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

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

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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.

KEYWORDS

augmented automatic choosing control, nonlinear control, genetic algorithm, gradient optimization automatic choosing function

REFERENCES

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[4] A.Isidori, Nonlinear Control Systems : An Introduction (2nd edition), Springer-Verlag, 1989.

[5] H.Takata, Automatic Choosing Control Design via GA for Nonlinear Systems, Proc.38th IEEE CDC, 1999, pp.5301–5306.

Cite this paper

Toshinori Nawata. (2017) An Augmented Automatic Choosing Control Designed by Extremizing a Combination of Hamiltonian and Lyapunov Functions for Nonlinear Systems. International Journal of Control Systems and Robotics, 2, 96-102