Integrated stochastic optimal control and system learning to simultaneously reduce parametric and model structure uncertainty can create new avenues for achieving high-performance operation of uncertain systems using model predictive control. This paper presents a generic framework for stochastic optimal control with integrated (control-oriented) model structure adaptation, and discusses genera...

The description of a physical problem through model necessarily involves the introduction parameters. Hence, one wishes to have solution that is function all these parameters: parametric solution. However, construction such solutions exhibiting localization in space only ensured by costly and time-consuming tests, which can be both numerical or experimental. Numerical methodologies used classic...

A control system design procedure is proposed for a widely applicable class of discrete-time, non-linear systems in which the system nonlinearities are incorporated into a linear model structure in the form of State-Dependent Parameter (SDP) functions. The identification and estimation of both non-parametric and parametric SDP models is discussed briefly. The SDP NMSS model structure is defined...

Mixed-integer polynomial programming (MIPP) problems are one class of mixed-integer nonlinear programming (MINLP) problems where objective function and constraints are restricted to the polynomial functions. Although the MINLP problem is NP-hard, in special cases such as MIPP problems, an efficient algorithm can be extended to solve it. In this research, we propose an algorit...

An optimal stochastic control problem is considered for systems with unbounded controls satisfying an integral constraint. It is shown that there exists an optimal control within the class of generalized controls leading to impulse actions. Applying an approach of time transformation, developed recently for deterministic systems, the original control problem is shown to be equivalent to an opti...

We consider a parallel server system that consists of several customer classes and server pools in parallel. We propose a simple robust control policy to minimize the total linear holding and reneging costs. We show that this policy is asymptotically optimal under the many-server heavy traffic regime for parallel server systems when the service times are only server pool dependent and exponenti...

We study the problem of optimally sampling parametric surfaces by means of reparameterization. A criterion is first formulated for measuring the parameterization quality of a given surface. According to this criterion, the optimal parameterization is identified for the surface by exploring admissible reparameterizations. Then the optimal sampling of the surface is obtained by uniformly sampling...

Consider a seller who seeks to provide service to a collection of interested parties, subject to feasibility constraints on which parties may be simultaneously served. Assuming that a distribution is known on the value of each party for service—arguably a strong assumption— Myerson’s seminal work provides revenue optimizing auctions [12]. We show instead that, for very general feasibility const...