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January  2016, 12(1): 169-186. doi: 10.3934/jimo.2016.12.169

## Robust output stabilization for a class of nonlinear uncertain stochastic systems under multiplicative and additive noises: The attractive ellipsoid method

 1 Departamento de Ingenieria Mecatronica, Universidad Autonoma del Carmen, Capmeche, Mexico 2 Centro de Investigacin y de Estudios Avanzados del I.P.N. (Cinvestav-IPN), Mexico

Received  December 2013 Revised  November 2014 Published  April 2015

This work concerns the robust stabilization of a class of Quasi-Lipschitz" nonlinear uncertain systems governed by stochastic Differential Equations (SDE) subject to both multiplicative and additive stochastic noises modeled by a vector Brownian motion. The state-vector is admitted to be non-completely available, and be estimated by a Luenberger-type filter. The stabilization around the origin is realized by a linear feedback proportional to the current state-estimates. First, the class of feedback matrices and filter matrix-gains, providing the boundedness of the stochastic trajectories with probability one in a vicinity of the origin, is specified. Then a corresponding ellipsoid, containing these trajectories, is found. Its size" (the trace of the ellipsoid matrix) is derived as a function of the applied gain matrices. To make this ellipsoid as small as possible" the corresponding constrained optimization problem is suggested to be solved. These constraints are given by a system of Matrix Inequalities (MI's) which under a specific change of variables may be converted into a conventional system of Bilinear Matrix Inequalities (BMI's). The last may be resolved by the standard MATLAB toolboxes such as penbmiTL, Tomlab toolbox". Finally, a numerical example, containing the arctangent-type nonlinearities, is presented to illustrate the effectiveness of the suggested methodology.
Citation: Hussain Alazki, Alexander Poznyak. Robust output stabilization for a class of nonlinear uncertain stochastic systems under multiplicative and additive noises: The attractive ellipsoid method. Journal of Industrial & Management Optimization, 2016, 12 (1) : 169-186. doi: 10.3934/jimo.2016.12.169
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