Model Reference Neural-Fuzzy Adaptive Control of the Concentration in a Chemical Reactor (CSTR)

In this work, we consider the application of a special type of neural networks based on radial basis functions (RBF) which belongs to a class of associative memory neural networks. The RBF neural network is used in a novel approach as a controller in a model reference adaptive control (MRAC) architecture. The global control scheme is based on a one step ahead fuzzy model and the objective is to control a Continuous Stared Tank Reactor (CSTR) complex nonlinear system.