Title :
Modeling, identification, and control of a class of nonlinear systems
Author :
Huaguang, Zhang ; Yongbing, Quan
Author_Institution :
Dept. of Autom. Control, Northeastern Univ., Shenyang, China
fDate :
4/1/2001 12:00:00 AM
Abstract :
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 :
backpropagation; control system synthesis; distributed parameter systems; feedforward neural nets; fuzzy control; large-scale systems; modelling; nonlinear control systems; parameter estimation; stability; 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; Control systems; Equations; Function approximation; Fuzzy control; Fuzzy sets; Fuzzy systems; Neural networks; Nonlinear control systems; Nonlinear systems; Stability analysis;
Journal_Title :
Fuzzy Systems, IEEE Transactions on
