Abstract
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.
This work was supported by National Natural Science Foundation (grant Nos. 60574082).
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© 2006 Springer-Verlag Berlin Heidelberg
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Bo, C., Li, J., Wang, Z., Lin, J. (2006). Adaptive Neural Model Based Fault Tolerant Control for Multi-variable Process. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence. ICIC 2006. Lecture Notes in Computer Science(), vol 4114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37275-2_72
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DOI: https://doi.org/10.1007/978-3-540-37275-2_72
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