Nonlinear optimal tracking with incomplete state information using finite-horizon State Dependent Riccati Equation (SDRE)

In this paper, an online technique for finite-horizon nonlinear stochastic tracking problems is presented. The idea of the proposed technique is to integrate the Kalman filter algorithm and the State Dependent Riccati Equation (SDRE) technique. Unlike the ordinary methods which deal with the linearized system, this technique will estimate the unmeasured states of the nonlinear system directly, and this will make the proposed technique effective for wide range of operating points. Numerical example is given to illustrate the effectiveness of the proposed technique.