Modelling Free Response of a Solar Plant for Predictive Control

IFAC System Identification, Kitakyushu, Fukuoka,Japan,1997This paper deals with the identification of a nonlinear plant by means of a neural network (NN) modelling approximation. The problem of neural identification is tackled using a static NN in a NARX configuration. A method is proposed to obtain the number of past values needed to feed the network. The on-line adaptation of the model and other issues are discussed. In order to show the benefits that can be achieved with the proposed methods, the NN model is used within a Model Predictive Control (MPC) framework. The MPC scheme uses the prediction of the output of the system calculated as the sum of the free response (obtained using the nonlinear NN model) and the forced response (obtained linearizing around the current operating point) to optimize a performance index. The control scheme has been applied and tested in a solar power plant

Input variable selection for forecasting models

2002 IFAC15th Triennial World Congress, Barcelona, SpainThe selection of input variables plays a crucial role when modelling time series. For nonlinear models there are not well developed techniques such as AIC and other criteria that work with linear models. In the case of Short Term Load Forecasting (STLF) generalization is greatly influenced by such selection. In this paper two approaches are compared using real data from a Spanish utility company. The models used are neural networks although the algorithms can be used with other nonlinear models. The experiments show that that input variable selection affects the performance of forecasting models and thus should be treated as a generalization problem

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

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

Chiller Load Forecasting Using Hyper-Gaussian Nets

Energy load forecasting for optimization of chiller operation is a topic that has been receiving increasing attention in recent years. From an engineering perspective, the methodology for designing and deploying a forecasting system for chiller operation should take into account several issues regarding prediction horizon, available data, selection of variables, model selection and adaptation. In this paper these issues are parsed to develop a neural forecaster. The method combines previous ideas such as basis expansions and local models. In particular, hyper-gaussians are proposed to provide spatial support (in input space) to models that can use auto-regressive, exogenous and past errors as variables, constituting thus a particular case of NARMAX modelling. Tests using real data from different world locations are given showing the expected performance of the proposal with respect to the objectives and allowing a comparison with other approaches.Unión Europea RTI2018-101897-B-I00Ministerio de Ciencia e Innovación RTI2018-101897-B-I0

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

Adaptive Cost Function FCSMPC for 6-Phase IMs

In this paper, an adaptive cost function FCSMPC is derived from newly obtained results concerning the distribution of figures of merits used for the assessment of stator current model-based control of multi-phase induction machines. A parameter analysis of FCSMPC is carried out for the case of a six-phase motor. After extensive simulation and Pareto screening, a new structure has been discovered linking several figures of merit. This structure provides an simple explanation for previously reported results concerning the difficulty of cost function tuning for FCSMPC. In addition, the newly discovered link among figures of merit provides valuable insight that can be used for control design. As an application, a new cost function design scheme is derived and tested. This new method avoids the usual and cumbersome procedure of testing many different controller parameters.Unión Europea RTI2018-101897-B-I00Ministerio de Ciencia e Innovación RTI2018-101897-B-I00Agencia Estatal de Investigación RTI2018-101897-B-I0

Robust control of the distributed solar collector field ACUREX using MPC for tracking

17th IFAC World Congress 2008. Seoul (Korea). 06/07/2008This paper presents the application of a robust model predictive control for tracking of piece-wise constant references (RMPCT) to a distributed collector field, ACUREX, at the solar power plant of PSA (Solar Plant of Almería). The main characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong disturbances in the process. The real plant is assumed to be modeled as a linear system with additive bounded uncertainties on the states. Under mild assumptions, the proposed RMPCT can steer the uncertain system in an admissible evolution to any admissible steady state, that is, under any change of the set point. This allows us to reject constant disturbances compensating the effect of then changing the setpoint

Fast finite-state predictive current control of electric drives

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/This work presents a novel optimization method for the implementation of finite-state modelbased predictive current controllers in electrical drives. The proposal avoids the usual exhaustive search to find the control action, reducing the computational burden. The method is based on physical considerations of the power converter voltage vectors and is easy to implement on digital signal processors. The proposal is applied to a five-phase induction machine. Experimental results are compared with those obtained by a standard model-based controller, showing the feasibility of the proposal and the improvements in terms of sampling time reduction and control accuracy