Abstract
This paper first focuses on the problem of adaptive output feedback stabilization for a more general class of stochastic nonlinear time-delay systems with unknown control directions. By using a linear state transformation, the original system is transformed to a new system for which control design becomes feasible. Then a novel adaptive neural network (NN) output feedback control strategy, which only contains one adaptive parameter, is developed for such systems by combining the input-driven filter design, the backstepping technique, the NN’s parameterization, the Nussbaum gain function method and the Lyapunov–Krasovskii approach. The proposed control design guarantees that all signals in the closed-loop systems are 4-moment (or 2-moment) semi-globally uniformly bounded. Finally, two simulation examples are given to demonstrate the effectiveness and the applicability of the proposed control design.
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References
Niu YG, WC HOD, Lam J (2005) Robust integral sliding mode control for uncertain stochastic systems with time-varying delay. Automatica 41(5):873–880
Fu YS, Tian ZH, Shi SJ (2005) Output feedback stabilization for a class of stochastic time-delay nonlinear systems. IEEE Trans Autom Control 50(6):847–851
Cui P, Zhang CH, Zhang XF (2008) Global stabilization of uncertain stochastic nonlinear time-delay systems by output feedback. Int J Robust Nonlinear Control 18(9):946–959
Liu SJ, Ge SS, Zhang JF (2008) Adaptive output-feedback control for a class of uncertain stochastic non-linear systems with time delays. Int J Control 81(8):1210–1220
Yu ZX, Du HB (2010) Neural-network-based bounded adaptive stabilization for uncertain stochastic nonlinear systems with time-delay. Control Theory Appl 27(6):855–860
Yu ZX, Du HB (2011) Adaptive neural control for uncertain stochastic nonlinear strict-feedback systems with time-varying delays: a Razumikhin functional method. Neurocomputing 74(12–13):2072–2082
Wang HQ, Chen B, Lin C (2012) Adaptive neural control for strict-feedback stochastic nonlinear systems with time-delay. Neurocomputing 77(1):267–274
Cui GZ, Jiao TC, Wei YL, Song GF, Chu YM (2014) Adaptive neural control of stochastic nonlinear systems with multiple time-varying delays and input saturation. Neural Comput Appl. doi:10.1007/s00521-014-1548-6
Chen WS, Jiao LC, Li J et al (2010) Adaptive NN backstepping output-feedback control for stochastic nonlinear strict-feedback systems with time-varying delays. IEEE Trans Syst Man Cybern Part B Cybern 40(3):939–950
Zhou Q, Shi P, Xu SY, Li HY (2013) Observer-based adaptive neural network control for nonlinear stochastic systems with time delay. IEEE Trans Neural Netw Learn Syst 24(1):71–80
Ge SS, Wang J (2002) Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems. IEEE Trans Neural Netw 13(6):1409–1419
Jiang ZP, Mareels I, Hill DJ et al (2004) A unifying framework for global regulation via nonlinear output feedback: from ISS to iISS. IEEE Trans Autom Control 49(4):549–562
Liu YG (2007) Output-feedback adaptive control for a class of nonlinear systems with unknown control directions. Acta Automatica Sinica 33(12):1306–1312
Hua CC, Wang QG, Guan XP (2009) Adaptive fuzzy output-feedback controller design for nonlinear time-delay systems with unknown control direction. IEEE Trans Syst Man Cybern Part B Cybern 39(2):363–374
Tong SC, Liu CL, Li YM (2010) Fuzzy-adaptive decentralized output-feedback control for large-scale nonlinear systems with dynamical uncertainties. IEEE Trans Fuzzy Syst 18(5):845–861
Wen YT, Ren XM (2011) Neural networks-based adaptive control for nonlinear time-varying delays systems with unknown control direction. IEEE Trans Neural Netw 22(10):1599–1612
Yue H, Li J (2012) Output-feedback adaptive fuzzy control for a class of non-linear time-varying delay systems with unknown control directions. IET Control Theory Appl 6(9):1266–1280
