Modeling, identification, and control of a class of nonlinear systems. - Zhang Huaguang
In this paper, we propose a new fuzzy hyperbolic model for a class of complex systems, which is difficult to model. The fuzzy hyperbolic model is a nonlinear model in nature and can be easily derived from a set of fuzzy rules. It can also be seen as a feedforward neural network model and so we can identify the model parameters by BP-algorithm. We prove that the stable controller can be designed based on linear system theory. Two methods of designing the controller for the fuzzy hyperbolic model are proposed. The results of simulation support the effectiveness of the model and the control scheme
Keywords: BP; Backpropagation; Complex systems; Feedforward neural network model; Fuzzy hyperbolic model; Fuzzy rules; Linear system theory; Nonlinear system control; Nonlinear system identification; Nonlinear system modeling; Parameter identification; Stable controller design