Online solution of state dependent Riccati equation for nonlinear system stabilization

Abstract

A number of computational methods have been proposed in the literature for synthesizing nonlinear control based on state-dependent Riccati equation (SDRE). Most of these methods are numerically complex or depend on correct initial conditions. This paper presents a new and computationally efficient online method for the design of stabilizing control for a class of nonlinear systems based on state-dependent Riccati equation using a gradient-type neural network. Moreover, the proposed network is proven to be stable. The efficacy of this approach is demonstrated through illustrative examples for the proof of concept.