Nonlinear Control. doi:10.-002/?rnc.-195
  16.Liao X, Chen G, Sanchez EN (2002) Delay-dependent exponential stability analysis of delayed neural networks: an LMI approach. Neural Netw 15(7):855-66MathSciNet CrossRef
  17.Lin D, Wang X, Yao Y (2012) Fuzzy neural adaptive tracking control of unknown chaotic systems with input saturation. Nonlinear Dyn 67(4):2889-897MATH MathSciNet CrossRef
  18.Nürnberger G (1989) Approximation by spline functions. Springer, BerlinMATH CrossRef
  19.Polycarpou M, Farrell J, Sharma M (2003) On-line approximation control of uncertain nonlinear systems: issues with control input saturation. In: American Control Conference, 2003. Proceedings of the 2003, vol. 1:543-48. IEEE
  20.Polycarpou MM (1996) Stable adaptive neural control scheme for nonlinear systems. Autom Control IEEE Trans on 41(3):447-51MATH MathSciNet CrossRef
  21.Wang D, Huang J (2005) Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. Neural Netw IEEE Trans on 16(1):195-02CrossRef
  22.Wang LX, Mendel JM (1992) Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. Neural Netw IEEE Trans on 3(5):807-14CrossRef
  23.Wang M, Chen B, Liu X, Shi P (2008) Adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear time-delay systems. Fuzzy Sets Syst 159(8):949-67MATH MathSciNet CrossRef
  24.Wen C, Zhou J, Liu Z, Su H (2011) Robust adaptive control of uncertain nonlinear systems in the presence of input saturation and external disturbance. Autom Control IEEE Trans on 56(7):1672-678MathSciNet CrossRef
  25.Xu S, Lam J (2005) Improved delay-dependent stability criteria for time-delay systems. Autom Control IEEE Trans on 50(3):384-87MathSciNet CrossRef
  26.Yip PP, Hedrick JK (1998) Adaptive dynamic surface control: a simplified algorithm for adaptive backstepping control of nonlinear systems. Int J Control 71(5):959-79MATH MathSciNet CrossRef
  27.Yu Z, Li S, Du H (2014) Adaptive neural output feedback control for stochastic nonlinear time-delay systems with unknown control directions. Neural Comput Appl 25(7-):1979-992CrossRef
  28.Zhou J, Wen C (2008) Adaptive backstepping control of unc
• 作者单位：Shigen Gao (1)
  Bin Ning (1)
  Hairong Dong (1)
  
  1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China
  
• 刊物类别：Computer Science
• 刊物主题：Simulation and Modeling
  
• 出版者：Springer London
• ISSN：1433-3058

文摘

In this paper, the adaptive neural control is proposed for a class of single-input-single-output nonlinear systems with state delay and input saturation. An intercepted adaptation approach is designed to attenuate the effect caused by the input saturation based on a constructed auxiliary system, and radial basis function neural networks are used in the online learning of unknown dynamics. Lyapunov–Krasovskii function is introduced to deal with the state delay. The proposed control scheme can guarantee semi-globally uniformly boundedness of the closed-loop system as rigorously proved by Lyapunov stability theorem. The ultimate and transient tracking errors will be confined in compact regions. The diameters of these regions can be adjusted to be arbitrarily small by tuning proper design parameters. Illustrative examples are used to demonstrate the effectiveness of the proposed control method. Keywords Adaptive neural control Nonlinear system State delay Actuator saturation