Experimental validation for nonlinear estimation and tracking using finite-horizon SDRE

In this paper, an effective online method used for finite-horizon, nonlinear, stochastic tracking problems, is presented. The method incorporates the finite-horizon State Dependent Riccati Equation (SDRE) with Kaiman filter to account for the stochastic environment thereby extending the application spectrum to nonlinear systems and overcoming the hurdle with linear tracking systems limited to small variations around the operating point. The method is illustrated by both computer simulation and experimental verification via hardware in the loop Simulation (HILS).