Neural Network Based Fault Tolerant Control of a Class of Nonlinear Systems with Input Time Delay

Accurate multi-step state predication is very important for the fault tolerant control of nonlinear systems with input delay. Neural network (NN) possesses strong anti-interference ability at multi-step predication, but the predication accuracy is usually not satisfactory. The strong tracking filter (STF) can reduce adaptively estimate bias and has the ability to track changes in nonlinear systems. Thus in this paper the STF and the NN are combined together to provide more accurate multi-step state predication. Based on the state predication an active fault tolerant control law is then proposed against sensor failures of nonlinear time delay systems. Simulation results on a three-tank-system show the effectiveness of the proposed fault tolerant control law.