Wang YC, Zhang HG, Wang YZ (2006) Fuzzy adaptive control of stochastic nonlinear systems with unknown virtual control gain function. ACTA Automatica Sinica 32(2):170–178
Yu ZX, Jin ZH, Du HB (2012) Adaptive neural control for a class of nonaffine stochastic nonlinear systems with time-varying delay: a Razumikhin-Nussbaum method. IET Control Theory Appl 6(1):14–23
Yu ZX, Li SG, Du HB (2013) Razumikhin-Nussbaum-lemma-based adaptive neural control for uncertain stochastic pure-feedback nonlinear systems with time-varying delays. Int J Robust Nonlinear Control 23(11):1214–1239
Yu ZX, Li SG (2014) Neural-network-based output-feedback adaptive dynamic surface control for a class of stochastic nonlinear time-delay systems with unknown control directions. Neurocomputing 129:540–547
Plolycarpou MM (1996) Stable adaptive neural control scheme for nonlinear systems. IEEE Trans Autom Control 41(3):447–451
Deng H, Kristic M (1997) Stochastic nonlinear stabilization-PART I: a backstepping design. Syst Control Lett 32(3):143–150
Park J, Sandberg LW (1991) Universal approximation using radial-basis-function networks. Neural Comput 3(2):246–257
Ge SS, Hang CC, Lee TH, Zhang T (2002) Stable adaptive neural network control. Kluwer, New York
Niculescu SL (2001) Delay effects on stability: a robust control approach. Springer, New York
Zhou Q, Shi P, Lu J, Xu SY (2011) Adaptive output feedback fuzzy tracking control for a class of nonlinear systems. IEEE Trans Fuzzy Syst 19(5):972–982
Tong SC, Li YM (2012) Adaptive fuzzy output feedback tracking backstepping control of strict-feedback nonlinear systems with unknown dead zones. IEEE Trans Fuzzy Syst 20(1):168–180
Tong SC, Li Y, Li YM, Liu YJ (2011) Observer-based adaptive fuzzy backstepping control for a class of stochastic nonlinear strict-feedback systems. IEEE Trans Syst Man Cybern Part B Cybern 41(6):1693–1704
Tong SC, Li YM (2009) Observer-based fuzzy adaptive control for strict feedback nonlinear systems. Fuzzy Sets Syst 160(12):1749–1764
Li J, Chen WS, Li JM, et, al, (2009) Adaptive NN output-feedback stabilization for a class of stochastic nonlinear strict-feedback systems. ISA Trans 48(4):468–475
Chen WS, Jiao LC, Du ZB (2010) Output-feedback adaptive dynamic surface control of stochastic nonlinear systems using neural network. IET Control Theory Appl 4(12):3012–3021
Tong SC, Huo BY, Li YM (2014) Observer-based adaptive decentralized fuzzy fault-tolerant control of nonlinear large-scale systems with actuator failures. IEEE Trans Fuzzy Syst 22(1):1–15
Tong SC, Li CY, Li YM (2009) Fuzzy adaptive observer backstepping control for MIMO nonlinear systems. Fuzzy Sets Syst 160(19):2755–2775
Wang M, Chen B, Shi P (2008) Adaptive neural control for a class of perturbed strict-feedback nonlinear time-delay systems. IEEE Trans Syst Man Cybern Part B Cybern 38(3):721–730
Wang M, Liu X, Shi P (2011) Adaptive neural control of pure-feedback nonlinear time-delay systems via dynamic surface technique. IEEE Trans Syst Man Cybern Part B Cybern 41(6):1681–1692
Wang RL, Li J (2012) Adaptive neural control design for a class of perturbed nonlinear time-varying delay systems. Int J Innov Comput Inform Control 8(5):3727–3740
Yu JP, Ma YM, Chen B, Yu HS, Pan SF (2011) Adaptive neural position tracking control for induction motors via backstepping. Int J Innov Comput Inform Control 7(7):4503–4516
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Appendix
Appendix
The Proof of Lemma 1 is given as follows
Proof
Firstly, we set
It is easy to be obtained
Which is expressed, by (62), in the form
By the use of (4), this is bounded above by
From (63), we have
By recalling that \(W(t,x)=V(t,x)e^{C_1 t}\), for any \(t\in [0,t_f)\), we obtain
Rewrite (67) as
where \(Const = EV(0,x(0)) + \frac{{C_2 }}{{C_1 }}\).
