Nonlinear optimal stochastic regulator using finite-horizon state dependent riccati equation

Abstract

A number of computational techniques have been offered for estimation of unmeasured states in nonlinear systems. Most of these techniques rely on applying the linear estimation techniques to the linearized systems, which can be effective only in the neighborhood of the operating point. This paper presents a new efficient approximate online technique used for finite-horizon nonlinear stochastic regulator problems. This technique based on change of variables that converts the differential Riccati equation to a linear Lyapunov differential equation. Illustrative examples are given to illustrate the effectiveness of the proposed technique.