Computationally efficient min-max MPC

2005 IFAC 16th Triennial World Congress, Prague, Czech RepublicMin-Max MPC (MMMPC) controllers (Campo and Morari, 1987) suffer from a great computational burden that is often circumvented by using upper bounds of the worst possible case of a performance index. These upper bounds are usually computed by means of LMI techniques. In this paper a more efficient approach is shown. This paper proposes a computationally efficient MMMPC control strategy in which the worst case cost is approximated by an upper bound which can be easily computed using simple matrix operations. This implies that the algorithm can be coded easily even in non mathematical oriented programming languages such as those found in industrial embedded control hardware. Simulation examples are given in the paper

Computational burden reduction in Min-Max MPC

Min–max model predictive control (MMMPC) is one of the strategies used to control plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the complex numerical optimization problem that has to be solved at every sampling time. This paper shows how to overcome this by transforming the original problem into a reduced min–max problem whose solution is much simpler. In this way, the range of processes to which MMMPC can be applied is considerably broadened. Proofs based on the properties of the cost function and simulation examples are given in the paper

Neural Network Based Min-Max Predictive Control. Application to a Heat Exchanger

IFAC Adaptation and Learning in Control and Signal Processing. Cemobbio-Como. Italy. 2001Min-max model predictive controllers (MMMPC) have been proposed for the control of linear plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the numerical optimization problem that has to be solved at every sampling time. This fact severely limits the class of processes in which this control is suitable. In this paper the use of a Neural Network (NN) to approximate the solution of the min-max problem is proposed. The number of inputs of the NN is determined by the order and time delay of the model together with the control horizon. For large time delays the number of inputs can be prohibitive. A modification to the basic formulation is proposed in order to avoid this later problem. Simulation and experimental results are given using a heat exchanger

Implementation of min–max MPC using hinging hyperplanes. Application to a heat exchanger

Min–max model predictive control (MMMPC) is one of the few control techniques able to cope with modelling errors or uncertainties in an explicit manner. The implementation of MMMPC suffers a large computational burden due to the numerical min–max problem that has to be solved at every sampling time. This fact severely limits the range of processes to which this control structure can be applied. An implementation scheme based on hinging hyperplanes that overcome these problems is presented here. Experimental results obtained when applying the controller to the heat exchanger of a pilot plant are given.Ministerio de Ciencia y Tecnología DPI2001-2380-C02-01Ministerio de Ciencia y Tecnología DPI2002-04375-C03-0

Feedback control ideas for call center staffing

European Control Conference 2009 • Budapest, Hungary, August 23–26, 2009Call centers are nowadays a widespread solution to deal with customer support and as platform for different kind of business. Call center staffing is crucial to provide adequate service levels at acceptable costs. The task is usually accomplished using heuristics with the help of a human experts or with some static offline optimization based on operations research. Simulators based on queue theory are in some cases also used. The aim of the paper is to show that call center staffing can be posed as a feedback control problem with the advantage of getting a higher level of automation, and a wealth of results from control theory that can help to obtain the best possible staffing. In the paper the authors briefly describe the working procedures of call centers and how the staffing is usually made. They propose a feedback controller that it is used with a call center simulator. The results show that good call center staffing can be obtained even with a not very sophisticated controller

A Java based simulation for basic control

7th IFAC Symposium on Advances in Control Education 21/06/2006 MadridIn this paper we present a java based simulator for control education in basiccourses. The application has been developed using the well known tool Easy JavaSimulation.The objective of the application is to help the student to learn the design of classiccontrollers such as P,PI, PID, etc testing the tuning procedures to control the position ofan antenna controlled by a DC motor. Thus the application allows the student to choosethe parameters of the antenna and the DC motor, to choose the controller to be used andits parameters and finally to simulate the closed loop system observing the evolution ofthe signals as well as a 3-D view. Furthermore, in order to show the real behavior of thesystem, dead zone, saturation, disturbances and non-linearities can be added to the model.This application has been used by the authors to teach a basic control course at EscuelaSuperior de Ingenieros (University of Seville) as virtual laboratory.Moreover, since the application is java based, this can be used by the students from theauthors’ web pages and this can also be installed in the student’s laptop (whichever theplatform is) by downloading it from the authors web page (Limon and Salas, 2003Ministerio de Ciencia y Tecnología DPI2004-07444Ministerio de Ciencia y Tecnología DPI2003-0042

Min-Max Predictive Control of a Five-Phase Induction Machine

In this paper, a fuzzy-logic based operator is used instead of a traditional cost function for the predictive stator current control of a five-phase induction machine (IM). The min-max operator is explored for the first time as an alternative to the traditional loss function. With this proposal, the selection of voltage vectors does not need weighting factors that are normally used within the loss function and require a cumbersome procedure to tune. In order to cope with conflicting criteria, the proposal uses a decision function that compares predicted errors in the torque producing subspace and in the x-y subspace. Simulations and experimental results are provided, showing how the proposal compares with the traditional method of fixed tuning for predictive stator current control.Ministerio de Economía y Competitividad DPI 2016-76493-C3-1-R y 2014/425Unión Europea DPI 2016-76493-C3-1-R y 2014/425Universidad de Sevilla DPI 2016-76493-C3-1-R y 2014/42

A Nonlinear Technical Indicator Selection Approach for Stock Markets. Application to the Chinese Stock Market

In this paper we present a combinatorial nonlinear technical indicator approach for the identification of appropriate combinations of stock technical indicators as inputs in non-linear models. This approach is illustrated with the example of Chinese stock indexes and 35 different stock technical indicators using neural networks as the chosen non-linear method. Stock market technical indicators can generate contradictory signals regarding the future performance of the stock analyzed. Furthermore, some non-linear methods, such as neural networks, can have poor generalization power when dealing with problems of high dimensionality due to the issue of local minima. Therefore, non-linear approaches that can identify appropriate combinations of input variables are of clear importance. It will be shown that the proposed approach, when using neural networks as classifiers, generates error rates lower than those using directly neural networks without dimensionality reduction. It will also be shown that merely increasing the number of neurons does not increase the accuracy. The approach proposed in this article is illustrated with an application to the stock market using neural networks but it could be applied to other fields and it can also be used with other non-linear techniques such as for instance support vector machines.Ministerio de Ciencia e Innovación PID2019-106212RB-C41

Model predictive control techniques for hybrid systems

This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de Eduación y Ciencia DPI2007-66718-C04-01Ministerio de Eduación y Ciencia DPI2008-0581

Probabilistic interval predictor based on dissimilarity functions

This work presents a new methodology to obtain probabilistic interval predictions of a dynamical system. The proposed strategy uses stored past system measurements to estimate the future evolution of the system. The method relies on the use of dissimilarity functions to estimate the conditional probability density function of the outputs. A family of empirical probability density functions, parameterized by means of two scalars, is introduced. It is shown that the proposed family encompasses the multivariable normal probability density function as a particular case. We show that the presented approach constitutes a generalization of classical estimation methods. A validation scheme is used to tune the two parameters on which the methodology relies. In order to prove the effectiveness of the presented methodology, some numerical examples and comparisons are provided