Robust control for linear system based on gradient flow neural network

A gradient flow algorithm model developed for the on-line robust pole assignment is proposed for solving Sylvester equations. The algorithm shows to be capable of synthesizing linear feedback control systems via on-line computing feedback gain matrix and desired closed-loop poles. Meanwhile, the close-loop system matrix is least sensitive to perturbation or uncertainty, and uniformly asymptotically stable in largely range. Simulation results are shown that the proposed approach is suitable to problem of robust stabilization for nonlinear system and on-line robust pole assignment.