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We look at the task of iceberg monitoring using a single mobile sensor, and we suggest a modular framework for this. The focus is on path planning for which we come up with a novel strategy, which includes solving a static optimization problem often to account for changes. We formulate the optimization problem in a MILP framework, and we illustrate how this yields acceptable computational time for...
We propose an optimization-based path-planning framework for an aerial mobile sensor network. The purpose of the path planning is to monitor a set of moving surface objects. The algorithm provides collision-free mobile sensor trajectories that are feasible with respect to user-defined vehicle dynamics. The objective of the resulting optimal control problem is to minimize the uncertainty of the objects,...
This paper presents a moving horizon estimation (MHE) approach for estimating flow and pressure inside the annulus during managed pressure drilling (MPD). MPD is an advanced pressure control method that is used to precisely control the annular pressure throughout the wellbore in oil well drilling. The MPD model, which is a hyperbolic-type partial differential equation (PDE), is a hydraulic model based...
Steam delivery networks are large energy-consuming processes and an important part in many processing plants. Often, there is a potential for improved operation and reduction in energy consumption by application of advanced control algorithms and real time optimization. In this article, such a solution is investigated by implementing a model predictive controller to control the common header pressure...
This manuscript presents an optimization-based approach for path planning of an aerial mobile sensor that monitors a set of moving surface objects. The purpose of the optimization problem is to obtain feasible mobile sensor trajectories with an objective to minimize the uncertainty of the objects, represented as the trace of the state estimation error covariance. The dynamic optimization problem is...
This paper presents a case study of nonlinear model predictive control (NMPC) applied to a benchmark nonlinear boiler model. The motivation for NMPC is that if the controlled plant exhibits nonlinear behavior, a linear controller based on a linearized model may often be unable to achieve satisfactory performance. The case study in this paper, a power plant boiler, exhibits significant nonlinearities...
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