Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Bounded Dynamic Stochastic Systems: Modelling and Control
Bounded Dynamic Stochastic Systems: Modelling and Control
Optimal actuator fault detection via MLP neural network for PDFs
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
PID controller design for output PDFs of stochastic systems using linear matrix inequalities
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatica (Journal of IFAC)
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An optimal fault tolerant control (FTC) scheme using output probability density functions (PDFs) is studied for the general stochastic continuous time systems. Being different from the classical FTC problems, the measured information is the stochastic distribution of the system output rather than its value. The control objective is to use the output PDFs to design control schemes that can compensate the fault and attenuate the disturbance. A multi-layer perceptron (MLP) neural network is applied to approximate the output PDFs, with which nonlinear principal component analysis (NLPCA) can be used to reduce the model order. For the established continuous-time weighting system with disturbances and uncertainties which is used to link the input and the weights, an LMI-based feasible FTC method is presented to assure that the fault can be well measured and compensated, where the H"~ performance index for the uncertain error systems is optimized.