Depending on the signs of \(N(\zeta )\) and \(\dot{\zeta }\), (68) can be further written as
where \(Const1 = Const + \int _{\zeta (0)}^0 {\left| {N(\zeta )} \right| \hbox{d}\zeta }\).
In the following, we first show that \(\zeta (t)\) is bounded on \([0,t_f )\). It needs to consider the following two cases: (a) \(\zeta (t)\) have upper bound, and (b) \(\zeta (t)\) have lower bound.
Cases (a): Suppose that \(\zeta (t)\) has no upper bounds on \([0,t_f )\). According to the properties of Nussbaum-type function, there must exist two monotone increasing sequences \(\{\zeta _n^{(j)} \}\) with \(\zeta _1^{(j)} > \left| {\zeta (0)} \right|\) and \(\mathop {\lim }\nolimits _{n \rightarrow \infty } \zeta _n^{(j)} = \infty\), \(j=1,2\), such that
Since \(\zeta (t)\) has no upper bounds on \([0,t_f )\), thus, there exist monotone increasing sequences \({t_n^{(j)}}, j=1,2\) such that
Dividing (69) by \(\zeta (t_n^{(j)} ) = \zeta _n^{(j)},\;j = 1,2\), we obtain
Thus, if \(N(\zeta ) > 0\), (74) contradicts (71) and when \(N(\zeta ) < 0\), (73) contradicts (70). Therefore, \(\zeta (t)\) has upper bound on \([0,t_f )\).
Case (b): the following procedure will prove that \(\zeta (t)\) have lower bounds on \([0,t_f )\). Firstly, we assume that \(\zeta (t)\) have no lower bounds on \([0,t_f )\). According to the properties of Nussbaum-type function, there exist two monotone increasing sequences \(\{ t_n^{(j)} \}\), \(j=1,2\), such that \(\zeta (t_n^{(j)} ) = - \zeta _n^{(j)},\;j = 1,2\). Obviously, \(\mathop {\lim }\limits _{n \rightarrow \infty } t_n^{(j)} = t_f,\;j = 1,2\). Noting that the function \(N(\cdot )\) is even, (69) can be further written as
Dividing (75) by \(\zeta (t_n^{(j)} ) = -\zeta _n^{(j)},\;j = 1,2\), we obtain
where \(n=1,2,\ldots\). Noting that \(t_n^{(j)} \rightarrow t_f\), \(\zeta _n^{(j)} \rightarrow \infty, j=1,2\) when \(n \rightarrow \infty\). Similarly, a contradiction can be found no matter what the sign of \(N(\zeta )\) is. Therefore, \(\zeta (t)\) has lower bound on \([0,t_f )\).
From the above analysis, we thus conclude the boundedness of \(\zeta (t)\) on \([0,t_f )\), which in turns shows that \(\int _0^t {N(\zeta )\dot{\zeta }\hbox{d}s}\) and \(EV(t,x(t))\) must be bounded on \([0,t_f )\).
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Yu, Z., Li, S. & Du, H. Adaptive neural output feedback control for stochastic nonlinear time-delay systems with unknown control directions. Neural Comput & Applic 25, 1979–1992 (2014). https://doi.org/10.1007/s00521-014-1686-x
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DOI: https://doi.org/10.1007/s00521-014-1686-x