916 research outputs found
Adaptive Control of Unknown Pure Feedback Systems with Pure State Constraints
This paper deals with the tracking control problem for a class of unknown
pure feedback system with pure state constraints on the state variables and
unknown time-varying bounded disturbances. An adaptive controller is presented
for such systems for the very first time. The controller is designed using the
backstepping method. While designing it, Barrier Lyapunov Functions is used so
that the state variables do not contravene its constraints. In order to cope
with the unknown dynamics of the system, an online approximator is designed
using a neural network with a novel adaptive law for its weight update. In the
stability analysis of the system, the time derivative of Lyapunov function
involves known virtual control coefficient with unknown direction and to deal
with such problem Nussbaum gain is used to design the control law. Furthermore,
to make the controller robust and computationally inexpensive, a novel
disturbance observer is designed to estimate the disturbance along with neural
network approximation error and the time derivative of virtual control input.
The effectiveness of the proposed approach is demonstrated through a simulation
study on the third-order nonlinear system
Testing quantum mechanics: a statistical approach
As experiments continue to push the quantum-classical boundary using
increasingly complex dynamical systems, the interpretation of experimental data
becomes more and more challenging: when the observations are noisy, indirect,
and limited, how can we be sure that we are observing quantum behavior? This
tutorial highlights some of the difficulties in such experimental tests of
quantum mechanics, using optomechanics as the central example, and discusses
how the issues can be resolved using techniques from statistics and insights
from quantum information theory.Comment: v1: 2 pages; v2: invited tutorial for Quantum Measurements and
Quantum Metrology, substantial expansion of v1, 19 pages; v3: accepted; v4:
corrected some errors, publishe
Probabilistic data-driven methods for forecasting, identification and control
This dissertation presents contributions mainly in three different fields: system
identification, probabilistic forecasting and stochastic control.
Thanks to the concept of dissimilarity and by defining an appropriate dissimilarity
function, it is shown that a family of predictors can be obtained. First, a
predictor to compute nominal forecastings of a time-series or a dynamical system
is presented. The effectiveness of the predictor is shown by means of a numerical
example, where daily predictions of a stock index are computed. The obtained
results turn out to be better than those obtained with popular machine learning
techniques like Neural Networks.
Similarly, the aforementioned dissimilarity function can be used to compute conditioned
probability distributions. By means of the obtained distributions, interval
predictions can be made by using the concept of quantiles. However, in order to
do that, it is necessary to integrate the distribution for all the possible values of
the output. As this numerical integration process is computationally expensive,
an alternate method bypassing the computation of the probability distribution is
also proposed. Not only is computationally cheaper but it also allows to compute
prediction regions, which are the multivariate version of the interval predictions.
Both methods present better results than other baseline approaches in a set of
examples, including a stock forecasting example and the prediction of the Lorenz
attractor.
Furthermore, new methods to obtain models of nonlinear systems by means of
input-output data are proposed. Two different model approaches are presented:
a local data approach and a kernel-based approach. A kalman filter can be added
to improve the quality of the predictions. It is shown that the forecasting performance
of the proposed models is better than other machine learning methods in
several examples, such as the forecasting of the sunspot number and the R¨ossler
attractor. Also, as these models are suitable for Model Predictive Control (MPC),
new MPC formulations are proposed. Thanks to the distinctive features of the
proposed models, the nonlinear MPC problem can be posed as a simple quadratic
programming problem. Finally, by means of a simulation example and a real
experiment, it is shown that the controller performs adequately.
On the other hand, in the field of stochastic control, several methods to bound
the constraint violation rate of any controller under the presence of bounded or
unbounded disturbances are presented. These can be used, for example, to tune
some hyperparameters of the controller. Some simulation examples are proposed
in order to show the functioning of the algorithms. One of these examples considers
the management of a data center. Here, an energy-efficient MPC-inspired policy is developed in order to reduce the electricity consumption while keeping
the quality of service at acceptable levels
Aerial Vehicles
This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space
Aeronautical engineering: A continuing bibliography with indexes (supplement 267)
This bibliography lists 661 reports, articles, and other documents introduced into the NASA scientific and technical information system in June, 1991. Subject coverage includes design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; theoretical and applied aspects of aerodynamics and general fluid dynamics; electrical engineering; aircraft control; remote sensing; computer sciences; nuclear physics; and social sciences
AFIT School of Engineering Contributions to Air Force Research and Technology. Calendar Year 1971
This report contains abstracts of Master of Science theses and Doctoral Dissertations completed during the 1971 calendar year at the School of Engineering, Air Force Institute of Technology
Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning
The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques
Guidance, flight mechanics and trajectory optimization. Volume 11 - Guidance equations for orbital operations
Mathematical formulation of guidance equations and solutions for orbital space mission
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