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The purpose of this paper is to focus on the implementation and management of multi‐objective optimizations, with the help of heuristic algorithms such as space mapping methods.

The authors consider the design of electromechanical actuators by the use of mathematical and computer means. Experiments are then virtual, because they correspond to numerical simulations. Dimensioning is then ensured by an optimization procedure of the space mapping type, whose main characteristic consists in using two models of the same size actuator (instead of a single one for classical optimization methods). Moreover, one considers here that multiple outputs are defined: this defines a multi‐objective optimization. This paper proposes several techniques making it possible to include the definition of multiple objectives to be fulfilled as part of an output space mapping optimization process.

The proposed approaches make it possible to stabilize and accelerate the convergence of multi‐objective optimizations performed by space mapping. This is illustrated by the example of the dimensioning of a resonant linear electromagnetic actuator.

The approach presented in the paper is original because it allows finding of a solution to the multi‐objective problem, without building any Pareto front, and most effectively by improving the convergent behavior of the optimization algorithm.

Tempting as it is to be deterministic about world trade, Western economies are facing a number of new challenges. The collapse of hi‐tech stocks has prompted a re‐evaluation of the new economy, yet traditional economic models no longer offer reliable predictions for the future. New information and communications technology has unquestionably jump‐started America’s economy, yet its re‐found prosperity is in many ways illusory. While the economists debate the relevance of classical theory, one inescapable fact confronts the nations of Europe: the inexorable decline of their population – and with it, their intellectual capital.

Whereas many researchers have examined the way in which health institutions have been transformed through funding modalities, and particularly through prospective payment systems (PPS), few have investigated the architecture of these systems, that is, costs and cost variance. Focusing on the study of costs and on the production of hospital rates, this chapter shows that the French PPS, called “rate per activity” made possible what we call a policy of variance. For health policymakers, the aim was to make the different accounting figures between hospitals, and between ways of practising healthcare, visible, in order to reduce these variances. This policy was attended by uncertainty in the processes of quantification, which led to metrological controversies. As a consequence of the issues around the way of calculating costs, some accounts and calculations were redone. In this chapter, we consider the case of metrological controversy over the remuneration of costs for cystic fibrosis patients’ hospital stays, and over the action of a patient organization that criticized the costs calculated officially. It leads to the analysis of the way calculative infrastructures, as cost accounting and rates, are challenged, and how some actors try to stabilize them.

The purpose of this paper is to address the synthesis and experimental application of a generalized predictive control (GPC) technique on an Orthoglide robot.

The control strategy is composed of two control loops. The inner loop aims at linearizing the nonlinear robot dynamics using feedback linearization. The outer loop tracks the desired trajectory based on GPC strategy, which is robustified against measurement noise and neglected dynamics using Youla parameterization.

The experimental results show the benefits of the robustified predictive control strategy on the dynamical performance of the Orthoglide robot in terms of tracking accuracy, disturbance rejection, attenuation of noise acting on the control signal and parameter variation without increasing the computational complexity.

The paper shows the implementation of the robustified predictive control strategy in real time with low computational complexity on the Orthoglide robot.