TY - JOUR
T1 - H ∞ tracking-based sliding mode control for uncertain nonlinear systems via an adaptive fuzzy-neural approach
AU - Wang, Wei Yen
AU - Chan, Mei Lang
AU - Hsu, Chen Chien James
AU - Lee, Tsu Tian
N1 - Funding Information:
Manuscript received April 20, 2001; revised January 26, 2002. This work was supported by the National Science Council, Taiwan, R.O.C., under Grant NSC 89-2218-E-030-004. This paper was recommended by Associate Editor W. Pedrycz.
PY - 2002/8
Y1 - 2002/8
N2 - In this paper, a novel adaptive fuzzy-neural sliding mode controller with H ∞ tracking performance for uncertain nonlinear systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors. Because of the advantages of fuzzy-neural systems, which can uniformly approximate nonlinear continuous functions to arbitrary accuracy, adaptive fuzzy-neural control theory is then employed to derive the update laws for approximating the uncertain nonlinear functions of the dynamical system. Furthermore, the H ∞ tracking design technique and the sliding mode control method are incorporated into the adaptive fuzzy-neural control scheme so that the derived controller is robust with respect to unmodeled dynamics, disturbances and approximate errors. Compared with conventional methods, the proposed approach not only assures closed-loop stability, but also guarantees an H ∞ tracking performance for the overall system based on a much relaxed assumption without prior knowledge on the upper bound of the lumped uncertainties. Simulation results have demonstrated that the effect of the lumped uncertainties on tracking error is efficiently attenuated, and chattering of the control input is significantly reduced by using the proposed approach.
AB - In this paper, a novel adaptive fuzzy-neural sliding mode controller with H ∞ tracking performance for uncertain nonlinear systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors. Because of the advantages of fuzzy-neural systems, which can uniformly approximate nonlinear continuous functions to arbitrary accuracy, adaptive fuzzy-neural control theory is then employed to derive the update laws for approximating the uncertain nonlinear functions of the dynamical system. Furthermore, the H ∞ tracking design technique and the sliding mode control method are incorporated into the adaptive fuzzy-neural control scheme so that the derived controller is robust with respect to unmodeled dynamics, disturbances and approximate errors. Compared with conventional methods, the proposed approach not only assures closed-loop stability, but also guarantees an H ∞ tracking performance for the overall system based on a much relaxed assumption without prior knowledge on the upper bound of the lumped uncertainties. Simulation results have demonstrated that the effect of the lumped uncertainties on tracking error is efficiently attenuated, and chattering of the control input is significantly reduced by using the proposed approach.
KW - Adaptive control
KW - Fuzzy-neural approximator
KW - H tracking performance
KW - Sliding mode control
KW - Uncertain nonlinear systems
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U2 - 10.1109/TSMCB.2002.1018767
DO - 10.1109/TSMCB.2002.1018767
M3 - Article
AN - SCOPUS:0036683732
SN - 1083-4419
VL - 32
SP - 483
EP - 492
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IS - 4
ER -