An Augmented Automatic Choosing Control Designed by Extremizing a Combination of Hamiltonian and Lyapunov Functions for Nonlinear Systems

Abstract: In this paper, we consider a nonlinear feedback control called augmented automatic choosing control (AACC) using sigmoid type gradient optimization automatic choosing functions for a class of nonlinear systems. When the control is designed, a constant term which arises from linearization of a given nonlinear system is treated as a coefficient of a stable zero dynamics. The controller is a structure-specified type which has some parameters. Parameters of the control are suboptimally selected by extremizing a combination of the Hamiltonian and Lyapunov functions with the aid of the genetic algorithm. This approach is applied to a field excitation control problem of power system, which is Ozeki-Power-Plant of Kyushu Electric Power Company in Japan, to demonstrate the usefulness of the AACC. Simulation results show that the new controller can improve the performance remarkably.