Mean-square data-based controller for nonlinear polynomial systems with multiplicative noise

  • Authors:

  • Michael Basin;Peng Shi;Pedro Soto

  • Affiliations:

  • Department of Physical and Mathematical Sciences, Autonomous University of Nuevo Leon, San Nicolas de los Garza, Nuevo Leon, Mexico;Department of Computing and Mathematical Sciences, Faculty of Advanced Technology, University of Glamorgan, Pontypridd, United Kingdom and School of Engineering and Science, Victoria University, M ...;Department of Physical and Mathematical Sciences, Autonomous University of Nuevo Leon, San Nicolas de los Garza, Nuevo Leon, Mexico

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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Abstract

This paper presents the mean-square optimal data-based quadratic-Gaussian controller for stochastic nonlinear polynomial systems with a polynomial multiplicative noise, a linear control input, and a quadratic criterion over linear observations. The mean-square optimal closed-form controller equations are obtained using the separation principle, whose applicability to the considered problem is substantiated. As an intermediate result, the paper gives a closed-form solution of the optimal regulator (control) problem for stochastic nonlinear polynomial systems with a polynomial multiplicative noise, a linear control input, and a quadratic criterion. Performance of the obtained mean-square optimal data-based controller is verified in the illustrative example against the conventional LQG controller that is optimal for linearized systems. Simulation graphs demonstrating overall performance and computational accuracy of the designed optimal controller are included.