1,511 research outputs found
A Parametric Non-Convex Decomposition Algorithm for Real-Time and Distributed NMPC
A novel decomposition scheme to solve parametric non-convex programs as they
arise in Nonlinear Model Predictive Control (NMPC) is presented. It consists of
a fixed number of alternating proximal gradient steps and a dual update per
time step. Hence, the proposed approach is attractive in a real-time
distributed context. Assuming that the Nonlinear Program (NLP) is
semi-algebraic and that its critical points are strongly regular, contraction
of the sequence of primal-dual iterates is proven, implying stability of the
sub-optimality error, under some mild assumptions. Moreover, it is shown that
the performance of the optimality-tracking scheme can be enhanced via a
continuation technique. The efficacy of the proposed decomposition method is
demonstrated by solving a centralised NMPC problem to control a DC motor and a
distributed NMPC program for collaborative tracking of unicycles, both within a
real-time framework. Furthermore, an analysis of the sub-optimality error as a
function of the sampling period is proposed given a fixed computational power.Comment: 16 pages, 9 figure
A Parametric Multi-Convex Splitting Technique with Application to Real-Time NMPC
A novel splitting scheme to solve parametric multiconvex programs is
presented. It consists of a fixed number of proximal alternating minimisations
and a dual update per time step, which makes it attractive in a real-time
Nonlinear Model Predictive Control (NMPC) framework and for distributed
computing environments. Assuming that the parametric program is semi-algebraic
and that its KKT points are strongly regular, a contraction estimate is derived
and it is proven that the sub-optimality error remains stable if two key
parameters are tuned properly. Efficacy of the method is demonstrated by
solving a bilinear NMPC problem to control a DC motor.Comment: To appear in Proceedings of the 53rd IEEE Conference on Decision and
Control 201
An Alternating Trust Region Algorithm for Distributed Linearly Constrained Nonlinear Programs, Application to the AC Optimal Power Flow
A novel trust region method for solving linearly constrained nonlinear
programs is presented. The proposed technique is amenable to a distributed
implementation, as its salient ingredient is an alternating projected gradient
sweep in place of the Cauchy point computation. It is proven that the algorithm
yields a sequence that globally converges to a critical point. As a result of
some changes to the standard trust region method, namely a proximal
regularisation of the trust region subproblem, it is shown that the local
convergence rate is linear with an arbitrarily small ratio. Thus, convergence
is locally almost superlinear, under standard regularity assumptions. The
proposed method is successfully applied to compute local solutions to
alternating current optimal power flow problems in transmission and
distribution networks. Moreover, the new mechanism for computing a Cauchy point
compares favourably against the standard projected search as for its activity
detection properties
Entre Etat et marché : la difficile maîtrise du système de santé laotien
Cet article se propose d'analyser le système de santé laotien confronté à une crise grave de fréquentation et de ressources, dans le contexte du passage à une économie de marché permettant l'ouverture de pharmacies privées et de cliniques. Le champ de la santé se présente comme un terrain hautement politique en termes de contrôle social. La nature de l'Etat exerce une influence majeure sur le mode de gestion du système. Après avoir observé les contraintes générales et spécifiques, le poids du contexte politique, les pratiques et ajustements des acteurs sociaux dans cette période de transition et d'ambigütés, il faut constater que des choix essentiels de politiques de santé demeurent à opérer. (Résumé d'auteur
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