Francesco Scibilia

Stabilization of gas-lift oil wells using topside measurements

Abstract Highly oscillatory flow regimes that can occur in gas-lift oil wells have been successfully treated using conventional linear control. However, these control systems rely on downhole pressure measurements which are unreliable or even unavailable in some cases. In this paper we propose a solution based on a high gain observer for the state of the process. The estimates are used to compute the downhole pressure, that is the controlled variable considered in the feedback control. Moreover, we propose an estimator to extend a nonlinear observer already presented in the literature, and then we compare the performances. The key feature of the solution proposed is its simplicity and that it relies only on measurements easily obtainable from the top of the single well, and thus it is immediately applicable to multiple-well systems where, since there is often one common outflow manifold, it would be hard to see from the outflow measurements which well is operating in an oscillatory regime.

On feasible sets for MPC and their approximations

In this paper we are interested in the computation of feasible sets for linear model predictive control techniques, based on set relations and not on the conventional orthogonal projection. Further, the problem of computing suitable inner approximations of the feasible sets is considered. Such approximations are characterized by simpler polytopic representations, and preserve essential properties as convexity, positive invariance, inclusion of the set of expected initial states.

Verifying stability of approximate explicit MPC

Several authors have proposed algorithms for approximate explicit MPC [1],[2],[3]. These algorithms have in common that they develop a stability criterion for approximate explicit MPC that require the approximate cost function to be within a certain distance from the optimal cost function. In this paper, stability is instead ascertained by considering only the cost function of the approximate MPC. If a region of the state space is found where the cost function is not decreasing, this indicates that an improved approximation (to the optimal control) is required in that region. If the approximate cost function is decreasing everywhere, no further refinement of the approximate MPC is necessary, since stability is guaranteed.

Multi-Rate Moving Horizon Estimation with Erroneous Infrequent Measurements Recovery

Abstract Moving horizon estimation (MHE) represents a desirable approach in processes monitoring and/or control. MHE allows to take into account model uncertainties and unknown disturbances, to consider additional insight about the process in form of inequality constraints, and suggests a straightforward way to include infrequently occurring measurements, as the process history over a user defined horizon is utilized. The problem addressed in this paper is relevant in chemical processes, but may be relevant also in other application areas. In such processes it is common to have measurements at different sampling rates and with time delay. Often an operator is called to collect some of the infrequent measurements, and to insert them into the estimator. Industrial practitioners report that it is not rare for a wrong value to be inserted for such a manual measurement, leading to significant errors in the state and parameter estimates. Recovering from such errors can take hours. We propose a strategy to recover quickly from faulty infrequent measurements. Since nonlinear models are considered, to overcome the non-convex optimization problem resulting from the MHE, in the paper we consider an efficient formulation based on successive linearization.

Forecasting using multivariate empirical mode decomposition — Applied to iceberg drift forecast

The prediction of the movement of a floating object in the ocean, such as an iceberg, is a challenging problem. Large uncertainties in the driving forces and possibly in the geometry of the object itself prevent accurate forecasts. However, if observations of the past trajectory of the object are available the forecast can be improved considerably. This article proposes an adaptive data-driven forecast algorithm using multivariate empirical mode decomposition to handle these kinds of forecast problems. The algorithm identifies the common oscillatory modes and noise in the velocity of the floating object and its driving forces. Thereafter, it decides which mode contributes to the movement and how the future movement of each mode can be predicted best with the available information. The efficacy of the proposed forecast algorithm is shown on a real iceberg drift data set.

The moving horizon estimator used in iceberg drift estimation and Forecast

Iceberg drift forecast is a challenging process. Large uncertainties in iceberg geometry and driving forces prevent accurate forecasts. In this work, a moving horizon estimation approach that uses past drift information of the iceberg to correct the uncertainties is proposed to improve the forecast. Simple criteria are introduced to aid the decision of which uncertain parameters are most beneficial to estimate. In addition, it is discussed why other choices result in a lower prediction performance after the estimation process. Based on these considerations, the moving horizon estimator is implemented on a drift trajectory of an iceberg surveyed during a research expedition Offshore Newfoundland in 2015. It is demonstrated that the iceberg drift forecast improves significantly within a short-time frame. Furthermore, it is shown that the approach will in general also improve 24 hour iceberg drift predictions.

A Study on an Iceberg Drift Trajectory

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. NOMENCLATURE a Major axis of ellipse [m] A Iceberg layer cross sectional area [m2] b Minor axis of ellipse [m] Fa Air drag force [N] Fc Water drag force [N] Fcor Coriolis force [N] Fp Pressure gradient force [N] Fr Wave radiation force [N] h Iceberg layer height [m] k Ratio between minor and major axis [−] m Iceberg mass [kg] ∗Address all correspondence to this author. p Vector of parameters r Iceberg layer radius [m] R Measurement noise covariance [−] u Vector of inputs v Measurement noise V Iceberg velocity [m/s] Vkeel Iceberg keel volume [m3] Vsail Iceberg sail volume [m3] x Vector of differential states y Vector of outputs ρice Iceberg density [kg/m3] ρw Water density [kg/m3] INTRODUCTION Icebergs are a threat to navigation and offshore installations. Good operational iceberg drift forecasts are important for marine operations such as station keeping in areas subjected to drifting icebergs. Mechanistic dynamic models, which model the drift of an iceberg by considering the forces that act on the iceberg, have been developed by [1–3]. An operational iceberg drift model was developed at the Canadian Ice Service [4]. The model uses environmental inputs as winds, waves and currents and detailed description of the iceberg keel geometry to simulate the iceberg trajectory. Currents are usually identified as the most important driving force for the iceberg drift [4–7]. However, current direction and speed are identified as the most uncertain iceberg model param1 Copyright c

AUV Guidance System for Subsurface Ice Intelligence

This paper considers an AUV guidance system for subsurface ice intelligence. A topologically organized neural network model is used to represent the operating environment. The dynamics of each neuron, characterized by a shunting equation, are used to represent the local environmental information. Targeted areas have the highest values. The AUV moves from areas with low dynamics to areas with higher dynamics like in a potential field navigation. The kinematic constraints of the AUV are taken into account by using Dubins theory to generate feasible paths.Copyright

AUV guidance system for dynamic trajectory generation

Abstract This paper considers an AUV guidance system for collision-free transit and complete area coverage. A topologically organized neural network model is used to represent the operating environment. The activity of each neuron, characterized by a shunting equation, is used to represent the local environmental information. Targeted areas have the highest values. The AUV moves from areas with low activity to areas with higher activity like in a potential field navigation. The kinetic constraints of the AUV are taken into account by using Dubins theory to generate feasible paths.

Constrained Control Allocation for Vessels with Azimuth Thrusters

Abstract Control allocation may produce particular thruster configurations which cause poor maneuverability and temporary loss of controllability in certain directions. The paper presents a new approach for dealing with these singular configurations, which allows to formulate the control allocation problem into optimization problems easy to set up. The approach is validated with numerical simulations on experimental model test data.