Bibliografías temáticas / Predictive control ; Process control ; Automatic control

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Literatura académica sobre el tema "Predictive control ; Process control ; Automatic control"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 3 de febrero de 2022

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Artículos de revistas sobre el tema "Predictive control ; Process control ; Automatic control"

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Yu, Jian Zhi y Yong Sheng Chen. "Study on Control Strategy of Train Automatic Speed Control System". Advanced Materials Research 291-294 (julio de 2011): 2763–66. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.2763.

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To improve the efficiency of the automatic control in the process of train operation, reduce state transition caused by the signal interference and guarantee the safe operation of automatic train operation system, a kind of train automatic speed control strategy based on the fuzzy-predictive control algorithm is put forward. According to the dual-redundant control model, the reliability and safety of the train automatic speed control system is analyzed. Using anti-interference design, a series of model simulations have been accomplished. The results show that the system has good reliability.
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Pacheco Quiñones, Daniel, Maria Paterna y Carlo De Benedictis. "Automatic Electromechanical Perturbator for Postural Control Analysis Based on Model Predictive Control". Applied Sciences 11, n.º 9 (29 de abril de 2021): 4090. http://dx.doi.org/10.3390/app11094090.

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Objective clinical analyses are required to evaluate balance control performance. To this outcome, it is relevant to study experimental protocols and to develop devices that can provide reliable information about the ability of a subject to maintain balance. Whereas most of the applications available in the literature and on the market involve shifting and tilting of the base of support, the system presented in this paper is based on the direct application of an impulsive (short-lasting) force by means of an electromechanical device (named automatic perturbator). The control of such stimulation is rather complex since it requires high dynamics and accuracy. Moreover, the occurrence of several non-linearities, mainly related to the human–machine interaction, signals the necessity for robust control in order to achieve the essential repeatability and reliability. A linear electric motor, in combination with Model Predictive Control, was used to develop an automatic perturbator prototype. A test bench, supported by model simulations, was developed to test the architecture of the perturbation device. The performance of the control logic has been optimized by iterative tuning of the controller parameters, and the resulting behavior of the automatic perturbator is presented.
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Hoffmann, Hartmut, Michael F. Zäh, Ingo Faass, Roland Mork, Matthias Golle, Bernd Griesbach y Matthias Kerschner. "Automatic Process Control in Press Shops". Key Engineering Materials 344 (julio de 2007): 881–88. http://dx.doi.org/10.4028/www.scientific.net/kem.344.881.

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The manufacturing of automotive body components in press lines is a sensitive process. The quality characteristics of body components vary. These fluctuations are rooted in the fact that the factors influencing the component quality are varying, e.g., fluctuations of batches regarding material quality, abrasion or heating of the tool during the production cycle. If a certain quality characteristic exceeds a predefined range an intervention in the process is necessary. This intervention is based upon the subjective know-how of the machine operator. Objective information about the state of the process, like tool temperature or the material quality of the semi-finished product is not available. Therefore, a lack of knowledge emerges in the interrelations between the tuning parameters of the system press-tool and the component quality during different stages of the process (material quality, temperature…). In this paper a complete concept for an automatic process control in press shops is described. The concept will be realized in a pilot plant for mass production in the press shop of AUDI AG. The mechanisms of occurrence of quality defects are shown in the paper, as well as the essential factors influencing the quality during the mass production of body components in the automotive industry and their variation. A sensor-system for continuous measurement of influencing variables during the mass production is presented. The key element of the concept is the non-destructive identification of material-properties for every single blank. By associating the sensor-data with the respective quality, a knowledge-based process control can be realized. The purpose is to create a failure prediction algorithm and make optimal adjustments for each stroke of the moulding press, respectively. The potential of existing actuators in modern press lines as well as new, tool integrated proposals for actuators are highlighted.
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Baek, Sujeong. "System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems". International Journal of Advanced Manufacturing Technology 113, n.º 3-4 (29 de enero de 2021): 955–66. http://dx.doi.org/10.1007/s00170-021-06652-z.

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AbstractAs automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.
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Zhang, Lihui, Helei Cui, Hongli Li, Feng Han, Yaqiu Zhang y Wenfu Wu. "Parameters Online Detection and Model Predictive Control during the Grain Drying Process". Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/924698.

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In order to improve the grain drying quality and automation level, combined with the structural characteristics of the cross-flow circulation grain dryer designed and developed by us, the temperature, moisture, and other parameters measuring sensors were placed on the dryer, to achieve online automatic detection of process parameters during the grain drying process. A drying model predictive control system was set up. A grain dry predictive control model at constant velocity and variable temperature was established, in which the entire process was dried at constant velocity (i.e., precipitation rate per hour is a constant) and variable temperature. Combining PC with PLC, and based on LabVIEW, a system control platform was designed.
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Yin, Xiao Hong, Can Yang y Han Zhao. "Sectionalized Motion Control for Automatic Guided Vehicle". Applied Mechanics and Materials 152-154 (enero de 2012): 1127–32. http://dx.doi.org/10.4028/www.scientific.net/amm.152-154.1127.

