Author
E. B. Lee
Bio: E. B. Lee is an academic researcher from Honeywell. The author has contributed to research in topics: Control system & Linear-quadratic-Gaussian control. The author has an hindex of 2, co-authored 2 publications receiving 64 citations.
Papers
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TL;DR: In this article, it was shown that the state vector of a linear system can be reconstructed from observations of the system inputs and outputs, and that the observer which reconstructs this state vector is itself a linear systems whose complexity decreases as the number of output quantities available increases.
Abstract: In much of modern control theory designs are based on the assumption that the state vector of the system to be controlled is available for measurement. In many practical situations only a few output quantities are available. Application of theories which assume that the state vector is known is severely limited in these cases. In this paper it is shown that the state vector of a linear system can be reconstructed from observations of the system inputs and outputs. It is shown that the observer, which reconstructs the state vector, is itself a linear system whose complexity decreases as the number of output quantities available increases. The observer may be incorporated in the control of a system which does not have its state vector available for measurement. The observer supplies the state vector, but at the expense of adding poles to the over-all system.
1,581 citations
TL;DR: In this paper, an optimal control problem for a linear regulator with constant external disturbance is formulated, where the optimal control is not an explicit function of the external disturbance, but can be synthesized as a time-invariant linear function.
Abstract: An optimal control problem for a linear regulator with constant external disturbance is formulated. It is shown that, for a suitably selected quadratic-type performance index, the optimal control is not an explicit function of the external disturbance. Moreover, the optimal control can be synthesized as a time-invariant linear function of the state plus the first time integral of a certain other time-invariant linear function of the state.
244 citations
243 citations
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124 citations