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This paper is concerned with tube-based model predictive control (MPC) for both linear and nonlinear, input-affine continuous-time dynamic systems that are affected by time-varying disturbances. We derive a min-max differential inequality describing the support function of positive robust forward invariant tubes, which can be used to construct a variety of tube-based model predictive controllers. These constructions are conservative, but computationally tractable and their complexity scales linearly with the length of the prediction horizon. In contrast to many existing tube-based MPC implementations, the proposed framework does not involve discretizing the control policy and, therefore, the conservatism of the predicted tube depends solely on the accuracy of the set parameterization. The proposed approach is then used to construct a robust MPC scheme based on tubes with ellipsoidal cross-sections. This ellipsoidal MPC scheme is based on solving an optimal control problem under linear matrix inequality constraints. We illustrate these results with the numerical case study of a spring-mass-damper system.

AbstractEnergy efficiency is one of the main drivers for achieving sustainable manufacturing. Advances in machine tool design have reduced the energy consumption of such equipment, but still machine tools remain one of the most energy demanding equipment in a workshop. This study presents a novel approach aimed to improve the energy efficiency of machine tools through the online optimization of cutting conditions. The study is based on an industrial CNC controller with smart algorithms optimizing the cutting parameters to reduce the overall machining time while at the same time minimizing the peak energy consumption.

General awareness on energy consumption is globally growing, becoming a significant performance parameter also in the manufacturing field. The current work outlines a reduced model for the analysis of the energy consumption of a machine tool during face milling operations. The model is characterized by a minimum set of significant parameters describing the product, the process and the machine. The influence of these parameters on energy consumption is represented by a feed forward neural network with 20 inputs, two hidden layers and one output. The input parameters are identified a priori by means of simplifications based on physical and technological considerations, while their relevance is evaluated by a sensitivity analysis using the neural network. The network has been trained and validated on 800 experiments consisting of runs of a continuous-Time simulator that estimates the energy consumption of a machine tool during part program execution. © 2014 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

El control predictivo engloba a una familia de controladores que replanifican continuamente las entradas del sistema durante un cierto horizonte temporal con el fin de optimizar su evolución esperada conforme a un criterio dado. Esta metodología tiene entre sus retos actuales la adaptación al paradigma de los llamados sistemas ciberfísicos, que están compuestos por computadoras, sensores, actuadores y entidades físicas de diversa índole entre las que se incluyen robots e incluso seres humanos que intercambian información con el objetivo de controlar procesos físicos. Este tutorial presenta los conceptos centrales de la integración del control predictivo en este tipo de sistemas mediante el repaso a una serie de ejemplos que explotan la versatilidad de este marco de diseño de controladores para resolver los desafíos que presentan las aplicaciones del siglo XXI.

In this research, a multi-view photogrammetric model was developed and tested in simulation in order to understand its capabilities for close-range photogrammetric applications. It was based on contour line detection and least squares geometrical fitting of a cylindrical geometry from multiple views. To feed and validate this model, synthetic data were created for several cylinders poses and camera network set-up. The simulation chain comprises three main stages: synthetic image creation, image data processing by means of shape-matching and cylinder pose estimation based on developed photogrammetric model. Beforehand, a priori data was theoretically established according to a common reference for both for intrinsic and extrinsic parameters of the cameras. The preliminary results highlight that the model is suitable for close-range photogrammetry and sensible to a priori known data as well as to image data quality. These results were compared against other validated geometrical methods to assure that the model is truthful. Preliminary results show that the accuracy of the model ranges between 1/1000 and 1/20 000 for multiple poses and cylinder dimensions. Moreover the simulation procedure has been enhanced with a Montecarlo approach to estimate more realistic pose uncertainties considering possible imaging error sources.

Traditionally, Life Cycle Assessment (LCA) methodologies include four stages to assess the environmental impact of a product or process. These four stages are scope definition, life cycle inventory (LCI), life cycle impact assessment (LCIA) and interpretation of results. Data collection for the LCI requires substantial effort and may be considered the most critical phase of a LCA, as poor data availability and inconsistency of data may lead to unrealistic results and conclusions. In machine tools, this inventory definition effort is amplified by the inherent complexity of machine tools and their operational behaviour alternatives. This paper describes a LCI method for machine tools providing a new procedure, which improves the transparency and consistency of LCA data. The method is partially based on the developments and strategies described in the draft standard ISO/DIS 14955-1. In this paper, a detailed description of the new method is presented along with the demonstration of its applicability by its implementation in two different manufacturing cases. One of the cases involves the manufacturing of an aeronautic part, while the other case relates to a part manufactured for the household sector. The environmental impact of these two parts, which are produced by a traditional process and by an alternative process is considered, thus it is demonstrated that LCI method as part of a LCA, can be used as a decision support tool to assess the lifecycle impact of the manufacturing of a product using machine tools

Traffic congestion is one of the major issues for urban traffic networks. The connected and autonomous vehicles (CAV) is an emerging technology that has the potential to address this issue by improving safety, efficiency, and capacity of the transportation system. In this paper, the problem of optimal trajectory planning of battery-electric CAVs in the context of cooperative crossing of an unsignalized intersection is addressed. An optimization-based centralized intersection controller is proposed to find the optimal velocity trajectory of each vehicle so as to minimize electric energy consumption and traffic throughput. Solving the underlying optimization problem for a group of CAVs is not straightforward because of the nonlinear and nonconvex dynamics, especially when the powertrain model is explicitly modelled. In order to ensure a rapid solution search and a unique global optimum, the optimal control problem (OCP) is reformulated via convex modeling techniques. Several simulation case studies show the effectiveness of the proposed approach and the trade-off between energy consumption and traffic throughput is illustrated.