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In this work, an approach called sectionalized motion control (SMC) was proposed in order to achieve control with high precision and low energy consumption for the entire AGV tracking process. In this method, according to the characteristics of the AGV’s early, middle and terminal motion phases, the neural dynamics-based tracking, energy-efficient tracking, and model predictive technologies were adopted. Furthermore, a simulation using Matlab software was performed in order to verify the proposed approach. The simulation results showed that SMC is capable of providing smooth, energy-efficient, robust and globally stable control for the AGV system.
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Wen, Shuhuan, Jingwei Yang, Ahmad B. Rad, Shengyong Chen y Pengcheng Hao. "Weighted Multimodel Predictive Function Control for Automatic Train Operation System". Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/520627.

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Train operation is a complex nonlinear process; it is difficult to establish accurate mathematical model. In this paper, we design ATO speed controller based on the input and output data of the train operation. The method combines multimodeling with predictive functional control according to complicated nonlinear characteristics of the train operation. Firstly, we cluster the data sample by using fuzzy-c means algorithm. Secondly, we identify parameter of cluster model by using recursive least square algorithm with forgetting factor and then establish the local set of models of the process of train operation. Then at each sample time, we can obtain the global predictive model about the system based on the weighted indicators by designing a kind of weighting algorithm with error compensation. Thus, the predictive functional controller is designed to control the speed of the train. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm.
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Guo, Yan Ping y Qi Cui. "PLC-based Automatic Control of Food Production Line". Advanced Materials Research 482-484 (febrero de 2012): 2214–17. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.2214.

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Standard programmable logic controller (PLC) based proportional-integral-derivative (PID) was originally employed as part of the food production line control strategy, but after observing the response of those process due to measured disturbances during normal operation, it was evident that PID control could not meet the desired cooking specifications. It was decided to pursue advanced process control strategies as a means to meet the food production specifications. The problem of this research study was to design and analyze the performance of a PLC-based model state feedback controller implementation for an industrial food production line, and to determine its viability in comparison to commercially available PC-based model predictive controller implementations applied to food production line.
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Yang, Da Peng, Ping Bo Sun y Ke Qiang Hua. "Model Predictive Control of Flight Arrival Interval". Advanced Materials Research 503-504 (abril de 2012): 1375–80. http://dx.doi.org/10.4028/www.scientific.net/amr.503-504.1375.

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In order to achieve the automation of Air Traffic Control (ATC), use system to identify the controlled model of flights arrival process which has been already built, using Model Predictive Control (MPC) of the dynamic matrix contro1 (DMC) to control the ATC process. According to DMC algorithm and the features of ATC, the design parameters of this system can be determined by a lot of simulations. It proves that the system design and parameters selection make the system has the required performance and the robustness even if the parameters be changed in a wide range. The experiment on the ATC Simulation System proves that the MPC method is available, conclusion of the study provides a new idea and method for the engineering implementation of the automation of flights arrival process control and some improvement of airspace utilization.
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Lin, Chi Ying y Yu Sheng Zeng. "Visual Servoing of Automatic Alignment System Using Model Predictive Control". Key Engineering Materials 625 (agosto de 2014): 627–32. http://dx.doi.org/10.4028/www.scientific.net/kem.625.627.

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Over the past few decades, vision based alignment has been accepted as an important technique to achieve higher economic benefits for precision manufacturing and measurement applications. Also referred to as visual servoing, this technique basically applies the vision feedback information and drives the moving parts to the desired target location using some appropriate control laws. Although recently rapid development of advanced image processing algorithms and hardware have made this alignment process an easier task, some fundamental issues including inevitable system constraints and singularities, still remain as a challenging research topic for further investigation. This paper aims to develop a visual servoing method for automatic alignment system using model predictive control (MPC). The reason for using this optimal control for visual servoing design is because of its capability of handling constraints such as motor and image constraints in precision alignment systems. In particular, a microassembly system for peg and hole alignment application is adopted to illustrate the design process. The goal is to perform visual tracking of two image feature points based on a XYθ motor-stage system. From the viewpoint of MPC, this is an optimization problem that minimizes feature errors under given constraints. Therefore, a dynamic model consisting of camera parameters and motion stage dynamics is first derived to build the prediction model and set up the cost function. At each sample step the control command is obtained by solving a quadratic programming optimization problem. Finally, simulation results with comparison to a conventional image based visual servoing method demonstrate the effectiveness and potential use of this method.
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Tesis sobre el tema "Predictive control ; Process control ; Automatic control"

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Hitzemann, U. "Extensions in non-minimal state-space and state-dependent parameter model based control with application to a DC-DC boost converter". Thesis, Coventry University, 2013. http://curve.coventry.ac.uk/open/items/ca983ce5-bec4-4598-8ac2-48e7302489f5/1.

