Dynamic Event-Triggered State Estimation for Continuous-Time Polynomial Nonlinear Systems With External Disturbances

Abstract

This article is concerned with the problem of state estimation for continuous-time polynomial nonlinear (CTPN) systems with unknown but bounded disturbances. The Taylor polynomial expansion technique is employed to realize the conversion from polynomial nonlinear systems to linear-parameter-varying systems related to the estimate. Moreover, for the purpose of saving the communication resources, an event-triggered sampling scheme is first introduced in the state estimation for CTPN systems, where the event-triggered condition is changed dynamically and the Zeno behavior is excluded. Based on the matrix inequality approach, a sufficient condition is derived in terms of the parameter-dependent linear matrix inequality (LMI) such that the estimation error system is input-to-state stable. Then, the desired estimator parameters can be obtained by solving the parameter-dependent LMI via the sum of squares decomposition technique. Finally, two examples with one concerning the permanent magnet synchronous motor systems are provided to demonstrate the usefulness of proposed method.