Zobrazeno 1 - 10
of 1 682
pro vyhledávání: '"Smith, Roy A."'
In this paper, a distributed dual-quaternion multiplicative extended Kalman filter for the estimation of poses and velocities of individual satellites in a fleet of spacecraft is analyzed. The proposed algorithm uses both absolute and relative pose m
Externí odkaz:
http://arxiv.org/abs/2411.19033
Static structured control refers to the task of designing a state-feedback controller such that the control gain satisfies a subspace constraint. Structured control has applications in control of communication-inhibited dynamical systems, such as sys
Externí odkaz:
http://arxiv.org/abs/2411.11542
This paper develops a method to upper-bound extreme-values of time-windowed risks for stochastic processes. Examples of such risks include the maximum average or 90% quantile of the current along a transmission line in any 5-minute window. This work
Externí odkaz:
http://arxiv.org/abs/2404.06961
We study non-parametric frequency-domain system identification from a finite-sample perspective. We assume an open loop scenario where the excitation input is periodic and consider the Empirical Transfer Function Estimate (ETFE), where the goal is to
Externí odkaz:
http://arxiv.org/abs/2404.01100
Autor:
Abdalmoaty, Mohamed, Smith, Roy S.
We revisit the problem of non-parametric closed-loop identification in frequency domain; we give a brief survey of the literature and provide a small noise analysis of the direct, indirect, and joint input-output methods when two independent experime
Externí odkaz:
http://arxiv.org/abs/2403.15771
Data-driven control uses a past signal trajectory to characterise the input-output behaviour of a system. Willems' lemma provides a data-based prediction model allowing a control designer to bypass the step of identifying a state-space or transfer fu
Externí odkaz:
http://arxiv.org/abs/2403.15329
It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators. On the other hand, optimal statistical identification, via likelihood-based methods, i
Externí odkaz:
http://arxiv.org/abs/2403.05899
We propose a computationally tractable method for the identification of stable canonical discrete-time rational transfer function models, using frequency domain data. The problem is formulated as a global non-convex optimization problem whose objecti
Externí odkaz:
http://arxiv.org/abs/2312.15722
Data-driven predictive control methods based on the Willems' fundamental lemma have shown great success in recent years. These approaches use receding horizon predictive control with nonparametric data-driven predictors instead of model-based predict
Externí odkaz:
http://arxiv.org/abs/2312.02758
This paper introduces a dual input-output parameterization (dual IOP) for the identification of linear time-invariant systems from closed-loop data. It draws inspiration from the recent input-output parameterization developed to synthesize a stabiliz
Externí odkaz:
http://arxiv.org/abs/2311.09019