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This Thesis is concerned with model-based control, where models of linear nonminimal state-space (NMSS) and nonlinear state-dependent parameter (SDP) form are considered. In particular, the focus is on model-based predictive control (MPC) in conjunction with the linear NMSS model and on proportional-integralplus (PIP) pole-assignment control in conjunction with the SDP model. The SDP-PIP pole-assignment controller is based on a nonlinear SDP model, however, the approach uses a linear pole-assignment controller design technique. This ‘potential paradox’ is addressed in this Thesis. A conceptual approach to realising the SDP-PIP pole-assignment control is proposed, where an additional conceptual time-shift operator is introduced. This allows the SDPPIP, at each sampling time instance, to be considered as an equivalent linear controller, while operating, in fact, in a nonlinear overall context. Additionally, an attempt to realise SDP-PIP control, where the SDP model exhibits equivalent linear system numerator zeros, is proposed. Regarding the NMSS MPC, emphasis is on square, i.e. equal number of inputs and outputs, multi-input multi-output (MIMO) modelled systems, which exhibit system output cross-coupling effects. Moreover, the NMSS MPC in incremental input form and making use of an integral-of-errors state variable, is considered. A strategy is proposed, that allows decoupling of the system outputs by diagonalising the closed-loop system model via an input transformation. A modification to the NMSS MPC in incremental input form is proposed such that the transformed system input - system output pairs can be considered individually, which allows the control and prediction horizons to be assigned to the individual pairs separately. This modification allows imposed constraints to be accommodated such that the cross-coupling effects do not re-emerge. A practical example is presented, namely, a DC-DC boost converter operating in discontinuous conduction mode (DCM), for which a SDP model is developed. This model is based on measured input-output data rather than on physical relationships. The model incorporates the output current so that the requirements for the load, driven by the converter, is constrained to remain within a predefined output current range. The proposed SDP model is compared to an alternative nonlinear Hammerstein-bilinear structured (HBS) model. The HBS model is, in a similar manner to the SDP model, also based on measured input-output data. Moreover, the differences as well as the similarities of the SDP and HBS model are elaborated. Furthermore, SDP-PIP pole-assignment control, based on the developed SDP model, is applied to the converter and the performance is compared to baseline linear PIP control schemes.
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Henriksson, Erik. "Predictive Control for Wireless Networked Systems in Process Industry". Doctoral thesis, KTH, Reglerteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-141459.

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Wireless networks in industrial process control enable new system architectures and designs. However, wireless control systems can be severely affected by the imperfections of the communication links. This thesis proposes new methods to handle such imperfections by adding additional components in the control loop, or by adapting sampling intervals and control actions. First, the predictive outage compensator is proposed. It is a filter which is implemented at the receiver side of networked control systems. There it generates predicted samples when data are lost, based on past data. The implementation complexity of the predictive outage compensator is analyzed. Simulation and experimental results show that it can considerably improve the closed-loop control performance under communication losses. The thesis continues with presenting an algorithm for controlling multiple processes on a shared communication network, using adaptive sampling intervals. The methodology is based on model predictive control, where the controller jointly decides the optimal control signal to be applied as well as the optimal time to wait before taking the next sample. The approach guarantees conflict-free network transmissions for all controlled processes. Simulation results show that the presented control law reduces the required amount of communication, while maintaining control performance. The third contribution of the thesis is an event-triggered model predictive controller for use over a wireless link. The controller uses open-loop optimal control, re-computed and communicated only when the system behavior deviates enough from a prediction. Simulations underline the methods ability to significantly reduce computation and communication effort, while guaranteeing a desired level of system performance. The thesis is concluded by an experimental validation of wireless control for a physical lab process. A hybrid model predictive controller is used, connected to the physical system through a wireless medium. The results reflect the advantages and challenges in wireless control.

QC 20140217

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Bowyer, Robert O. "Multiple order models in predictive control". Thesis, University of Oxford, 1998. http://ora.ox.ac.uk/objects/uuid:a5f48d14-0250-4fc6-bb6f-7b43659930e8.

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Predictive control has attracted much attention from both industry and academia alike due to its intuitive time domain formulation and since it easily affords adaption. The time domain formulation enables the user to build in prior knowledge of the operating constraints and thus the process can be controlled more efficiently, and the adaptive mechanism provides tighter control for systems whose behaviour changes with time. This thesis presents a fusion of technologies for dealing with the more practical aspects of obtaining suitable models for predictive control, especially in the adaptive sense. An accurate model of the process to be controlled is vital to the success of a predictive control scheme, and most the of work to date has assumed that this model is of fixed order, a restriction which can lead to poor controller performance associated with under/overparameterisation of the estimated model. To overcome this restriction a strategy which estimates both the parameters and the order of a linear model of the time-varying plant online is suggested. This Multiple Model Least-Squares technique is based on the recent work of Niu and co-workers who have ingeniously extended Bierman's method of UD updating so that, with only a small change to the existing UD update code, a wealth of additional information can be obtained directly from the U and D matrices including estimates of all the lower order models and their loss functions. The algorithm is derived using Clarke's Lagrange multiplier approach leading to a neater derivation and possibly a more direct understanding of Niu's Augmented UD Identification algorithm. An efficient and robust forgetting mechanism is then developed by analysing the properties of the continuous-time differential equations corresponding to existing parameter tracking methods. The resulting Multiple Model Recursive Least-Squares estimator is also ported to the δ-domain in order to obtain models for predictive controllers that employ fast sampling. The MMRLS estimator is then used in an adaptive multiple model based predictive controller for a coupled tanks system to compare performance with the fixed model order case.
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Costa, Thiago Vaz da 1982. "Estudo e implementação de estruturas de controle reconfigurável aplicado a processos químicos". [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266123.

