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

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

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

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

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

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

Predictive Control of Multi-Phase Motor for Constant Torque Applications

Constant torque motors are needed for rotary screw compressors that are used for cooling and other applications. In such systems, the torque demanded by the load is approximately the same over the whole range of mechanical speeds. In this paper, the use of multi-phase induction machines is investigated for this type of application. The requirement of low stator current distortion is considered. A scheduled approach is used to provide the best possible tuning for each operating point, similar to the concept of gain scheduling control. Simulations and laboratory tests are used to assess the proposal and compare it with finite-state predictive control. The experiments show that a trade-off situation appears between the ripple content in stator currents in the torque-producing and harmonic planes. As a consequence, the controller tuning appears as an important step. The proposed method considers various figures of merit with cost function tuning, resulting in a scheduled scheme that provides improved results. It is shown that the approach leads to a reduction in current ripple, which is advantageous for this particular applicationMinisterio de Ciencia e Innovación - FEDER RTI2018-101897-B-I00Junta de Andalucía -FEDER P20_0054

Online Adaptive Set of Virtual Voltage Vectors for Stator Current Regulation of a Six-Phase Induction Machine Using Finite State Model Predictive Controllers

(This article belongs to the Special Issue Electric Power Applications II) // "This article is an open access article distributed under the terms and conditions of the Creative Commons ttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/"Virtual voltage vectors (VVV) have been used for the control of multi-phase induction machines, where different sub-spaces appear related to the torque production and losses generation. In the literature, several sets of VVV have been used, aiming at reducing harmonic content while maintaining a low computational burden. This paper proposes the use of different sets of VVV to regulate the stator current of multi-phase drives using finite-state model predictive controllers. In the proposal, only one set is active at each control period. This active set is obtained through a preliminary analysis using performance maps. As a result, a method is derived for the online selection using the current operating point. The selection is based on a simple computation from variables usually measured on variable-speed drives. Results are provided for a symmetrical six-phase IM, showing that the proposal improves the closed-loop performance of the multi-phase drive with a low computational cost

Predictive Stator Current Control of a Five-Phase Motor Using a Hybrid Control Set

Finite state model predictive control (FSMPC) of multiphase drives can use an extra number of inverter configurations compared with the three-phase case. This, however, requires more computing power for the optimization phase. The application time of each selected voltage vector (VV) is then increased, which can result in higher harmonic content. Reducing the allowed VVs can speed up the computations, thus ameliorating the current tracking/regulation in different orthogonal subspaces. However, the flexibility offered by the reduced set of VVs is less than that of the full set. Furthermore, a lower sampling time can result in an increase in the switching frequency, especially for some speed-load combinations. This article proposes the use of a hybrid scheme where the set of allowed VVs is not fixed but rather selected on-line according to the actual speed and torque producing stator current which are computed by the outer loop. A five-phase induction machine (IM) is used as a test bed for the proposal, showing improved results with respect to the nonhybrid case.Ministerio de Ciencia e Innovación RTI2018-101897-B-I0

Evolutionary Gaps Stator Current Control of Multi-phase Drives Balancing Harmonic Content

Multiphase machines are increasingly used in research and industry applications due to their inherent advantages. Stator current control is a common strategy for this type of systems. The most important issue it must face is regulation of currents in the torque producing plane and the harmonic plane. For this task, finite control set model predictive control (FCS-MPC) constitutes an interesting alternative to methods using modulation. However, the implementation of FCS-MPC is characterized by a high computational demand, limiting the sampling frequency. This work proposes a predictive algorithm that needs less computation time. As a result, the sampling period can be reduced while producing predictive control. This brings about several benefits resulting from improved current tracking. The proposed method avoids the combinatorial optimization phase of standard FCS-MPC, which is the most time-consuming part. The algorithm is based on physical insights obtained from the application of FCS-MPC to multiphase drives leading to the concept of evolutionary gaps regions. The experimental results for a five-phase motor demonstrate improved performance. Moreover, the method is flexible enough to balance the tradeoff appearing between the torque producing plane and the harmonic plane.Ministerio de Ciencia e Innovación TED2021-129558B-C22 PID2021-125189OBI0