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- W2313335906 abstract "This paper describes a new stochastic control methodology for nonlinear affine systems subject to parametric and functional uncertainties, with random excitations. The primary objective of this method is to control the statistical nature of the state of a nonlinear system to designed (attainable) statistical properties (e.g. moments). This methodology involves a constrained optimization problem for obtaining the undetermined control parameters, where the norm of the error between the desired and actual stationary moments of state/output responses is minimized subject to constraints on moments corresponding to a stationary distribution. To overcome the difficulties in solving the associated Fokker-Planck equation, generally experienced in nonlinear stochastic control and filtering problems, an approximation using the direct quadrature method of moments is proposed. In this innovative approach, the state probability density function is expressed in terms of a finite collection of Dirac delta functions, with the associated weights and locations determined by moment equations. The advantages of the proposed method are: (1) robustness with respect to parametric and functional uncertainties; (2) ability to control any specified stationary moments of the states/output probability density function; and (3) the state process can be Non-Gaussian. A numerical simulation is used to demonstrate the capability of the proposed nonlinear stochastic control method." @default.
- W2313335906 created "2016-06-24" @default.
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- W2313335906 date "2009-06-14" @default.
- W2313335906 modified "2023-10-05" @default.
- W2313335906 title "Nonlinear Stochastic Control Part I: A Moment-Based Approach" @default.
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- W2313335906 doi "https://doi.org/10.2514/6.2009-5627" @default.
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