TY - JOUR
T1 - RGA-based on-line tuning of BMF fuzzy-neural networks for adaptive control of uncertain nonlinear systems
AU - Leu, Yih Guang
AU - Wang, Wei Yen
AU - Li, I. Hsum
N1 - Funding Information:
This work was financially supported by the National Science Council of Taiwan, ROC, under Grant NSC-96-2221-E-003-012-MY3.
PY - 2009/6
Y1 - 2009/6
N2 - In this paper, an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear systems is proposed by using a reduced-form genetic algorithm (RGA). Both the control points of B-spline membership functions (BMFs) and the weighting factors of the adaptive fuzzy-neural controller are tuned on-line via the RGA approach. Each gene represents an adjustable parameter of the BMF fuzzy-neural network with real number components. For the purpose of on-line tuning these parameters and evaluating the stability of the closed-loop system, a special fitness function is included in the RGA approach. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the RIAFC. To illustrate the feasibility and applicability of the proposed method, two examples of nonlinear systems controlled by the RIAFC are demonstrated.
AB - In this paper, an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear systems is proposed by using a reduced-form genetic algorithm (RGA). Both the control points of B-spline membership functions (BMFs) and the weighting factors of the adaptive fuzzy-neural controller are tuned on-line via the RGA approach. Each gene represents an adjustable parameter of the BMF fuzzy-neural network with real number components. For the purpose of on-line tuning these parameters and evaluating the stability of the closed-loop system, a special fitness function is included in the RGA approach. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the RIAFC. To illustrate the feasibility and applicability of the proposed method, two examples of nonlinear systems controlled by the RIAFC are demonstrated.
KW - Adaptive fuzzy-neural control
KW - B-spline membership function
KW - Fuzzy-neural network
KW - Reduced-form genetic algorithm
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U2 - 10.1016/j.neucom.2008.10.005
DO - 10.1016/j.neucom.2008.10.005
M3 - Article
AN - SCOPUS:67349219690
SN - 0925-2312
VL - 72
SP - 2636
EP - 2642
JO - Neurocomputing
JF - Neurocomputing
IS - 10-12
ER -