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Orientadores: Flávio Vasconcelos da Silva, Luís Cláudio Oliveira Lopes
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
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Resumo: A consideração sistemática relacionada ao tratamento de falhas na estrutura de controle é essencial para se atingir as metas de um processo ao mesmo tempo seguro e produtivo. Nesse sentido, este trabalho propõe o estudo e a implementação de técnicas baseadas no controle reconfigurável com aplicações em processos químicos. Essa técnica está relacionada à etapa que lida com a falha no sistema em malha fechada, por meio de sua reconfiguração, evitando a evolução dos seus efeitos na estrutura de controle. Nesse estudo, a atenção é voltada ao rastreamento do comportamento nominal do sistema considerando as técnicas baseadas nos atuadores virtuais. O método considera o desvio da planta em relação ao seu comportamento nominal na presença de falhas em seus atuadores. Deste modo, se o distanciamento em relação ao comportamento nominal pode ser estabilizado por meio das redundâncias físicas e analíticas presentes no processo, o comportamento da planta sujeita à falha também pode ser estabilizado. Neste sentido, uma consideração sobre esse desvio no futuro é proposta, permitindo que técnicas baseadas no controle ótimo e preditivo sejam utilizadas. São consideradas especificamente falhas que comprometam o desempenho do processo quando existe a perda total ou parcial de suas variáveis de entrada. Com os componentes comprometidos devidamente localizados, a redistribuição dos sinais de controle da planta é encaminhada para que a estabilidade e desempenho do processo sejam assegurados. A concepção do atuador virtual com horizonte móvel possibilita o uso de técnicas de otimização, permitindo que as informações referentes às características físicas das redundâncias do processo sejam incluídas, evitando a saturação dos componentes utilizados durante o tratamento da falha. A validação da técnica proposta é avaliada experimentalmente em um processo de neutralização, no qual ferramentas de injeção de falhas são capazes de simular os efeitos de falha dos atuadores no processo real. As estratégias estudadas foram implementadas em um software em código aberto e foram testadas experimentalmente a partir do uso de comunicação OPC (Object Linking and Embedding for Process Control). Cenários com diferentes falhas afetando os atuadores do processo foram concebidos e avaliados. Em todos os estudos, observou-se a capacidade da ferramenta em efetuar a reconfiguração do sistema em malha fechada para mitigar os efeitos indesejáveis da falha. Com base nos resultados, conclui-se que a utilização das técnicas em cenários industriais é viável e pode garantir a operação estável do processo mesmo quando surjam falhas em seus componentes, consideradas as condições de controlabilidade e estabilizabilidade do sistema
Abstract: A systematic approach to fault handling in control loops is important to achieve safe and productive process goals. Therefore, the present work proposes the study and implementation of reconfigurable control methods with applications in chemical processes. This technique is related to the fault handling stage in the closed loop system by means of reconfiguration, preventing the progression of fault effects in the control structure. In this study, particular focus is given to virtual actuator based techniques for tracking nominal system behavior. The method considers the deviation of the plant in relation to its nominal behavior when there are faults in its actuators. Therefore, if the nonconformity from the nominal response can be stabilized using the process physical and analytical redundancies, the faulty plant behavior can also be stabilized. Hence, it is proposed the use of the deviation from the nominal system response in the future, enabling the use of optimal and predictive control techniques. Faults affecting process performance due to total or partial loss of input variables are particularly considered. Once the faulty components are located, input signal redistribution is determined in order to ensure process performance and stability. The moving horizon virtual actuator proposal provides the use of optimization techniques allowing information about physical characteristics of the process redundancies to be included, avoiding component saturation during the fault handling approach. The validation of the technique is performed experimentally in a neutralization process, in which fault injection tools are able to simulate the effects of actuators faults in the real process. The studied strategies were implemented using open-source software and tested experimentally by means of an OPC (Object Linking and Embedding for Process Control) communication. Several fault scenarios considering actuator faults were planned and fulfilled for evaluation. The experimental tests showed that the proposed framework is capable of performing closed loop system reconfiguration to mitigate undesirable fault effects. Based on the results, it is concluded that using such a technique in industrial scenarios is feasible and can guarantee stable process operation even when there are faults in its components, considering system controllability and stabilizability conditions
Doutorado
Sistemas de Processos Quimicos e Informatica
Doutor em Engenharia Química
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Costa, Thiago Vaz da 1982. "Controle preditivo baseado em rede de modelos lineares locais aplicado a um reator de neutralização". [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266959.

