Design of delay-dependent state estimation algorithm for nonlinear coupling complex networks with dynamical bias: An adaptive event-triggered scheme - Liu, Xia (Neurocomputing (Amsterdam), 2023)
•A time-varying triggered parameter is introduced to govern the released rate during communication.•A new delay-dependent state estimation method is developed.•The monotonicity analysis of the trace of UBSEEC regarding coupling strength is clarified. This paper is concerned with the delay-dependent variance-constrained state estimation (DDVCSE) problem for nonlinear coupling complex networks (NCCNs) subject to dynamical bias and adaptive event-triggered mechanism (AETM). First of all, the nonlinear coupling is modeled to embody the form of data exchange between different network units, which is linearized by resorting to the Taylor expansion. Then, an augmented technique is adopted to deal with the random bias described by a dynamical equation. Furthermore, an AETM is arranged in the sensor-to-estimator channel to adjust communication rate thereby averting resource waste. A novel augmented delay-dependent state estimator is constructed such that the upper bound of state estimation error covariance (UBSEEC) can be derived in the presence of state delay, nonlinear coupling, dynamical bias and AETM. Moreover, the estimator gain is designed appropriately in the sense of locally minimized variance-constrained index. Besides, the algorithm performance issue is discussed with rigourous mathematical proof, where the monotonicity of the trace of UBSEEC with respect to coupling strength is clarified in detail. Finally, an illustrative example is carried out to demonstrate the validity of the developed DDVCSE strategy.