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  • Name

    Fernando Fontes
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    01st April 2019
Publications

2019

Guaranteed Constraint Satisfaction in Continuous-Time Control Problems

Authors
Fontes, FACC; Paiva, LT;

Publication
IEEE Control Systems Letters

Abstract

2019

Optimal power consumption for demand response of thermostatically controlled loads

Authors
Halder, A; Geng, XB; Fontes, FACC; Kumar, PR; Xie, L;

Publication
OPTIMAL CONTROL APPLICATIONS & METHODS

Abstract
We consider the problem of determining the optimal aggregate power consumption of a population of thermostatically controlled loads such as air conditioners. This is motivated by the need to synthesize the demand response for a load serving entity (LSE) catering a population of such customers. We show how the LSE can opportunistically design the aggregate reference consumption to minimize its energy procurement cost, given day-ahead price, load forecast, and ambient temperature forecast, while respecting each individual load's comfort range constraints. The resulting synthesis problem is intractable when posed as a direct optimization problem after Euler discretization of the dynamics, since it results in a mixed-integer linear programming problem with number of variables typically of the order of millions. In contrast, in this paper, we show that the problem is amenable to continuous-time optimal control techniques. Numerical simulations elucidate how the LSE can use the optimal aggregate power consumption trajectory thus computed, for the purpose of demand response.

2019

Optimal Control of Thermostatic Loads for Planning Aggregate Consumption: Characterization of Solution and Explicit Strategies

Authors
Fontes, FACC; Halder, A; Becerril, J; Kumar, PR;

Publication
IEEE Control Systems Letters

Abstract

2019

SAMPLED-DATA MODEL PREDICTIVE CONTROL: ADAPTIVE TIME-MESH REFINEMENT ALGORITHMS AND GUARANTEES OF STABILITY

Authors
Paiva, LT; Fontes, FACC;

Publication
DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B

Abstract
This article addresses the problem of controlling a constrained, continuous-time, nonlinear system through Model Predictive Control (MPC). In particular, we focus on methods to efficiently and accurately solve the underlying optimal control problem (OCP). In the numerical solution of a nonlinear OCP, some form of discretization must be used at some stage. There are, however, benefits in postponing the discretization process and maintain a continuous-time model until a later stage. This is because that way we can exploit additional freedom to select the number and the location of the discretization node points. We propose an adaptive time-mesh refinement (AMR) algorithm that iteratively finds an adequate time-mesh satisfying a pre-defined bound on the local error estimate of the obtained trajectories. The algorithm provides a time-dependent stopping criterion, enabling us to impose higher accuracy in the initial parts of the receding horizon, which are more relevant to MPC. Additionally, we analyze the conditions to guarantee closed-loop stability of the MPC framework using the AMR algorithm. The numerical results show that the proposed AMR strategy can obtain solutions as fast as methods using a coarse equidistant-spaced mesh and, on the other hand, as accurate as methods using a fine equidistant-spaced mesh. Therefore, the OCP can be solved, and the MPC law obtained, faster and/or more accurately than with discrete-time MPC schemes using equidistant-spaced meshes.

2019

A BRKGA for the integrated scheduling problem in FMSs

Authors
Mahdi Homayouni, S; Fontes, DBMM; Fontes, FACC;

Publication
Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '19

Abstract