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ABSTRACT:
This paper poses and solves a new problem of stochastic (nonlinear) disturbance attenuation where the task is to make the system solution bounded (in expectation, with appropriate nonlinear weighting) by a monotone function of the supremum of the covariance of the noise. This is a natural stochastic counterpart of the problem of input-to-state stabilization in the sense of Sontag. Our development starts with a set of new global stochastic Lyapunov theorems. For an exemplary class of stochastic strict-feedback systems with vanishing nonlinearities, where the equilibrium is preserved in the presence of noise, we develop an adaptive stabilization scheme (based on tuning functions) that requires no a priori knowledge of a bound on the covariance. Next, we introduce a control Lyapunov function formula for stochastic disturbance attenuation. Finally, we address optimality and solve a differential game problem with the control and the noise covariance as opposing players; for strict-feedback systems.

SUGGESTED CITATION:
MIROSLAV Krstic and Ruth J. Williams, "Stabilization of Stochastic Nonlinear Systems Driven by Noise of Unknown Covariance" (2001). IEEE TRANSACTIONS ON AUTOMATIC CONTROL. 48 (8), pp. 1237-1253. 0018-9286(01)07681-4. Postprint available free at: http://repositories.cdlib.org/postprints/397

REQUIRED PUBLISHER STATEMENT:
Original citation: Hua Deng, Miroslav Krstic´, and Ruth J. Williams, "Stabilization of Stochastic Nonlinear Systems Driven by Noise of Unknown Covariance" (2001). IEEE Transactions on Automatic Control. 48(8), pp 1237-1253.
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