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Orientadores: Flávio Vasconcelos da Silva, Ana Maria Frattini Fileti
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
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Resumo: Uma malha de controle com baixo desempenho implica em um aumento dos custos de produção, causando descarte de produto fora da especificação e desgaste desnecessário dos elementos finais de controle. A depender do processo controlado, uma malha deficiente pode também acarretar em paradas não previstas na planta e até mesmo em danos ao meio ambiente. Diante do exposto, o controle preditivo baseado em modelos (MPC) é um dos poucos algoritmos comprovadamente capazes de estabilizar processos na presença de não-linearidades e restrições. Para atender aos seus objetivos de controle, o algoritmo clássico MPC utiliza um procedimento de otimização baseado no modelo linear da planta. Contudo, o afastamento da região de projeto do modelo linear resulta na perda de sua efetividade e consequente do controlador que o utiliza. Deste modo, objetivou-se a partir de uma descrição não-linear do sistema a melhoria do desempenho do controlador. Os objetivos específicos dessa dissertação foram o estudo e análise de um controlador GPC (generalized predictive controller) operando em paralelo com uma rede de modelos lineares locais, identificada por meio do algoritmo LOLIMOT (local linear model trees), capaz de adequar o modelo de predição do controlador para a faixa de operação atual do processo. Para a avaliação e análise da qualidade do controlador proposto foi montado um aparato experimental para controle de pH. A estratégia de controle foi implementada em um sistema em código aberto para monitoramento e controle do processo. Portanto, considerando a característica estática não-linear do processo foram realizados estudos comparativos entre o GPC tradicional (baseado em um único modelo linear) e a abordagem proposta. Os resultados mostraram que a rede foi capaz de representar satisfatoriamente a saída do sistema, resultando em uma estrutura simples, com modelos locais na forma de estruturas ARX. Também foi demonstrado que o GPC utilizando a rede de modelos lineares locais desempenhou de forma satisfatória e até mesmo superior ao GPC tradicional.Observou-se que a saída calculada pelo controlador proposto foi consideravelmente menos agressiva que o controlador tradicional, levando a uma considerável diminuição do esforço de controle empregado ao sistema. Os resultados obtidos demonstraram que houve uma economia de até 45% no esforço de controle. Observou-se ainda que o sistema desenvolvido é conveniente para aplicações reais, já que a estratégia de controle preditivo concebida em Scilab obteve sucesso na solução do problema de controle dentro do intervalo de amostragem inclusive quando incorporado o problema QP (programação quadrática) com restrições. Tendo em vista que a estrutura destes sistemas permite que sejam utilizados nas mesmas aplicações destinadas a modelos lineares, comprovou-se também a viabilidade e aplicabilidade do uso das redes de modelos lineares locais diretamente em algoritmos de controle avançado já disponíveis para indústria, como nos controladores GPC
Abstract: Low performance control loops imply in higher production costs, leading to off-specification production loss and unnecessary wear of the final control elements. Depending on the controlled process, the deficient loop can also lead to non-expected plant stops and even on environment damage. In this sense, model predictive control (MPC) is one of the few algorithms proved capable to stabilize processes in the presence of nonlinearities and constraints. To meet its control objectives, the classic MPC algorithm is based on an optimization problem which relies in the system's linear model. Although, the removal of the linear model from its designed condition deteriorates the model's and controller's effectiveness. Hence, the general objective of the presented work relies in the non-linear description of the system for improving the control performance. The main objectives were the study and analysis of a generalized predictive controller (GPC) operating in parallel with a linear local model network, identified by the LOLIMOT algorithm, able to adequate the controller's prediction model for the process operation range. For the quality assessment of the proposed controller, tests were evaluated in an experimental apparatus for pH control. The control strategy was implemented in an open source system for monitoring and control. Therefore, considering the static nonlinear characteristics of the process, comparative studies were applied between the traditional GPC (based on an single global model) and the proposed approach. The results showed that the dynamic network was able to effectively represent the system output, resulting in a simple structure, given the fact that the local models are indeed local ARX models. It was also shown that the GPC using the linear local model network performed satisfactorily and even better than the single model GPC. It was observed that the output calculated by the proposed controller has been considerably less aggressive than the traditional controller, leading to a considerable reduction in the system's control effort. The results showed that there was a saving up to 45% in the control effort. It was also observed that the developed monitoring and control system is suitable for real applications, since the predictive control strategy, implemented in Scilab, succeeded in solving the control problem within the sampling time even with the embedded constrained QP (quadratic programming) problem. Considering that the structure of these systems allows them to be used in similar applications to linear models, it was also proved the viability and applicability of using the linear local models network directly into advanced control algorithms already available to industry as in the GPC controllers
Mestrado
Sistemas de Processos Quimicos e Informatica
Mestre em Engenharia Química
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Clavel, Fanny. "Modélisation et contrôle d'un réfrigérateur cryogénique Application à la station 800W à 4.5K du CEA Grenoble". Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00576608.

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Cette thèse concerne le développement de nouvelles stratégies de contrôle d'unréfrigérateur cryogénique soumis à de fortes variations de charge thermique. De telles perturbationsvont se rencontrer lors du refroidissement des aimants supraconducteurs des futurs réacteurs defusion (tokamak JT-60SA par exemple).La modélisation d'un réfrigérateur de test, offrant une capacité de refroidissement de 800Wà 4.5K, a été effectuée sous le logiciel Matlab/Simulink. Celle-ci est basée sur les équationsthéoriques de la thermodynamique, de la thermique et de l'hydraulique et prend en compte lespropriétés non linéaire de l'hélium à basse température.A partir de ce modèle, une stratégie de contrôle multivariable a été proposée sur les deuxparties du réfrigérateur : la station de compression et la boîte froide. Les résultats expérimentauxmontrent de nettes améliorations et une plus grande stabilité du réfrigérateur en présence decharges pulsées par rapport à la stratégie initiale (PI).Un observateur de la charge thermique du bain d'hélium liquide a également été développé.Le modèle utilisé est construit par identification à partir de mesures internes au réfrigérateur. Ilpourrait servir comme outil de surveillance aux opérateurs.
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Boza, Condorena Edwin Guido 1958. "Identificação de processos e controle preditivo com modelo utilizando técnicas de inteligência artificial aplicadas à produção de bioetanol". [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266648.

