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In the present work, model predictive control techniques are applied to the water management of a two-reservoir hydropower plant. The availability of forecasts on water requests by the hydroelectric plant was assumed and this motivated the adoption of a model predictive control approach. Ad hoc model mismatch compensation strategies has been developed which takes into account for unknown and unmeasured water flows and model uncertainties. The MPC strategy has been designed based on first principles linear model equations and on assigned control specifications. MPC parameter tuning is suitably adapted to the occurrence of specific state conditions, thus enabling optimal management of conditions of possible reservoir overflow, limiting water wastage while ensuring prompt reaction to possible variation in water demand related to the hydropower production plan. The reliability of the proposed approach has been tested through tailored simulation experiments, taking into account different significant scenarios.

This paper presents two industrial case studies where Advanced Process Control techniques have been applied. The first case study is represented by a hydroelectric plant located in Italy. In this plant, a model predictive controller has been designed and installed, in order to optimally satisfy the production plan. The predictive control approach has been motivated by the availability of forecasts on water requests by the hydroelectric plant and by the goodness of fit performances of the obtained linear process model. The second case study is represented by a pumping station located in an Italian Water Distribution Network. Advanced control logics have been designed in order to optimally and automatically manage the scheduling of the involved pumps. The developed Advanced Process Control systems have been successfully installed on the real plants, obtaining energy efficiency and process control improvement.

This paper presents a project aimed at the optimization of a water distribution network located in Trento (Italy). Several in-depth hydraulic studies have been conducted in order to perform hardware modifications through sectorization procedures. Advanced Process Control methods have been designed in order to optimally and automatically manage the net pressure and the scheduling of the involved pumping stations. Net pressure has been minimized through two-layer Model Predictive Control techniques, while advanced logics have been designed for the pumps scheduling. The developed Advanced Process Control system has been successfully installed on the considered network and the achieved results are here illustrated.

The detection and the isolation of a common fault occurred in an Unmanned Surface Vehicle (USV) is presented. A data-driven, model-free technique based on the Principal Components Analysis (PCA) technique is used to formulate the fault detection problem. This choice is particularly suited for applications on underwater robotic vehicles where, in general, dynamic models are not available or not appropriate for fault detection purposes. Tests performed on telemetry data acquired during field operations show that the presented approach is practical and effective to cope with unexpected environmental situations.

This paper describes a suite of automated tools to produce underwater georeferenced cartographic data that includes archaeologically relevant information. The automated data-processing methodology includes data acquisition, building a 3D model of the site and the final virtual-reality rendering. This paper outlines the methodology developed, with the exception of the final step, and describes results of this approach from the Pianosa 2006 mission, which was part of the European Union Project VENUS (Virtual ExploratioN of Underwater Sites). The data-acquisition system includes a Remotely Operated Vehicle (ROV) to collect optical data for photogrammetric processing. The optical data are geo-referenced through the use of an acoustic positioning system. The optical images and ROV navigation data are saved on-line. The optical data are processed off-line through standard photogrammetric techniques to obtain a 3D Digital Terrain Model (DTM). Archaeological artifacts at the site are identified on the DTM, and 3D virtual models of the objects are inserted into the final representation. Metadata, including the sequential processing steps to obtain the virtual model of the artifacts, are stored in a purpose-built data-base system. Evaluation of archaeologists regarding use of the final cartographic instrument must take into account the accuracy in the 3D model reconstruction.

In the present paper, a geometric-differential approach is employed to analyze the properties of a nonlinear model for the dynamics of Unmanned Underwater Vehicles. In particular, local accessibility, observability and feedback linearizability are investigated. The system is shown to be linearizable and a controller based on linearizing feedback and optimal control techniques is designed. Control performances and robustness properties of the control scheme are investigated by means of suitable simulations and discussed.