Computer Simulation of an Augmented Automatic Choosing Control Designed by Hamiltonian and Genetic Algorithm with Constrained Input

摘要

In this paper we consider a nonlinear feedback control called augmented automatic choosing control (AACC) using weighted gradient optimization automatic choosing functions for a class of nonlinear systems with constrained input. When designed the control, 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 of a structure-specified type which has some parameters. Parameters of the control are suboptimally selected by minimizing the Hamiltonian with the aid of a 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 splendidness of the AACC. Simulation results show that the new controller can improve performance remarkably well.