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Orientador: Aline Carvalho da Costa
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
Made available in DSpace on 2018-08-21T12:53:49Z (GMT). No. of bitstreams: 1 BozaCondorena_EdwinGuido_D.pdf: 5093932 bytes, checksum: 505cbc2b6c875e35004ad875c0f0ac6a (MD5) Previous issue date: 2012
Resumo: Na presente tese foram abordados problemas no contexto da produção de etanol de primeira e segunda geração. No caso de etanol de segunda geração foi abordado o problema de melhorar a eficiência da hidrolise enzimática, com as seguintes contribuições: (1) otimização da carga enzimática utilizando técnicas de modelagem por redes neurais. (2) substituição de um modelo com limitações na representatividade dos pontos experimentais obtido por desenho experimental pela superfície de resposta de um modelo de redes neurais que permite a exploração da região do ótimo no espaço de fatores e respostas. Os resultados obtidos mostram (1) boa precisão para a localização das coordenadas da região ótima de trabalho. (2) um mapeamento da evolução do processo com localização do ótimo global utilizando algoritmos genéticos e técnicas de computação evolutiva. No caso dos processos de produção de etanol de primeira geração foi estudado o problema do controle com modelo de um processo de fermentação continua com extração de etanol utilizando vácuo quando não se tem modelo fenomenológico disponível com as seguintes contribuições e resultados: (1) foi desenvolvida uma abordagem que integrou redes neurais artificiais, com o controle preditivo com modelo (MPC). (2) foram desenvolvidos modelos empíricos do processo com redes neurais artificiais (3) foi desenvolvida uma abordagem que utiliza o "conhecimento aprendido" pelas redes neurais o qual e armazenado em pesos sinápticos e bias. (4) foram desenvolvidos vários modelos empíricos de redes neurais para o monitoramento das concentrações de etanol que podem ser utilizados para desenvolver software sensores. (5) foram implementadas diferentes estruturas de controle preditivo com diferentes modelos internos de redes neurais para os controladores, com otimiza dor linear e não linear para o caso de estudo. Nos diferentes capítulos em que foram implementadas estruturas de controle integrando as redes neurais com a tecnologia MPC, se mostrou que, a abordagem desenvolvida e eficiente para os projetos dos sistemas de controle com modelo empírico
Abstract: In this thesis were studied problems in the context of ethanol production of first and second generation. In the case of second generation ethanol was studied the problem of improving the enzymatic hydrolysis efficiency, with the following contributions: 1) optimization of enzyme loading by using modeling techniques based on neural networks, 2) substitution of a model with limited representation capacity of the experimental points which was obtained by using experimental design by the response surface model of a neural network that allows the exploration of the space of factors and responses. The results show: 1) good accuracy in locating the coordinates of the optimum working region, 2) the mapping of the evolution of the process with the location of the global optimum by using genetic algorithms and evolutionary computing techniques. In case of the production of first generation ethanol was studied the problem of the control with process model of a fermentation process with continuous extraction of ethanol by using a vacuum system when there is no a phenomenological model available with the following contributions and results: 1) It was developed an approach in which were integrated artificial neural networks with model predictive control (MPC). 2) It were developed empirical process models by using artificial neural networks 3) It was developed an approach that uses the "learned knowledge" by neural networks which is stored in synaptic weights and bias. 4) It were developed several empirical models of neural networks for monitoring concentrations of ethanol that can be used to develop software sensors. 5) It were implemented different predictive control structures with different internal models based on neural networks for controllers with linear and non-linear optimizer to be applied on the case of study. In the different chapters in which were implemented control structures by integrating neural networks with MPC technology, it was showed that the developed approach is efficient to be applied in the designs of control systems with empirical model
Doutorado
Desenvolvimento de Processos Químicos
Doutor em Engenharia Química
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Tamagnini, Filippo. "EKF based State Estimation in a CFI Copolymerization Reactor including Polymer Quality Information". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20235/.

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State estimation is an integral part of modern control techniques, as it allows to characterize the state information of complex plants based on a limited number of measurements and the knowledge of the process model. The benefit is twofold: on one hand it has the potential to rationalize the number of measurements required to monitor the plant, thus reducing costs, on the other hand it enables to extract information about variables that have an effect on the system but would otherwise be inaccessible to direct measurement. The scope of this thesis is to design a state estimator for a tubular copolymerization reactor, with the aim to provide the full state information of the plant and to characterize the quality of the product. Due to the fact that, with the existing set of measurements, only a small number of state variables can be observed, a new differential pressure sensor is installed in the plant to provide the missing information, and a model for the pressure measurement is developed. Following, the state estimation problem is approached rigorously and a comprehensive method for analyzing, tuning and implementing the state estimator is assembled from scientific literature, using a variety of tools from graph theory, linear observability theory and matrix algebra. Data reduction and visualization techniques are also employed to make sense of high dimensional information. The proposed method is then tested in simulations to assess the effect of the tuning parameters and measured set on the estimator performance during initialization and in case of estimation with plant-model mismatch. Finally, the state estimator is tested with plant data.
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Al, Seyab Rihab Khalid Shakir. "Nonlinear model predictive control using automatic differentiation". Thesis, Cranfield University, 2006. http://hdl.handle.net/1826/1491.

