Adaptive Neural Model Based Fault Tolerant Control for Multi-variable Process
A new FTC scheme based on adaptive radial basis function (RBF) neural network (NN) model for unknown multi-variable dynamic systems is proposed. The scheme designs an adaptive RBF model to built process model and uses extended Kalman filter (EKF) technique to online learn the fault dynamics. Then, a model inversion controller is designed to produce the fault tolerant control (FTC) actions. The proposed scheme is applied to a three-tank process to evaluate the performance of the scheme. The simulation results show that component fault can be quickly compensated so that the system performances are recovered well.
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