An estimation-forecast set-up for iceberg drift prediction

Cold Regions Science and Technology

Scibilia

Copland

et al. 2018

Self Cite

Short-term iceberg drift prediction is challenging. Large uncertainties in the driving forces -current, wind and waves -usually prevent accurate forecasts. Recently several statistical iceberg forecast models have been proposed by the authors, which use iceberg position measurements to improve the short-term drift forecast. In this article these statistical forecast methods and models are briefly reviewed. An extensive comparison between the statistical models, in addition to a dynamic iceberg forecast model, is performed on several iceberg drift trajectories. Based on this comparison a new statistical forecast scheme is proposed that combines some of the advantages of the other methods.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of statistical iceberg forecast models

Cold Regions Science and Technology

Scibilia

Copland

et al. 2018

Self Cite

“…x 1 (0) = 0, x 2 (0) = 1 (15) where x(•) ∈ R 2 is the (differential) state, z(•) ∈ R is the algebraic variable and u(•) ∈ R is the control. We let T = 10.…”

Section: The Direct Multiple Shooting Methodsmentioning

confidence: 99%

“…In robotics research, applications include control of agricultural robots [125], remote sensing of icebergs with UAVs [15,70], time-optimal control of robots [132], motion templates for robot-human interaction [133], motion planning of robotic systems with contacts [8,99], and multi-objective control of complex robots [86].…”

Section: Optimization and Simulation In Science And Engineeringmentioning

confidence: 99%

CasADi: a software framework for nonlinear optimization and optimal control

Gillis

Horn³

et al. 2018

Math. Prog. Comp.

2,585

976

We present CasADi, an open-source software framework for numerical optimization. CasADi is a general-purpose tool that can be used to model and solve optimization problems with a large degree of flexibility, larger than what is associated with popular algebraic modeling languages such as AMPL, GAMS, JuMP or Pyomo. Of special interest are problems constrained by differential equations, i.e. optimal control problems. CasADi is written in self-contained C++, but is most conveniently used via full-featured interfaces to Python, MATLAB or Octave. Since its inception in late 2009, it has been used successfully for academic teaching as well as in applications from multiple fields, including process control, robotics and aerospace. This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last seven years.

“…This is only possible if some confidence exist that the current input to the dynamic model is approximately correct. If this is not the case, the current input is more strongly corrected than the shape and drag coefficients [24]. Such an iceberg shape estimation can reduce the uncertainty in the shape parameters and reduce forecast errors in the iceberg prediction.…”

Section: Estimation Of the Iceberg Geometrymentioning

confidence: 99%

A Study on an Iceberg Drift Trajectory

Volume 8: Polar and Arctic Sciences and Technology; Petroleum Technology

Scibilia

Imsland

2017

Self Cite

Iceberg drift forecast is a challenging process. Large uncertainties in iceberg geometry and in the driving forces — current, wind and waves — make accurate forecasts difficult. This article illustrates from a data set that even if the uncertainties in current, wind and waves are reduced the forecast using a dynamic iceberg models stays difficult, because of the sensitivity of the model to different parameters and inputs. Nevertheless, if the uncertainty of the current driving force on the iceberg is reduced by measuring the current at the iceberg location, it is possible under specific conditions to estimate the approximate iceberg shape. This iceberg shape geometry can be used directly in the dynamic iceberg model.