Development of event-triggered-based minimum variance recursive estimator for the NLNS using multi-model approach

The minimum variance-based recursive estimator is designed to estimate the unmeasurable states of a non-linear networked system (NLNS). The measurable states are transmitted from sensor to the estimator (S-E) through a communication network. An event-triggered mechanism is proposed to limit data transmission in the S-E network to improve channel utilisation. The control input is transmitted from the controller to actuator (C-A) through another communication network. The networks in the S-E and C-A channels are affected by random packet delay, loss, and uncertain observation. These effects are modelled using five Bernoulli distributed random variables. The NLNS is converted to 'm' local linear stochastic augmented models by piecewise linearisation technique. The 'm' number of local estimators are designed for every local model using projection theorem. Global estimation is obtained by blending local estimators using T-S fuzzy approach. The proposed algorithm is recursive and has the advantage of scalability and flexibility. Finally, estimator performance is demonstrated using a non-linear tunnel diode and compared with the other popular filters. Monte Carlo simulation is performed and accumulated mean-square error values are calculated. From the simulation results, the proposed filter shows satisfactory performance.