Neuro-fuzzy control of exothermic chemical reactor

The paper deals with the neuro-fuzzy controller design for nonlinear controlled system. The designed neuro-fuzzy controller was Takagi-Sugeno type with two Gaussian membership functions for each considered input. The benchmark system represents Continuous Stirred Tank Reactor (CSTR) with first order exothermic chemical reaction of propylene oxide hydrolyses. Processing of the reactor is influenced by various uncertainties as e.g. measurement noise, varying parameters, disturbances, and conventional control may not lead to satisfying control performance. The control performance was investigated using simulation of control. The control trajectory ensured by the designed neuro-fuzzy controller was compare with the ones generated by the classical PI controller. The simulation results confirmed improved the control performance of the both, the set-point tracking and the disturbance rejection.