Robust multi-rate fusion state estimation for networked nonlinear systems via a dynamic event-timing-triggered mechanism

This paper investigates the scenario of remote state estimation using multi-rate asynchronous sampling sensor data for perturbed nonlinear systems. The sensors are categorized into N groups with different sampling rates according to the designed event-triggered mechanism. Subsequently, the measurement data from each sensor group is transmitted to the remote state observer through independent scheduling protocols and communication networks at the sampling instants. To address this problem, this paper introduces a novel multi-rate continuous-discrete observer framework with a dynamic event-timing-triggered mechanism. The multi-rate continuous-discrete observer framework fuses sampled outputs from different rates into continuous output signals, enabling the derivation of continuous state estimation. The dynamic event-timing-triggered mechanism presents a versatile and unified framework for both time-triggered and event-triggered mechanisms. Through the utilization of a hybrid system framework, it is proven that the state estimation error remains input-to-state stable concerning measurement noise and disturbance inputs. The proposed multi-rate continuous-discrete observer is applicable to design a new type of event-triggered high-gain observer. Numerical simulations are conducted to demonstrate the efficacy of the proposed methodology.