Fault tolerant sliding mode intelligent control based on fault hiding for a nonlinear induction furnace system

In this article, the design of a fault tolerant controller for an induction furnace is presented. The fault studied was that of an actuator fault in the furnace. The design is comprised of a fault detection and identification (FDI) unit as well as a reconfiguration block. The induction furnace is modeled using Radial Basis Function (RBF) neural networks so that when an actuator fault happens, the FDI block detects and estimates the magnitude of the fault that occurred. Then based on a fault hiding strategy, the reconfiguration mechanism is activated to accommodate the destructive effects of the fault in a way that from the controller's point of view the actuator fault is hidden. Utilization of the RBF neural networks model in the system also gives training ability to the system and raises its intelligence. In order to make the system robust against uncertainties caused by the fault, a sliding mode scheme is implemented as well. Using Lyapunov's stability approach, the stability of the proposed method is verified. Finally, the simulation result of this method on the induction furnace system validates the effectiveness of the proposed algorithm in the presence of an actuator fault.