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Although nonlinear model predictive control (NMPC) might be the best choice for a nonlinear plant, it is still not widely used. This is mainly due to the computational burden associated with solving online a set of nonlinear differential equations and a nonlinear dynamic optimization problem in real time. This thesis is concerned with strategies aimed at reducing the computational burden involved in different stages of the NMPC such as optimization problem, state estimation, and nonlinear model identification. A major part of the computational burden comes from function and derivative evaluations required in different parts of the NMPC algorithm. In this work, the problem is tackled using a recently introduced efficient tool, the automatic differentiation (AD). Using the AD tool, a function is evaluated together with all its partial derivative from the code defining the function with machine accuracy. A new NMPC algorithm based on nonlinear least square optimization is proposed. In a first–order method, the sensitivity equations are integrated using a linear formula while the AD tool is applied to get their values accurately. For higher order approximations, more terms of the Taylor expansion are used in the integration for which the AD is effectively used. As a result, the gradient of the cost function against control moves is accurately obtained so that the online nonlinear optimization can be efficiently solved. In many real control cases, the states are not measured and have to be estimated for each instance when a solution of the model equations is needed. A nonlinear extended version of the Kalman filter (EKF) is added to the NMPC algorithm for this purpose. The AD tool is used to calculate the required derivatives in the local linearization step of the filter automatically and accurately. Offset is another problem faced in NMPC. A new nonlinear integration is devised for this case to eliminate the offset from the output response. In this method, an integrated disturbance model is added to the process model input or output to correct the plant/model mismatch. The time response of the controller is also improved as a by–product. The proposed NMPC algorithm has been applied to an evaporation process and a two continuous stirred tank reactor (two–CSTR) process with satisfactory results to cope with large setpoint changes, unmeasured severe disturbances, and process/model mismatches. When the process equations are not known (black–box) or when these are too complicated to be used in the controller, modelling is needed to create an internal model for the controller. In this thesis, a continuous time recurrent neural network (CTRNN) in a state–space form is developed to be used in NMPC context. An efficient training algorithm for the proposed network is developed using AD tool. By automatically generating Taylor coefficients, the algorithm not only solves the differentiation equations of the network but also produces the sensitivity for the training problem. The same approach is also used to solve online the optimization problem of the NMPC. The proposed CTRNN and the predictive controller were tested on an evaporator and two–CSTR case studies. A comparison with other approaches shows that the new algorithm can considerably reduce network training time and improve solution accuracy. For a third case study, the ALSTOM gasifier, a NMPC via linearization algorithm is implemented to control the system. In this work a nonlinear state–space class Wiener model is used to identify the black–box model of the gasifier. A linear model of the plant at zero–load is adopted as a base model for prediction. Then, a feedforward neural network is created as the static gain for a particular output channel, fuel gas pressure, to compensate its strong nonlinear behavior observed in open–loop simulations. By linearizing the neural network at each sampling time, the static nonlinear gain provides certain adaptation to the linear base model. The AD tool is used here to linearize the neural network efficiently. Noticeable performance improvement is observed when compared with pure linear MPC. The controller was able to pass all tests specified in the benchmark problem at all load conditions.
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Rosdal, David. "Missilstyrning med Model Predictive Control". Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2748.

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This thesis has been conducted at Saab Bofors Dynamics AB. The purpose was to investigate if a non-linear missile model could be stabilized when the optimal control signal is computed considering constraints on the control input. This is particularly interesting because the missile is controlled with rudders that have physical bounds. This strategy is called Model Predictive Control. Simulations are conducted to compare this strategy with others; firstly simulations with step responses and secondly simulations when the missile is supposed to hit a moving target. The latter is performed to show that the missile can be stabilized in its whole area of operation. The simulations show that the controller indeed can stabilize the missile for the given scenarios. However, this control strategy does not show any obvious improvements in comparison with alternative ones.

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Libros sobre el tema "Predictive control ; Process control ; Automatic control"

1

Haber, Robert. Predictive control in process engineering: From the basics to the applications. Weinheim: Wiley-VCH, 2011.

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Köln), Process Automation Workshop (2008 Fachhochschule. Control and monitoring algorithms in process automation applications: Extended proceedings of the Process Automation Workshop 2008 at the Cologne University of Applied Sciences. Aachen: Shaker Verlag, 2012.

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McMillan, Gregory K. Models unleashed: Virtual plant and model predictive control applications : a pocket guide. Research Triangle Park, NC: ISA, 2004.

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Bao-Cang, Ding. Modern predictive control. Boca Raton: Taylor & Francis, 2010.

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Modern predictive control. Boca Raton: Taylor & Francis, 2010.

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1967-, Tan Kok Kiong y Lee Tong Heng 1958-, eds. Applied predictive control. London: Springer, 2002.

