9,381 research outputs found
Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning
In this paper we present an online wide-area oscillation damping control
(WAC) design for uncertain models of power systems using ideas from
reinforcement learning. We assume that the exact small-signal model of the
power system at the onset of a contingency is not known to the operator and use
the nominal model and online measurements of the generator states and control
inputs to rapidly converge to a state-feedback controller that minimizes a
given quadratic energy cost. However, unlike conventional linear quadratic
regulators (LQR), we intend our controller to be sparse, so its implementation
reduces the communication costs. We, therefore, employ the gradient support
pursuit (GraSP) optimization algorithm to impose sparsity constraints on the
control gain matrix during learning. The sparse controller is thereafter
implemented using distributed communication. Using the IEEE 39-bus power system
model with 1149 unknown parameters, it is demonstrated that the proposed
learning method provides reliable LQR performance while the controller matched
to the nominal model becomes unstable for severely uncertain systems.Comment: Submitted to IEEE ACC 2019. 8 pages, 4 figure
Resource-constrained project scheduling for timely project completion with stochastic activity durations.
We investigate resource-constrained project scheduling with stochastic activity durations. Various objective functions related to timely project completion are examined, as well as the correlation between these objectives. We develop a GRASP-heuristic to produce high-quality solutions, using so-called descriptive sampling. The algorithm outperforms other existing algorithms for expected-makespan minimization. The distribution of the possible makespan realizations for a given scheduling policy is studied, and problem difficulty is explored as a function of problem parameters.GRASP; Project scheduling; Uncertainty;
Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit
Algorithms based on the hard thresholding principle have been well studied
with sounding theoretical guarantees in the compressed sensing and more general
sparsity-constrained optimization. It is widely observed in existing empirical
studies that when a restricted Newton step was used (as the debiasing step),
the hard-thresholding algorithms tend to meet halting conditions in a
significantly low number of iterations and are very efficient. Hence, the thus
obtained Newton hard-thresholding algorithms call for stronger theoretical
guarantees than for their simple hard-thresholding counterparts. This paper
provides a theoretical justification for the use of the restricted Newton step.
We build our theory and algorithm, Newton Hard-Thresholding Pursuit (NHTP), for
the sparsity-constrained optimization. Our main result shows that NHTP is
quadratically convergent under the standard assumption of restricted strong
convexity and smoothness. We also establish its global convergence to a
stationary point under a weaker assumption. In the special case of the
compressive sensing, NHTP effectively reduces to some of the existing
hard-thresholding algorithms with a Newton step. Consequently, our fast
convergence result justifies why those algorithms perform better than without
the Newton step. The efficiency of NHTP was demonstrated on both synthetic and
real data in compressed sensing and sparse logistic regression
Human-like movement of an anthropomorphic robot: problem revisited
Human-like movement is fundamental for natural human-robot interaction and collaboration. We have developed
in a model for generating arm and hand movements an anthropomorphic robot. This model was inspired by the Posture-Based
Motion-Planning Model of human reaching and grasping movements. In this paper we present some changes to the model we
have proposed in [4] and test and compare different nonlinear constrained optimization techniques for solving the large-scale
nonlinear constrained optimization problem that rises from the discretization of our time-continuous model. Furthermore, we
test different time discretization steps.Eliana Costa e Silva was supported by FCT (grant: SFRH/BD/23821/2005)
A Collection of Challenging Optimization Problems in Science, Engineering and Economics
Function optimization and finding simultaneous solutions of a system of
nonlinear equations (SNE) are two closely related and important optimization
problems. However, unlike in the case of function optimization in which one is
required to find the global minimum and sometimes local minima, a database of
challenging SNEs where one is required to find stationary points (extrama and
saddle points) is not readily available. In this article, we initiate building
such a database of important SNE (which also includes related function
optimization problems), arising from Science, Engineering and Economics. After
providing a short review of the most commonly used mathematical and
computational approaches to find solutions of such systems, we provide a
preliminary list of challenging problems by writing the Mathematical
formulation down, briefly explaning the origin and importance of the problem
and giving a short account on the currently known results, for each of the
problems. We anticipate that this database will not only help benchmarking
novel numerical methods for solving SNEs and function optimization problems but
also will help advancing the corresponding research areas.Comment: Accepted as an invited contribution to the special session on
Evolutionary Computation for Nonlinear Equation Systems at the 2015 IEEE
Congress on Evolutionary Computation (at Sendai International Center, Sendai,
Japan, from 25th to 28th May, 2015.
A novel class of scheduling policies for the stochastic resource-constrained project scheduling problem.
We study the resource-constrained project scheduling problem with stochastic activity durations. We introduce a new class of scheduling policies for this problem, which make a number of a-priori sequencing decisions in a pre-processing phase, while the remaining decisions are made dynamically during project execution. The pre-processing decisions entail the addition of precedence constraints to the scheduling instance, hereby resolving some potential resource conflicts. We compare the performance of this new class with existing scheduling policies for the stochastic resource-constrained project scheduling problem, and we observe that the new class is significantly better when the variability in the activity durations is medium to high.Project scheduling; Uncertainty; Stochastic activity durations; Scheduling policies;
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