A stochastic predictive control approach to project risk management

This work shows a control policy based on MPC and applied to project risk management. MPC has been applied due the properties that presents such as the easy constraint treatment or the extension to multivariable case. The control actions are the mitigation actions to execute in order to reduce the risk exposure. Stochastic variables have been introduced to model the uncertainties of risk impacts. Integer variables are involved in the optimization problem modelling the mitigation actions

Optimal Economic Schedule for a Network of Microgrids With Hybrid Energy Storage System Using Distributed Model Predictive Control

Artículo Open Access en el sitio web el editor. Pago por publicar en abierto.In this paper, an optimal procedure for the economic schedule of a network of interconnected microgrids with hybrid energy storage system is carried out through a control algorithm based on distributed model predictive control (DMPC). The algorithm is specifically designed according to the criterion of improving the cost function of each microgrid acting as a single system through the network mode operation. The algorithm allows maximum economical benefit of the microgrids, minimizing the degradation causes of each storage system, and fulfilling the different system constraints. In order to capture both continuous/discrete dynamics and switching between different operating conditions, the plant is modeled with the framework of mixed logic dynamic. The DMPC problem is solved with the use of mixed integer linear programming using a piecewise formulation, in order to linearize a mixed integer quadratic programming problem.Ministerio de Economía, Industria y Competitivadad DPI2016-78338-RComisión Europea 0076-AGERAR-6-

Hybrid algorithm for scheduling and risk assessment of projects

IFAC CONFERENCE ON ANALYSIS AND DESIGN OF HYBRID SYSTEMS (.2003.SAINT-MALO BRITTANY, FRANCIA)This work presents a technique for optimal scheduling of projects in terms of time and cost, taking into account risk assessment. Tasks are characterized by p-timed Petri nets, where places have assigned an execution time. The proposed technique minimizes the time execution and the cost of the whole project taking into account the Petri nets describing the tasks and the project risk assessment plan. The risk mitigation is carried on through actions where variables that model them may be discrete or continuousMinisterio de Ciencia y Tecnología DPI200 1-2380-C02-0

Power Optimization of Multi-fluid Transportation Systems

6th IFAC/IFIP/IFORS/ IMACS Symposium on Information Control Problems in Manufacturing Technology, 26/09/1989. madrid.This paper presents an algorithm for optimizing the energy operating costs in multi-fluid transportation systems. The algorithm works in two steps. The first one consists of the computation of a function that measures the estimated minimum cost to the goal node. This computation involves the use of Bellman’s optimality principle and some heuristic rules in order to avoid the combinatorial explosion. During the second step the optimum trajectory is obtained with the help of the function mentioned above and using an accurate simulation of the transportation system. The algorithm is applied to a model of an oil pipeline system

Using Genetic Algorithms with Variable-length Individuals for Planning Two-Manipulators Motion

International Conference on Artificial Neural Networks and Genetic Algorithms. 01/01/1997. NorwichA method based on genetic algorithms for obtaining coordinated motion plans of manipulator robots is presented. A decoupled planning approach has been used; that is, the problem has been decomposed into two subproblems: path planning and trajectory planning. This paper focuses on the second problem. The generated plans minimize the total motion time of the robots along their paths. The optimization problem is solved by evolutionary algorithms using a variable-length individuals codification and specific genetic operators

Optimal Operation of Pipeline Transportation Systems

11th Triennial World Congress. Tallinn. Estonia. USSR. 1990This paper presents a simulator of an oil pipeline for scheduling purposes. The simulator includes an algorithm for optimizing the energy operating costs. The optimization algorithm works in two steps. The first one consists of the computation of a function that measures the estimated mininltun cost to the goal node. This computation involves the use of Bellman's optimality principle and of some heuristic rules in order to avoid the combinatorial explosion. During the second step the optinltmum trajectory is obtained with the help of the function mentioned above and using an accurate simulation of the transportation system. The simulation considers those aspects which are relevant t.o the optimization problem and takes into account the following factors: topology and topography of the network. non-linear characteristics of pumps and pipelines, variable demands of consumers, time changing prices of electrical energy and hydraulic equations throughout the system. The simulator is being used by CAMPSA (the major oil distribution company in Spain) Some results obtained with an oil pipeline system in Northern Spain are presented in the paper

Un sistema de decisión multicriterio basado en riesgos: aplicación a la fase de ofertas

XXIV JORNADAS DE AUTOMÁTICA (24) (24.2003.LEÓN, ESPAÑA)Este trabajo presenta un sistema de soporte de decisión para proporcionar ayuda en la fase de ofertas, caracterizada por un alto nivel de incertidumbres. La preparación de la propuesta involucra un coste considerable, sumado a una gran movilización de recursos. En la práctica, usualmente las ofertas son evaluadas en base a diferentes criterios o parámetros de decisión. El algoritmo propuesto evalúa los distintos candidatos a propuesta según las distintas configuraciones de criterios. Se ha introducido una estructura basada en riesgos para minimizar una función objetivo que contiene las posibles acciones mitigadoras que pueden eliminar, parcial o totalmente, los daños causados por riesgos. Las acciones mitigadoras pueden tener una naturaleza discreta o continua

Robust Hybrid Control for Demand Side Management in Islanded Microgrids

This paper focuses on designing a robust control law to manage the demand response of islanded microgrids composed of shifting and adjusting loads. On one side, Hybrid Dynamical System theory is suited here, because the microgrid model is composed of continuous-time dynamics (the energy stored in the battery and the adjustable loads), and discrete-time dynamics (the shifting loads). On the other side, Multi Agent System theory is used to control the adjusting loads in order to guarantee a consensus between them. Hence, non-uniform convergence of the State of Charge of the battery to a given reference is ensured. Robustness with respect to plug and play of any load and parameter variations is also ensured. Experimental results from a laboratory-scale microgrid validate the approach.Agencia Nacional de Investigación Francesa (ANR) ANR-18-CE40-0022-01Agencia Estatal de Investigación PID2019-105890RJI00Agencia Estatal de Investigación AEI/10.13039/501100011033Consejería de Economía y Conocimiento de Andalucía US-126591

Development and experimental evaluation of the control system of a hybrid fuel cell vehicle

This work presents the development and experimental evaluation of a Fuel Cell Hybrid Vehicle, focusing on the control system. The main objective of this paper is to present a real vehicle which has been designed in order to demonstrate the feasibility of the use of hydrogen as an energy source for automotive applications. The paper describes the components that are integrated in the vehicle and presents several experimental results obtained during normal operation. A control system is designed and tested in order to perform all the operations related to the coordinated operation of the fuel cell, the intermediate electrical storage and the power train. Its main task is to compute the power that must be demanded to the fuel cell in real time. This computation is done in order to satisfy the power demand of the electric motor taking into account the state of charge of the batteries and the operating regime of the fuel cell. This is accomplished by manipulating the electronic converter which regulate the current that the fuel cell supplies to the batteries.Ministerio de Ciencia y Tecnología DPI2007-66718-C04-0

A Risk-Based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

This brief examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based model predictive control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this brief can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm