Event-based state and unknown input estimation for uncertain systems with stochastic nonlinearities
Sijing Zhang, Hailong Tan, Huisheng Shu, Nan Li
Abstract
In this paper, the event-based state and unknown input estimation (SUIE) problem is investigated for a class of stochastic systems subject to parameter uncertainties and stochastic nonlinearities. For the purpose of reducing the energy consumption in data transmission, an event-triggering protocol is employed to regulate whether the current measurement is transmitted by the sensor. Utilising the event-triggered measurement, a recursive estimator is constructed to concurrently estimate the state and the unknown input. The upper bounds of estimation error covariances are given explicitly for both the state and the unknown input estimates. By means of the completing-the-square technique and Lagrange multiplier method, the estimator gain matrices are designed which minimise the obtained upper bounds. Finally, a numerical example is given to show the effectiveness of the proposed SUIE method.