Boundless multiobjective models for cash management

"This is an Accepted Manuscript of an article published by Taylor & Francis in Engineering Economist on 31-05-2018, available online: https://doi.org/10.1080/0013791X.2018.1456596"[EN] Cash management models are usually based on a set of bounds that complicate the selection of the optimal policies due to nonlinearity. We here propose to linearize cash management models to guarantee optimality through linear-quadratic multiobjective compromise programming models. We illustrate our approach through a reformulation of the suboptimal state-of-the-art Gormley-Meade¿s model to achieve optimality. Furthermore, we introduce a much simpler formulation that we call the boundless model that also provides optimal solutions without using bounds. 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Predictive control of systems with fast dynamics using computational reduction based on feedback control information

Predictive control is a method, which is suitable for control of linear discrete dynamical systems. However, control of systems with fast dynamics could be problematic using predictive control. The calculation of a predictivecontrol algorithm can exceed the sampling period. This situation occurs in case with higher prediction horizons and many constraints on variables in the predictive control. In this contribution, an improving of the classical approach is presented. The reduction of the computational time is performed using an analysis of steady states in the control. The presented approach is based on utilization of information from the feedback control. Then this information is applied in the control algorithm. Finally, the classical method is compared to the presented modification using the time analyses. © Springer International Publishing Switzerland 2015

Un algoritmo secuencial, aleatorio y óptimo para problemas de factibilidad robusta

[ES] En este trabajo (del cual se presentó una versión preliminar en Alamo et al. (2007)) se propone un algoritmo aleatorio para determinar la factibilidad robusta de un conjunto de desigualdades lineales matriciales (Linear Matrix Inequalities, LMI). El algoritmo está basado en la solución de una secuencia de problemas de optimización semidefinida sujetos a un bajo número de restricciones. Se aporta además una cota superior del número máximo de iteraciones que requiere el algoritmo para resolver el problema de factibilidad robusta. Finalmente, los resultados se ilustran mediante un ejemplo numérico. [EN] This paper proposes a randomized algorithm for feasibility of uncertain LMIs. The algorithm is based on the solution of a sequence of semidefinite optimization problems involving a reduced number of constraints. A bound of the maximum number of iterations required by the algorithm is given. Finally, the performance and behaviour of the algorithm are illustrated by means of a numerical example. Los autores agradecen la financiacion del Ministerio de Ciencia e Innovación mediante el proyecto DPI2010-21589-C05-01. Álamo, T.; Tempo, R.; Ramírez, D.; Luque, A.; Camacho, E. (2013). Un algoritmo secuencial, aleatorio y óptimo para problemas de factibilidad robusta. Revista Iberoamericana de Automática e Informática industrial. 10(1):50-61. https://doi.org/10.1016/j.riai.2012.11.005 OJS 50 61 10

Feedback methods for inverse simulation of dynamic models for engineering systems applications

Inverse simulation is a form of inverse modelling in which computer simulation methods are used to find the time histories of input variables that, for a given model, match a set of required output responses. Conventional inverse simulation methods for dynamic models are computationally intensive and can present difficulties for high-speed applications. This paper includes a review of established methods of inverse simulation,giving some emphasis to iterative techniques that were first developed for aeronautical applications. It goes on to discuss the application of a different approach which is based on feedback principles. This feedback method is suitable for a wide range of linear and nonlinear dynamic models and involves two distinct stages. The first stage involves design of a feedback loop around the given simulation model and, in the second stage, that closed-loop system is used for inversion of the model. Issues of robustness within closed-loop systems used in inverse simulation are not significant as there are no plant uncertainties or external disturbances. Thus the process is simpler than that required for the development of a control system of equivalent complexity. Engineering applications of this feedback approach to inverse simulation are described through case studies that put particular emphasis on nonlinear and multi-input multi-output models

Direct flux and current vector control for induction motor drives using model predictive control theory

The study presents the direct flux and current vector control of an induction motor (IM) drive, which is a relatively newer and promising control strategy, through the use of model predictive control (MPC) techniques. The results highlight that the fast flux control nature of direct flux control strategy is further enhanced by MPC. Predictive control is applied in two of its variants, namely the finite control set and modulated MPC, and the advantages and limitations of the two are underlined. This work also highlights, through experimental results, the importance of prioritising the flux part of the cost function which is particularly significant in the case of an IM drive. The performance of the MPC-based approach is compared with the proportional-integral controller, which also prioritises the flux control loop, under various operating regions of the drive such as in the flux-weakening regime. Simulations show the performance expected with different control strategies which is then verified through experiments

Robust nonlinear generalised predictive control for a class of uncertain nonlinear systems via an integral sliding mode approach

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Control on 15/02/2016, available online: http://dx.doi.org/10.1080/00207179.2016.1145356.In this paper, a robust nonlinear generalised predictive control (GPC) method is proposed by combining an integral sliding mode approach. The composite controller can guarantee zero steady-state error for a class of uncertain nonlinear systems in the presence of both matched and unmatched disturbances. Indeed, it is well known that the traditional GPC based on Taylor series expansion cannot completely reject unknown disturbance and achieve offset-free tracking performance. To deal with this problem, the existing approaches are enhanced by avoiding the use of the disturbance observer and modifying the gain function of the nonlinear integral sliding surface. This modified strategy appears to be more capable of achieving both the disturbance rejection and the nominal prescribed specifications for matched disturbance. Simulation results demonstrate the effectiveness of the proposed approach

Breakup reaction models for two- and three-cluster projectiles

Breakup reactions are one of the main tools for the study of exotic nuclei, and in particular of their continuum. In order to get valuable information from measurements, a precise reaction model coupled to a fair description of the projectile is needed. We assume that the projectile initially possesses a cluster structure, which is revealed by the dissociation process. This structure is described by a few-body Hamiltonian involving effective forces between the clusters. Within this assumption, we review various reaction models. In semiclassical models, the projectile-target relative motion is described by a classical trajectory and the reaction properties are deduced by solving a time-dependent Schroedinger equation. We then describe the principle and variants of the eikonal approximation: the dynamical eikonal approximation, the standard eikonal approximation, and a corrected version avoiding Coulomb divergence. Finally, we present the continuum-discretized coupled-channel method (CDCC), in which the Schroedinger equation is solved with the projectile continuum approximated by square-integrable states. These models are first illustrated by applications to two-cluster projectiles for studies of nuclei far from stability and of reactions useful in astrophysics. Recent extensions to three-cluster projectiles, like two-neutron halo nuclei, are then presented and discussed. We end this review with some views of the future in breakup-reaction theory.Comment: Will constitute a chapter of "Clusters in Nuclei - Vol.2." to be published as a volume of "Lecture Notes in Physics" (Springer