An Iterative Learning Scheme-Based Fault Estimator Design for Nonlinear Systems with Quantised Measurements

Abstract

This paper deals with fault estimation problem for a class of nonlinear system with quantised measurements. In this paper, a logarithmic quantiser is introduced and an iterative learning observer scheme is constructed, meanwhile 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 in this paper considers both state error and fault estimation which generated by previous iteration and use them to improve the fault estimation performance in the current iteration. Simultaneously, Lyapunov stability theory is employed to achieve the stability and convergence of the designed observer. Furthermore, 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 are presented to demonstrate the theoretical results.