779 research outputs found
Modeling and Optimization of the Microwave PCB Interconnects Using Macromodel Techniques
L'abstract è presente nell'allegato / the abstract is in the attachmen
Global Optimization of Gaussian processes
Gaussian processes~(Kriging) are interpolating data-driven models that are
frequently applied in various disciplines. Often, Gaussian processes are
trained on datasets and are subsequently embedded as surrogate models in
optimization problems. These optimization problems are nonconvex and global
optimization is desired. However, previous literature observed computational
burdens limiting deterministic global optimization to Gaussian processes
trained on few data points. We propose a reduced-space formulation for
deterministic global optimization with trained Gaussian processes embedded. For
optimization, the branch-and-bound solver branches only on the degrees of
freedom and McCormick relaxations are propagated through explicit Gaussian
process models. The approach also leads to significantly smaller and
computationally cheaper subproblems for lower and upper bounding. To further
accelerate convergence, we derive envelopes of common covariance functions for
GPs and tight relaxations of acquisition functions used in Bayesian
optimization including expected improvement, probability of improvement, and
lower confidence bound. In total, we reduce computational time by orders of
magnitude compared to state-of-the-art methods, thus overcoming previous
computational burdens. We demonstrate the performance and scaling of the
proposed method and apply it to Bayesian optimization with global optimization
of the acquisition function and chance-constrained programming. The Gaussian
process models, acquisition functions, and training scripts are available
open-source within the "MeLOn - Machine Learning Models for Optimization"
toolbox~(https://git.rwth-aachen.de/avt.svt/public/MeLOn)
Energy Reduction of Robot Stations with Uncertainties
This thesis aims to present a practical approach to reducing the energy use of industrial robot stations. The starting point of this work is different types of robot stations and production systems found in the automotive industry, such as welding stations and human-robot collaborative stations, and the aim is to find and verify methods of reducing the energy use in such systems. Practical challenges with this include limited information about the systems, such as energy models of the robots; limited access to the stations, which complicates experiment and data collection; limitations in the robot control system; and a general reluctance by companies to make drastic changes to already tested and approved production systems. Another practical constraint is to reduce energy use without slowing down production. This is especially challenging when a robot station contains stochastic variations, which is the case in many practical applications. Motivated by these challenges, this thesis presents an offline method of reducing the energy use of a production line of welding stations in an automotive factory. The robot stations contain stochastic uncertainties in the form of variations in the robot execution times, and the energy use is reduced by limiting the robot velocities. The method involves collecting data, modeling the system, formulating and solving a nonlinear and stochastic optimization problem, and applying the results to the real robot station. Tests on real stations show that, with only small modifications, the energy use can be reduced significantly, up to 24 percent.The thesis also contains an online method of controlling a collaborative human-robot bin picking station in a robust and energy-optimal way. The problem is partly a scheduling problem to determine in which orders the operations should be executed, and a timing problem to determine the velocities of the robots. A particular challenge is that some model parameters are unknown and have to be estimated online. A multi-layered control algorithm is presented that continuously updates the operation order and tunes the robot velocities as new orders arrive in the system. Simultaneously, a reinforcement learning algorithm is used to update estimates of the unknown parameters to be used in the optimization algorithms
Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization
This paper investigates the control barrier function (CBF) based
safety-critical control for continuous nonlinear control affine systems using
more efficient online algorithms by the time-varying optimization method. The
idea of the algorithms is that when quadratic programming (QP) or other convex
optimization algorithms needed in the CBF-based method is not computation
affordable, the alternative suboptimal feasible solutions can be obtained more
economically. By using the barrier-based interior point method, the constrained
CBF-QP problems are transformed into unconstrained ones with suboptimal
solutions tracked by two continuous descent-based algorithms. Considering the
lag effect of tracking and exploiting the system information, the prediction
method is added to the algorithms, which achieves exponential convergence to
the time-varying suboptimal solutions. The convergence and robustness of the
designed methods as well as the safety criteria of the algorithms are studied
theoretically. The effectiveness is illustrated by simulations on the
anti-swing and obstacle avoidance tasks
Distributed Optimization with Application to Power Systems and Control
Mathematical optimization techniques are among the most successful tools for controlling technical systems optimally with feasibility guarantees. Yet, they are often centralized—all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization overcome this limitation. Classical approaches, however, are often not applicable due to non-convexities. This work develops one of the first frameworks for distributed non-convex optimization
Non-intrusive hierarchical coupling strategies for multi-scale simulations in gravitational dynamics
Hierarchical code coupling strategies make it possible to combine the results
of individual numerical solvers into a self-consistent symplectic solution. We
explore the possibility of allowing such a coupling strategy to be
non-intrusive. In that case, the underlying numerical implementation is not
affected by the coupling itself, but its functionality is carried over in the
interface. This method is efficient for solving the equations of motion for a
self-gravitating system over a wide range of scales. We adopt a dedicated
integrator for solving each particular part of the problem and combine the
results to a self-consistent solution. In particular, we explore the
possibilities of combining the evolution of one or more microscopic systems
that are embedded in a macroscopic system. The here presented generalizations
of Bridge include higher-order coupling strategies (from the classic 2nd order
up to 10th-order), but we also demonstrate how multiple bridges can be nested
and how additional processes can be introduced at the bridge time-step to
enrich the physics, for example by incorporating dissipative processes. Such
augmentation allows for including additional processes in a classic Newtonian
N-body integrator without alterations to the underlying code. These additional
processes include for example the Yarkovsky effect, dynamical friction or
relativistic dynamics. Some of these processes operate on all particles whereas
others apply only to a subset.
The presented method is non-intrusive in the sense that the underlying
methods remain operational without changes to the code (apart from adding the
get- and set-functions to enable the bridge operator). As a result, the
fundamental integrators continue to operate with their internal time step and
preserve their local optimizations and parallelism.
... abridged ...Comment: Accepted for publication in Communications in Nonlinear Science and
Numerical Simulation (CNSNS) The associated software is part of the AMUSE
framework and can be downloaded from http:www.amusecode.or
Elements of Ion Linear Accelerators, Calm in The Resonances, Other_Tales
The main part of this book, Elements of Linear Accelerators, outlines in Part
1 a framework for non-relativistic linear accelerator focusing and accelerating
channel design, simulation, optimization and analysis where space charge is an
important factor. Part 1 is the most important part of the book; grasping the
framework is essential to fully understand and appreciate the elements within
it, and the myriad application details of the following Parts. The treatment
concentrates on all linacs, large or small, intended for high-intensity, very
low beam loss, factory-type application. The Radio-Frequency-Quadrupole (RFQ)
is especially developed as a representative and the most complicated linac form
(from dc to bunched and accelerated beam), extending to practical design of
long, high energy linacs, including space charge resonances and beam halo
formation, and some challenges for future work. Also a practical method is
presented for designing Alternating-Phase- Focused (APF) linacs with long
sequences and high energy gain. Full open-source software is available. The
following part, Calm in the Resonances and Other Tales, contains eyewitness
accounts of nearly 60 years of participation in accelerator technology.
(September 2023) The LINACS codes are released at no cost and, as always,with
fully open-source coding. (p.2 & Ch 19.10)Comment: 652 pages. Some hundreds of figures - all images, there is no data in
the figures. (September 2023) The LINACS codes are released at no cost and,
as always,with fully open-source coding. (p.2 & Ch 19.10
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