Second-order observer for nonlinear systems from discrete noiseless measurements

This paper presents a second-order observer which estimates the states of a nonlinear plant based on discrete deterministic measurements. The observer is obtained by applying the time-varying linear observer theory to the augmented linearized model which is obtained by replacing each quadratic term in the original system with new state variables. The gain of the observer is evaluated sequentially through a procedure similar to that in the Kalman filter. The comparison between the extended linear observer and this one is made through the computer simulation for two model systems of nonlinear type wherein one of them is an electric power system model. These simulation results indicate that the use of second-order observer leads to improved estimation performance.