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Many control schemes require significant tuning effort to achieve desired performance targets, causing a need for general tools that can perform controller tuning in an automated fashion. This paper represents a continuation of the previous work in which a ...
This paper aims to present an optimization method for very-high-speed electrical machines based on multiphysical analytical models. Analytical models allow a fast design but have to be robust and reliable. The set of models presented is very complete and c ...
The design of RC members with nontraditional methods is demanding due to the large number of unknown variables inherent in the design process. The complexity of the RC beam design optimization problem has led to many oversimplified models, so that the curr ...
Part I of this paper developed the exact diffusion algorithm to remove the bias that is characteristic of distributed solutions for deterministic optimization problems. The algorithm was shown to be applicable to the larger set of locally balanced left-sto ...
A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to some affine constraints. The majority of classical SDP solvers are designed fo ...
This work develops an effective distributed algorithm for the solution of stochastic optimization problems that involve partial coupling among both local constraints and local cost functions. While the collection of networked agents is interested in discov ...
Unexpected disruptions occur for many reasons in railway networks and cause delays, cancellations, and, eventually,
passenger inconvenience. This thesis focuses on the railway timetable rescheduling problem from a macroscopic point of view
in case of lar ...
We consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. Using the Wasserstein metric, we construct a ball in the space of (multivariate and non-discrete) probability distribut ...
Multiscale integrative modeling stands at the intersection between experimental and computational techniques to predict the atomistic structures of important macromolecules. In the integrative modeling process, the experimental information is often integra ...
Model predictive control schemes for power electronic applications are characterized by a great variety of problem formulations. In this paper, we consider a three phase voltage source converter with an arbitrary number of voltage levels and derive a model ...
