| Sigurd Skogestad - Norwegian University of Science and Technology, Noruega |  |
| Fecha y Hora: Miércoles 13 de octubre a las 9h. Tiempo del Centro de México = UTC/GMT (CNM) – 5 Lugar: Online | |
| Resumen: | |
| Control engineers rely on many tools, and although some people may think that in the future there will be one general universal tool that solves all problems, like model predictive control, this is not likely to happen. The main reason is that the possible benefits of using more general tools may not be worth the increased implementation costs (including modelling efforts) compared to using simpler "conventional" advanced process control (APC) solutions. In particular, this applies to process control, where each process is often unique. In addition, for a new process, a model is usually not available, so at least for the initial period of operation a conventional scheme must be implemented. Conventional APC includes the “advanced” standard control elements that typically are provided at the DCS (distributed control system) level and which industry commonly uses to enhance control when simple single-loop PID controllers do not give acceptable control performance. Examples of such control elements are cascade control, selectors (override), split range control, input or valve position control (VPC), multiple controllers (and MVs) for the same CV, and nonlinear calculation blocks (including nonlinear feedforward and decoupling and linearizing adaptive gain elements). Since its introduction in the 1940’s, about 80 years ago, conventional APC has largely been overlooked by the academic community, yet it is still thriving in industrial practice, even after 40 years with MPC. So, it seems safe to predict that conventional APC (including PID control) will not be replaced by MPC, but will remain in the toolbox along with MPC. The goal of this talk is to take a systematic view on how to design a conventional APC system. The starting point is the plantwide optimal steady-state economic operation, with focus on constraints. |
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| Biografía: | |
| Sigurd Skogestad received his Ph.D. degree from the California Institute of Technology, Pasadena, USA in 1987. He has been a full professor at Norwegian University of Science and Technology (NTNU), Trondheim, Norway since 1987. He is the principal author, together with Prof. Ian Postlethwaite, of the book "Multivariable feedback control" published by Wiley in 1996 (first edition) and 2005 (second edition). His research interests include the use of feedback as a tool to make the system well-behaved (including self-optimizing control), limitations on performance in linear systems, control structure design and plantwide control, interactions between process design and control, and distillation column design, control and dynamics. His other main interests are mountain skiing (cross country), orienteering (running around with a map) and grouse hunting. He is a Fellow of the American Institute of Chemical Engineers (2012) and IFAC (2014). | |
| Cristina Verde - Instituto de Ingeniería-UNAM, México |  |
| Fecha y Hora: Miércoles 13 de octubre a las 15h. Tiempo del Centro de México = UTC/GMT (CNM) – 5 Lugar: Online | |
| Resumen: | |
| En la actualidad, los procesos físicos operan conjuntamente con software formando sistemas complejos para satisfacer especificaciones de diseño con múltiples lazos de control, clases de variables, y garantizando normas de seguridad. Este avance de la tecnología con el cual se ha logrado seguimiento de trayectorias y optimización de sistemas en tiempo real, obliga a considerar esquemas automáticos de supervisión y predicción de eventos incorporados al sistema de control, a través de sensores no necesariamente usados para el control. La presentación está enfocada al diseño del supervisor y el control tolerante a fallas de sistemas complejos, estableciendo como parte de las tareas de un control seguro la seguridad física y la cibernética. Como aplicaciones específicas se presenta el caso de una central eléctrica de ciclo combinado y el de un sistema de generación de energía eléctrica a partir de oleaje del mar. | |
| Marcos Orchard - Universidad de Chile, Chile |  |
| Fecha y Hora: Jueves 14 de octubre a las 9h. Tiempo del Centro de México = UTC/GMT (CNM) – 5 Lugar: Online | |
| Resumen: | |
| This talk explores the fundamentals of the discipline of Prognostics and Health Management (PHM), with emphasis on the problem of characterizing the probability of critical failure events and how to use this information in the context of Prognostic Decision-Making. | |
| Biografía: | |
| Dr. Marcos Orchard is Professor with the Department of Electrical Engineering at Universidad de Chile and former member of the Intelligent Control Systems Laboratory at The Georgia Institute of Technology. His current research interests are the design and implementation of real-time machine learning algorithms for system identification, fault diagnosis, and failure prognostics. His research work at the Georgia Institute of Technology was the foundation of novel real-time fault diagnosis and failure prognostics approaches based on particle filtering algorithms. He received his Ph.D. and M.S. degrees from The Georgia Institute of Technology, Atlanta, GA, in 2005 and 2007, respectively. He received his B.S. degree (1999) and a Civil Industrial Engineering degree with Electrical Major (2001) from Catholic University of Chile. The quality of the research conducted by Dr. Marcos Orchard has been widely recognized by researchers in several centers of excellence (NASA Ames Research Center, CALCE - University of Maryland, Tarbes National School of Engineering, University of Waterloo), and especially by the Prognostics and Health Management Society (PHMS) with the distinction "PHMS Fellow" for his significant contributions to the state of the art of the discipline for more than 15 years, his impact on innovation, creativity and leadership demonstrated in his professional work. This has been endorsed by a clear positive trend regarding the number of citations associated with his prolific published work (more than 100 articles, 50 of them in mainstream indexed journals, 1618 citations and h-index 22 (Researcher ID), h-index 32 (Google Scholar), as for awards given by independent analysis groups, such as SAGE Publications USA and “Research Interfaces”, which have highlighted the value and quality of the research conducted in Chile. Dr. Orchard is Editor-in-Chief of the International Journal of Prognostic and Health Management (ISSN 2153-2648). | |
| Martin Guay - Queen’s University, Canadá |  |
| Fecha y Hora: Jueves 14 de octubre a las 15h. Tiempo del Centro de México = UTC/GMT (CNM) – 5 Lugar: Online | |
| Resumen: | |
| The complexity of system dynamics in chemical processes can often be an obstacle in the development of reliable dynamical models. In traditional process control engineering methodologies, the knowledge of process dynamic has always been a key element in the design, testing and implementation of control systems. Since the development of reliable nonlinear dynamical models is often restrictively onerous and fraught with technical and experimental difficulties, the access of high-quality dynamical models is often limited or inexistent in many industrial sectors.
