The monitoring of control loops in industrial processes is of great importance, considering that the correct operation of the productive procedures is related to the control loops that make up the system. Mostly, industrial processes are composed of a large amount of control loops that interact with each other, that means both are coupled, therefore, if one of the loops does not work properly it can negatively affect the system performance, leading the other loops into setpoints that were not designed for them. It has been found that many responsible causes for poor system performance can be identified by stochastic or deterministic performance indices. These performance indices, from a theoretical perspective, allow making relevant decisions, such as design parameters adjustment of the controllers or actuators maintenance. The most known are the stochastic performance index, it requires only normal operation and knowledge of the process. However, the performance analysis in a lot of cases is not conclusive and can present scale problems. On the contrary, deterministic performance index are easier to interpret, favoring the analysis and deduction of the operator. Nevertheless, it is necessary to perform invasive tests to get them, which makes it impractical.Therefore, this work obtains a deterministic index through a inferential model built with machine learning-based neural networks that use as input the stochastic index acquired throughout recollecting the normal operational data in closed loop and in the knowledge process. furthermore, count with a graphic interface that allows the operator interactively to get performance and robustness values represented in the deterministic indices. The strategy is put on test in a real study case of sensing levels for the industrial control process FESTO® MPS-PA Compact Workstation.
In several industries using pipelines to transport different products from one point to another is a common and indispensable process, especially at oil/hydrocarbon industries. Thus, optimizing the way this process is carried out must be an issue that cannot be stopped. Therefore, the performance of the control strategy implemented is one way of reaching such optimal operating zones. This study proposes using Model Predictive Control strategies for solving some issues related to the proper operation of pipelines. It is proposed a model based on physics and thermodynamic laws, using MATLAB® as the development environment. This model involves four pumping stations separated by three pipeline sections. Three MPC strategies are developed and implemented. Accordingly, the results indicate that a centralized controller with an antiwindup back-calculation method has the best results among the three configurations used.
Las personas tienen necesidades diversas y cambiantes a medida que envejecen y el número de personas que viven con alguna discapacidad está constantemente en aumento. Las casas inteligentes tienen un potencial único para proporcionar vida asistida, pero a menudo están diseñados de manera rígida con un problema específico y fijo en mente. Por esta razón se realiza una investigación con el fin de identificar esos componentes de la inteligencia ambiental aplicada en la domótica dirigida a la población del adulto mayor o pacientes que viven con alguna discapacidad física o mental. Se realizó una revisión de la literatura en tres (3) distintas bases de datos. de documentos publicados por diferentes universidades, revistas y asociaciones profesionales en el contexto internacional; la temática abordar fue sobre la inteligencia ambiental aplicada en la domótica. Dirigido a una población en específico que es el adulto mayor o pacientes que viven con alguna discapacidad debido a la edad. En esta investigación Se identificaron los objetivos principales, factores importantes y la confiabilidad de esta tecnología aplicada en los hogares inteligentes teniendo en cuenta la población a la que va dirigida.
This article presents the design of robust control system for the Ball and Beam system, as well as the comparison of their performances with classic control techniques. Two controllers were designed based on Algebraic Riccati Equations for the synthesis of H 2 and H ∞ controllers and a third one based on Linear Matrix Inequalities techniques for the design of H ∞ controllers. The results show that H ∞ controllers offer better performance.
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