A. E. Pearson
Brown University
18 Papers
201 Citations
A. E. Pearson is an academic researcher from Brown University. The author has contributed to research in topics: Nonlinear system & Estimation theory. The author has an hindex of 9, co-authored 18 publications.
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Papers
A multistage reduction technique for feedback stabilizing distributed time-lag systems
Y. A. Fiagbedzi,A. E. Pearson +1 more
TL;DR: It is shown that if the delay system is spectrally stabilizable, then it shares a common feedback stabilizing control law with its delay-free counterpart, which permits the determination of the control law using well-established ordinary system methods.
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Technical Communique: Output feedback stabilizing controller for time-delay systems
Jesus Leyva-Ramos,A. E. Pearson +1 more
TL;DR: This paper considers the problem of output feedback stabilization of unstable linear time-delay systems and concludes that sufficient conditions in the form of two algebraic Riccati equations and an upper bound explicitly characterize a stabilizing controller are needed.
53
Paper: Decoupled delay estimation in the identification of differential delay systems
A. E. Pearson,C. Y. Wuu +1 more
TL;DR: Based on the variable projection functional in nonlinear least-squares theory, the estimation of pure time delay is decoupled from the determination of the remaining system parameters for a class of differential delay models.
42
Output feedback stabilization of delay systems via generalization of the transformation method
Y. A. Fiagbedzi,A. E. Pearson +1 more
TL;DR: In this article, a generalization of the l.c.m.e. was proposed to allow the stabilization of an arbitrary but finite number of unstable modes in a linear autonomous delay system under the minimal assumptions of spectral stabilizability and detectability.
22
Paper: Nonlinear system identification with limited time data
TL;DR: With disturbances modeled by arbitrary solutions to a linear homogeneous differential equation, a least squares-equation error method is developed for parameter identification using data over a limited time interval which has application to certain classes of nonlinear and time varying systems.
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