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A. E. Pearson
Researcher at Brown University
Publications - 19
Citations - 1634
A. E. Pearson is an academic researcher from Brown University. The author has contributed to research in topics: Linear system & Optimal control. The author has an hindex of 11, co-authored 19 publications receiving 1566 citations.
Papers
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Journal ArticleDOI
Feedback stabilization of linear systems with delayed control
Wook Hyun Kwon,A. E. Pearson +1 more
TL;DR: The control laws of this paper are perhaps the easiest way to stabilize a linear system with delay in the control.
Journal ArticleDOI
A modified quadratic cost problem and feedback stabilization of a linear system
Wook Hyun Kwon,A. E. Pearson +1 more
TL;DR: In this article, a feedback control law for linear systems based on a minimum energy regulator problem with fixed terminal constraints on the state was considered and a modification of this control law was shown to be asymptotically stable.
Journal ArticleDOI
Feedback stabilization of linear autonomous time lag systems
Y. Fiagbedzi,A. E. Pearson +1 more
TL;DR: In this article, a stabilization theory for linear autonomous time lag systems is developed, which employs well-established finite-dimensional control system tools for the stabilization of linear autonomous delay systems, including a set whose elements are matrices each of which is a left zero of the system characteristic quasi-polynomial matrix.
Journal ArticleDOI
On feedback stabilization of time-varying discrete linear systems
Wook Hyun Kwon,A. E. Pearson +1 more
TL;DR: In this article, a feedback control stemming from a receding-horizon concept and a minimum quadratic cost with a fixed terminal constraint is proposed for stabilizing time-varying discrete linear systems.
Journal ArticleDOI
An asymptotic modal observer for linear autonomous time lag systems
Jesus Leyva-Ramos,A. E. Pearson +1 more
TL;DR: A full state vector observer is derived for a class of linear autonomous time lag systems represented by differential equations by using a partial-fraction expansion separating the unstable part from the system and then applying well-known finite dimensional state vector techniques.