12,447 research outputs found
Stabilization of Cascaded Two-Port Networked Systems Against Nonlinear Perturbations
A networked control system (NCS) consisting of cascaded two-port
communication channels between the plant and controller is modeled and
analyzed. Towards this end, the robust stability of a standard closed-loop
system in the presence of conelike perturbations on the system graphs is
investigated. The underlying geometric insights are then exploited to analyze
the two-port NCS. It is shown that the robust stability of the two-port NCS can
be guaranteed when the nonlinear uncertainties in the transmission matrices are
sufficiently small in norm. The stability condition, given in the form of
"arcsin" of the uncertainty bounds, is both necessary and sufficient.Comment: 8 pages, in preparation for journal submissio
Fault Tolerant Deep Reinforcement Learning for Aerospace Applications
With the growing use of Unmanned Aerial Systems, a new need has risen for intelligent algorithms that not only stabilize or control the system, but rather would also include various factors such as optimality, robustness, adaptability, tracking, decision making, and many more. In this thesis, a deep-learning-based control system is designed with fault-tolerant and disturbance rejection capabilities and applied to a high-order nonlinear dynamic system. The approach uses a Reinforcement Learning architecture that combines concepts from optimal control, robust control, and game theory to create an optimally adaptive control for disturbance rejection. Additionally, a cascaded Observer-based Kalman Filter is formulated for estimating adverse inputs to the system. Numerical simulations are presented using different nonlinear model dynamics and scenarios. The Deep Reinforcement Learning and Observer architecture is demonstrated to be a promising control system alternative for fault tolerant applications
Mathematical control of complex systems
Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Fault Tolerant Filtering and Fault Detection for Quantum Systems Driven By Fields in Single Photon States
The purpose of this paper is to solve a fault tolerant filtering and fault
detection problem for a class of open quantum systems driven by a
continuous-mode bosonic input field in single photon states when the systems
are subject to stochastic faults. Optimal estimates of both the system
observables and the fault process are simultaneously calculated and
characterized by a set of coupled recursive quantum stochastic differential
equations.Comment: arXiv admin note: text overlap with arXiv:1504.0678
Predictive Control of Autonomous Kites in Tow Test Experiments
In this paper we present a model-based control approach for autonomous flight
of kites for wind power generation. Predictive models are considered to
compensate for delay in the kite dynamics. We apply Model Predictive Control
(MPC), with the objective of guiding the kite to follow a figure-of-eight
trajectory, in the outer loop of a two level control cascade. The tracking
capabilities of the inner-loop controller depend on the operating conditions
and are assessed via a frequency domain robustness analysis. We take the
limitations of the inner tracking controller into account by encoding them as
optimisation constraints in the outer MPC. The method is validated on a kite
system in tow test experiments.Comment: The paper has been accepted for publication in the IEEE Control
Systems Letters and is subject to IEEE Control Systems Society copyright.
Upon publication, the copy of record will be available at
http://ieeexplore.ieee.or
The SLH framework for modeling quantum input-output networks
Many emerging quantum technologies demand precise engineering and control
over networks consisting of quantum mechanical degrees of freedom connected by
propagating electromagnetic fields, or quantum input-output networks. Here we
review recent progress in theory and experiment related to such quantum
input-output networks, with a focus on the SLH framework, a powerful modeling
framework for networked quantum systems that is naturally endowed with
properties such as modularity and hierarchy. We begin by explaining the
physical approximations required to represent any individual node of a network,
eg. atoms in cavity or a mechanical oscillator, and its coupling to quantum
fields by an operator triple . Then we explain how these nodes can be
composed into a network with arbitrary connectivity, including coherent
feedback channels, using algebraic rules, and how to derive the dynamics of
network components and output fields. The second part of the review discusses
several extensions to the basic SLH framework that expand its modeling
capabilities, and the prospects for modeling integrated implementations of
quantum input-output networks. In addition to summarizing major results and
recent literature, we discuss the potential applications and limitations of the
SLH framework and quantum input-output networks, with the intention of
providing context to a reader unfamiliar with the field.Comment: 60 pages, 14 figures. We are still interested in receiving
correction
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