As a result, control systems often rely on simplistic model forms, such as linear models or neural network approximations, that can only provide local performance guarantees and limited regulation capabilities. Over the last twenty years, the onset of machine (and deep) learning has changed the focus of control practitioners by highlighting the importance of process data (which can be extensive in many industrial sectors). The last five years has seen a tremendous amount of research activity on the development of model free control techniques. Many learning techniques such as reinforcement learning have been proposed to design of real-time system that can achieve near optimal control performance. While learning techniques can be quite general, they offer only limited stability and performance guarantees. Data driven control techniques have emerged as a possible alternative to learning techniques. The leading techniques generalize closed-loop system identification methodologies from the 70s and 80s. As such, they focus primarily on linear dynamical models. Their implementation relies on persistency of excitation conditions which require a carefully planned exploration and exploitation methodology. Recent activities have confirmed the guarantee of stability and performance of data driven control. However, the application of data driven control for nonlinear systems remains an open problem. Methodologies such as kernel-based techniques and Koopman operators have been applied for the identification and control of nonlinear dynamical systems in the context of data driven approaches. While the potential of such techniques is tremendous, their application in control remains limited to linear control techniques like model predictive control. The design of model free real-time optimization techniques has also been an area of tremendous development over the last ten years. One leading model free technique is extremum-seeking control (ESC). This classical adaptive optimization technique was first designed to solve steady state optimization problems for nonlinear dynamical systems in which one seeks the optimum steady state of a measured objective function. In contrast to other data driven techniques, ESC provides stability and performance guarantees that rely on minimal assumptions about the process dynamics. It does not require any knowledge about the process dynamical model or the objective function. This feature is quite appealing in practical situation. Correspondingly, the technique has been applied extensively in many application areas such as biomedical engineering, aerospace engineering, automotive, biotechnology and process control. In this presentation, we seek to review some of the outstanding developments on the generalization of extremum seeking control as a potential alternative to existing data-driven techniques. In particular, it is shown how one can apply this technique to design reliable control systems that require only limited knowledge of the process dynamics. The results will seek to address some of the limitations of the proposed data driven approach. New areas of research are identified to fully realize the potential of ESC as a data driven control approach. |
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| Biografía: | |
| Martin Guay (M’03–SM’18)) received the Ph.D. degree from Queen’s University, Kingston, ON, Canada, in 1996. He is currently a Professor with the Department of Chemical Engineering, Queen’s University. His current research interests include nonlinear control systems, especially extremum-seeking control, nonlinear model predictive control, adaptive estimation and control, and geometric control. Dr. Guay was a recipient of the Syncrude Innovation Award, the D. G. Fisher from the Canadian Society of Chemical Engineers, and the Premier Research Excellence Award. He is a Senior Editor of IEEE Control Systems Letters. He is the Deputy Editor-in-Chief of the Journal of Process Control. He is also an Associate Editor for Automatica, the IEEE Transactions on Automatic Control, the Canadian Journal of Chemical Engineering, and Nonlinear Analysis: Hybrid Systems. | |
| Julia Badger - NASA, EUA |  |
| Fecha y Hora: Viernes 15 de octubre a las 9h. Tiempo del Centro de México = UTC/GMT (CNM) – 5 Lugar: Online | |
| Resumen: | |
| As humans look to explore the solar system beyond low Earth orbit, the technology advancements required point heavily towards autonomy. The operation of complex human spacecraft has thus far been solved with heavy human involvement- full ground control rooms and nearly constantly inhabited spacecraft. As the goal of space exploration moves to beyond the International Space Station, the physical and budgetary constraints of business as usual become overwhelming. A new paradigm of delivering spacecraft and other assets capable of self-maintenance and self-operation prior to launching crew solves many problems- and at the same time, it opens up an array of interesting control problems. This talk will focus on robotic and autonomous vehicle system control development efforts that support the new concepts of human exploration of the solar system. | |
| Biografía: | |
| Dr. Julia Badger is the Autonomy and Vehicle Systems Manager (VSM) system manager for the Gateway program at NASA-Johnson Space Center. She also serves as the Autonomous Systems Technical Discipline Lead for JSC. She is responsible for the research and development of autonomous system capabilities, on the Earth, the International Space Station, the Gateway, and for future exploration, that include dexterous manipulation, autonomous spacecraft control and caretaking, and human-robot interfaces. Julia has a BS from Purdue University, and an MS and PhD from the California Institute of Technology, all in Mechanical Engineering. Her work has been honored with several awards, including NASA Software of the Year, Early Career, Director’s Commendation, and Exceptional Achievement Awards. | |