An iterative learning scheme-based fault estimator design for nonlinear systems with quantised measurements

This paper considers the fault estimation problem for a class of nonlinear system with quantised measurements. An iterative learning observer scheme is constructed in this paper, which combined with a logarithmic quantiser of output signals, and the number of quantisation levels of output signals are finite. Compared with the existing approaches of observer-based fault estimation, the proposed iterative learning observer scheme in this paper improve the fault estimation performance in the current iteration by considers both state error and fault estimation consequence of previous iteration. Meanwhile, the designed observer achieves stability and convergence, since Lyapunov stability theory is employed. Moreover, the extension from nominal system to system with parameter uncertainties subjecting to Bernoulli-distributed white sequences with known conditional probabilities is also addressed. Finally, an illustrative example is provided to verify the theoretical results. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.