An Identifier-Based Control Method in Dynamic Tracking Neuro-Fuzzy Control System

It is difficult to realize dynamic control for some complex nonlinear processes which are operated in different environments and when operation conditions are changed frequently. In this paper we propose an identifier-based control method in dynamic tracking neuro-fuzzy control system. The dynamic tracking neuro-fuzzy control (DTNFC) system is comprised of two neural networks and a system identification network. The system identification network is used to identify the output of the manipulator system, and one of the dynamic neural networks is employed to learn the weighting factor of the fuzzy logic neural network, the other is control the manipulator system. The identifier combines two parts: performance index and selector. A hysteresis switching algorithm is applied to select the best model.