國立臺灣師範大學電機工程學系Y.-G. LeuW.-Y. WangI-H. Li2014-10-302014-10-302009-06-010925-2312http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31938In 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.B-spline membership functionFuzzy-neural networkReduced-form genetic algorithmAdaptive fuzzy-neural control