PDFAbstract: 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 [1] Y. N. Yu, K. Vongsuriya and L. N. Wedman, Application of an Optimal Control Theory to a Power System , IEEE Trans. Power Apparatus and Systems, 89-1, 1970, pp.55–62. [2] A. P. Sage and C. C. White III, Optimum Systems Control (2nd edition), Prentice-Hall, Inc., 1977. [3] M. Vidyasagar, Nonlinear Systems Analysis, Prentice-Hall, Inc., 1978. [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
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