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Haber, Robert, Ruth Bars y Ulrich Schmitz. Predictive Control in Process Engineering. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527636242.

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Camacho, E. F. Model predictive control. 2a ed. New York: Springer, 2004.

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1962-, Bordons C., ed. Model predictive control. Berlin: Springer, 1999.

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Camacho, E. F. Model predictive control. London: Springer, 2003.

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Capítulos de libros sobre el tema "Predictive control ; Process control ; Automatic control"

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Corriou, Jean-Pierre. "Generalized Predictive Control". En Process Control, 611–30. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61143-3_15.

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Corriou, Jean-Pierre. "Model Predictive Control". En Process Control, 631–77. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61143-3_16.

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Corriou, Jean-Pierre. "Generalized Predictive Control". En Process Control, 555–73. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3848-8_15.

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Corriou, Jean-Pierre. "Model Predictive Control". En Process Control, 575–615. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3848-8_16.

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Rao, Ming, Qijun Xia y Yiqun Ying. "Predictive Control". En Modeling and Advanced Control for Process Industries, 71–122. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-2094-0_4.

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Rengaswamy, Raghunathan, Babji Srinivasan y Nirav Pravinbhai Bhatt. "Model Predictive Control". En Process Control Fundamentals, 229–50. First edition. | Boca Raton : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780367433437-8.

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Tatjewski, Piotr y Maciej Ławryńczuk. "Nonlinear Predictive Control". En Automatic Control, Robotics, and Information Processing, 189–228. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48587-0_7.

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Weik, Martin H. "automatic process control". En Computer Science and Communications Dictionary, 86. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_1125.

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Kalbasenka, Alex N., Adrie E. M. Huesman y Herman J. M. Kramer. "Model Predictive Control". En Industrial Crystallization Process Monitoring and Control, 185–201. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527645206.ch16.

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Christofides, Panagiotis D., Jinfeng Liu y David Muñoz de la Peña. "Networked Predictive Process Control". En Networked and Distributed Predictive Control, 47–98. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-582-8_3.

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Actas de conferencias sobre el tema "Predictive control ; Process control ; Automatic control"

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Suwannik, Podcharapol, Tanagorn Jennawasin y David Banjerdpongchai. "Design of linear model predictive control for level control process with output feedback from wireless transmitter". En 2016 International Automatic Control Conference (CACS). IEEE, 2016. http://dx.doi.org/10.1109/cacs.2016.7973897.

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Efheij, Hafed, Abdulgani Albagul y Nabela Ammar Albraiki. "Comparison of Model Predictive Control and PID Controller in Real Time Process Control System". En 2019 19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA). IEEE, 2019. http://dx.doi.org/10.1109/sta.2019.8717271.

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Ye Yanfei, Wang Bailin, Shao Mingheng y Zhang Yongqi. "Improved generalized predictive control in polymerization process". En 2011 Second International Conference on Mechanic Automation and Control Engineering (MACE). IEEE, 2011. http://dx.doi.org/10.1109/mace.2011.5987195.

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Liang Wang y Jingtao Hu. "Fuzzy predictive R2R control to CMP process". En 2011 IEEE International Conference on Computer Science and Automation Engineering (CSAE). IEEE, 2011. http://dx.doi.org/10.1109/csae.2011.5952412.

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Ng, James, Stevan Dubljevic y Ilyasse Aksikas. "Model predictive control of Czochralski crystal growth process". En Automation (MED 2011). IEEE, 2011. http://dx.doi.org/10.1109/med.2011.5983228.

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Cao, Gang, Edmund M.-K. Lai y Fakhrul Alam. "Gaussian Process Model Predictive Control of unmanned quadrotors". En 2016 2nd International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2016. http://dx.doi.org/10.1109/iccar.2016.7486726.

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Tohidi, Akbar y Hadi Hajieghrary. "Self-Tuning Adaptive Multiple Model Predictive Control with application to pH Control process". En 2016 IEEE International Conference on Automation Science and Engineering (CASE). IEEE, 2016. http://dx.doi.org/10.1109/coase.2016.7743545.

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Lao, Liangfeng, Matthew Ellis y Panagiotis D. Christofides. "Economic model predictive control of a transport-reaction process". En 2013 21st Mediterranean Conference on Control & Automation (MED). IEEE, 2013. http://dx.doi.org/10.1109/med.2013.6608742.

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"MULTI-OBJECTIVE PREDICTIVE CONTROL: APPLICATION FOR AN UNCERTAIN PROCESS". En 2nd International Conference on Informatics in Control, Automation and Robotics. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001181602330238.

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Kun Qian y YuMing Zhang. "Optimal model predictive control of plasma pipe welding process". En 2008 IEEE International Conference on Automation Science and Engineering (CASE 2008). IEEE, 2008. http://dx.doi.org/10.1109/coase.2008.4626562.

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Informes sobre el tema "Predictive control ; Process control ; Automatic control"

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Feierl, Lukas y Peter Luidolt. Automated monitoring, failure detection of key components, control strategies and self-learning controls of key components. IEA SHC Task 55, septiembre de 2020. http://dx.doi.org/10.18777/ieashc-task55-2020-0005.

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