Data-Driven Filtering for Nonlinear Systems with Bounded Noises and Quantized Measurements

This paper focuses on the data-driven filtering problem on nonlinear systems with bounded noises and quantized measurements which are subject to logarithmic quantization. Because the accurate system model is unknown, an almost-optimal data-driven filter is designed from an available data set within the nonlinear set membership framework. The set is composed of system inputs and quantized measurements, which can be more easily obtained than the mathematical model. The logarithmic quantization is dependent on the fact that its quantization error serves as a multiplicative noise which widely exists in many applications. For a further improvement of estimation performance, a preliminary approximation is obtained by neural network technique. Two numerical examples are given to illustrate the effectiveness of the proposed algorithm.