D
Dennis S. Bernstein
Researcher at University of Michigan
Publications - 876
Citations - 29606
Dennis S. Bernstein is an academic researcher from University of Michigan. The author has contributed to research in topics: Adaptive control & Control theory. The author has an hindex of 70, co-authored 847 publications receiving 26704 citations. Previous affiliations of Dennis S. Bernstein include Northrop Grumman Corporation & Harris Corporation.
Papers
More filters
Journal ArticleDOI
On unscented Kalman filtering with state interval constraints
TL;DR: In this paper, the state estimation problem for nonlinear systems with prior knowledge is addressed in the form of interval constraints on the states, and an approximate solution to this problem is presented.
Journal ArticleDOI
Some open problems in matrix theory arising in linear systems and control
TL;DR: In this article, the authors discuss several open problems in matrix theory that arise from theoretical and practical issues in feedback control theory and the associated area of linear systems theory, including robust stability, matrix exponent and induced norms, stabilizability and pole assignability, and nonstandard matrix equations.
Proceedings ArticleDOI
Steady-state kalman filtering with an H ∞ error bound
TL;DR: An estimator design problem is considered which involves both L 2 (least squares) and H∞ constraint on the state-ESTimation error.
Journal ArticleDOI
Gain-Constrained Kalman Filtering for Linear and Nonlinear Systems
Bruno O. S. Teixeira,J. Chandrasekar,Harish J. Palanthandalam-Madapusi,Leonardo A. B. Tôrres,Luis A. Aguirre,Dennis S. Bernstein +5 more
TL;DR: This paper considers the state-estimation problem with a constraint on the data-injection gain, and the one-step gain-constrained Kalman predictor and the two-step Gain-ConstrainedKalman filter are presented.
Journal ArticleDOI
Robust controller synthesis using the maximum entropy design equations
TL;DR: In this paper, the optimality conditions obtained in [1] for dynamic compensation in the presence of state-, control-, and measurement-dependent noise were applied to a series of increasingly robust control designs for the example considered in [2].