Topic
Nonlinear programming
About: Nonlinear programming is a research topic. Over the lifetime, 19486 publications have been published within this topic receiving 656602 citations. The topic is also known as: non-linear programming & NLP.
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TL;DR: A maximum likelihood estimation algorithm is developed for estimating the parameters of Hammerstein nonlinear controlled autoregressive autore progressive moving average (CARARMA) systems by using the Newton iteration.
143 citations
TL;DR: In this paper, a technique is presented for the identification of localized reductions in the stiffness of a structure using natural frequency measurements, which minimizes one of three criteria: (1) the changes in the element stiffnesses; (2) the norm of the changes to the global stiffness matrix; or (3) the residuals of the eigenvalue problem.
Abstract: A technique is presented for the identification of localized reductions in the stiffness of a structure using natural frequency measurements. The sensitivities of the eigenvalues to localized changes in the stiffness have been developed as a set of underdetermined equations. These equations have been used as the constraints in an optimization problem, which minimizes one of three criteria: (1) the changes in the element stiffnesses; (2) the norm of the changes to the global stiffness matrix; or (3) the residuals of the eigenvalue problem. An additional constraint, which forces the stiffness to always decrease due to damage, places the optimization problem in the realm of nonlinear programming. The overall formulation has provided a useful method to identify damage with a small number of measured natural frequencies. Ten to 90% localized reduction in stiffness was successfully identified in a 10-story, two-bay steel frame. The method was verified using test data from an aluminum, cantilever beam.
143 citations
TL;DR: In this paper, a new optimal design method for building energy systems is proposed, which provides the most efficient energy system, best combination of equipment capacity and best operational planning for cooling, heating, and power simultaneously with respect to certain criteria such as energy consumption, CO 2 emission, etc.
143 citations
TL;DR: The proposed strategy addresses uncertainty using a two-stage decision process combined with a receding horizon approach that shows the appropriateness of the method to account for uncertainty in the power forecast.
Abstract: This paper presents the mathematical formulation and control architecture of a stochastic-predictive energy management system for isolated microgrids. The proposed strategy addresses uncertainty using a two-stage decision process combined with a receding horizon approach. The first stage decision variables (unit commitment) are determined using a stochastic mixed-integer linear programming formulation, whereas the second stage variables (optimal power flow) are refined using a nonlinear programming formulation. This novel approach was tested on a modified CIGRE test system under different configurations comparing the results with respect to a deterministic approach. The results show the appropriateness of the method to account for uncertainty in the power forecast.
143 citations