DISCRETE TIME STOCHASTIC ADAPTIVE CONTROL.

Graham C. Goodwin; Peter Jeffrey Ramadge; Peter E. Caines

doi:10.1137/0319052

DISCRETE TIME STOCHASTIC ADAPTIVE CONTROL.

Graham C. Goodwin, Peter Jeffrey Ramadge, Peter E. Caines

Research output: Contribution to journal › Article › peer-review

218 Scopus citations

Abstract

Global convergence of a stochastic adaptive control algorithm for discrete time linear systems is established. It is shown that, with probability one, the algorithm will ensure the system inputs and outputs are sample mean square bounded and the conditional mean square output tracking error achieves its global minimum possible value for linear feedback control. Thus, asymptotically, the adaptive control algorithm achieves the same performance as could be achieved if the system parameters were known.

Original language	English (US)
Pages (from-to)	829-853
Number of pages	25
Journal	SIAM Journal on Control and Optimization
Volume	19
Issue number	6
DOIs	https://doi.org/10.1137/0319052
State	Published - Jan 1 1981

All Science Journal Classification (ASJC) codes

Control and Optimization
Applied Mathematics

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10.1137/0319052

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title = "DISCRETE TIME STOCHASTIC ADAPTIVE CONTROL.",

abstract = "Global convergence of a stochastic adaptive control algorithm for discrete time linear systems is established. It is shown that, with probability one, the algorithm will ensure the system inputs and outputs are sample mean square bounded and the conditional mean square output tracking error achieves its global minimum possible value for linear feedback control. Thus, asymptotically, the adaptive control algorithm achieves the same performance as could be achieved if the system parameters were known.",

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N2 - Global convergence of a stochastic adaptive control algorithm for discrete time linear systems is established. It is shown that, with probability one, the algorithm will ensure the system inputs and outputs are sample mean square bounded and the conditional mean square output tracking error achieves its global minimum possible value for linear feedback control. Thus, asymptotically, the adaptive control algorithm achieves the same performance as could be achieved if the system parameters were known.

AB - Global convergence of a stochastic adaptive control algorithm for discrete time linear systems is established. It is shown that, with probability one, the algorithm will ensure the system inputs and outputs are sample mean square bounded and the conditional mean square output tracking error achieves its global minimum possible value for linear feedback control. Thus, asymptotically, the adaptive control algorithm achieves the same performance as could be achieved if the system parameters were known.

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DISCRETE TIME STOCHASTIC ADAPTIVE CONTROL.

Abstract

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