Fault tolerant fuzzy IMC control in a PH process

This paper proposes a new fault tolerant control methodology using Fuzzy Internal Model Control (IMC) for nonlinear systems. The models (direct and inverse plant models) used in the IMC controller are generated by an adaptive neural network called ANFIS, which implements a fuzzy inference system of Takagi-Sugeno type. The inverse model of the IMC controller is reconfigured by exploiting information estimated from a fault diagnosis unit and a qualitative model of the system, in terms of a fuzzy logic system. Simulation examples for a fault tolerant sulfitation control problem are given to demonstrate the effectiveness of the proposed scheme.