Economic Nonlinear Model Predictive Control
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Economic Nonlinear Model Predictive Control
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Author : Timm Faulwasser
language : en
Publisher: Foundations and Trends in Systems and Control
Release Date : 2018-01-12
Economic Nonlinear Model Predictive Control written by Timm Faulwasser and has been published by Foundations and Trends in Systems and Control this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-12 with Predictive control categories.
In recent years, Economic Model Predictive Control (EMPC) has received considerable attention of many research groups. The present tutorial survey summarizes state-of-the-art approaches in EMPC. In this context EMPC is to be understood as receding-horizon optimal control with a stage cost that does not simply penalize the distance to a desired equilibrium but encodes more sophisticated economic objectives. This survey provides a comprehensive overview of EMPC stability results: with and without terminal constraints, with and without dissipativity assumptions, with averaged constraints, formulations with multiple objectives and generalized terminal constraints as well as Lyapunov-based approaches.
Economic Nonlinear Model Predictive Control
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Author : Timm Faulwasser
language : en
Publisher:
Release Date : 2018
Economic Nonlinear Model Predictive Control written by Timm Faulwasser and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Electronic books categories.
In recent years, Economic Model Predictive Control (EMPC) has received considerable attention of many research groups. The present tutorial survey summarizes state-of-the-art approaches in EMPC. In this context EMPC is to be understood as receding-horizon optimal control with a stage cost that does not simply penalize the distance to a desired equilibrium but encodes more sophisticated economic objectives. This survey provides a comprehensive overview of EMPC stability results: with and without terminal constraints, with and without dissipativity assumptions, with averaged constraints, formulations with multiple objectives and generalized terminal constraints as well as Lyapunov-based approaches.
Economic Nonlinear Model Predictive Control For Intefrated And Optimized Non Stationary Operation Of Biotechnological Processes
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Author :
language : en
Publisher:
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Economic Nonlinear Model Predictive Control For Intefrated And Optimized Non Stationary Operation Of Biotechnological Processes written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Data Based Nonlinear Model Identification In Economic Model Predictive Control
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Author : Laura Giuliani
language : en
Publisher:
Release Date : 2018
Data Based Nonlinear Model Identification In Economic Model Predictive Control written by Laura Giuliani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Predictive control categories.
Many chemical/petrochemical processes in industry are not completely modeled from a first-principles perspective because of the complexity of the underlying physico-chemical phenomena and the cost of obtaining more accurate, physically relevant models. System identification methods have been utilized successfully for developing empirical, though not necessarily physical, models for advanced model-based control designs such as model predictive control (MPC) for decades. However, a fairly recent development in MPC is economic model predictive control (EMPC), which is an MPC formulated with an economics-based objective function that may operate a process in a dynamic (i.e., off steady-state) fashion, in which case the details of the process model become important for obtaining sufficiently accurate state predictions away from the steady-state, and the physics and chemistry of the process become important for developing meaningful profit-based objective functions and safety-critical constraints. Therefore, methods must be developed for obtaining physically relevant models from data for EMPC design. While the literature regarding developing models from data has rapidly expanded in recent years, many new techniques require a model structure to be assumed a priori, to which the data is then fit. However, from the perspective of developing a physically meaningful model for a chemical process, it is often not obvious what structure to assume for the model, especially considering the often complex nonlinearities characteristic of chemical processes (e.g., in reaction rate laws). In this work, we suggest that the controller itself may facilitate the identification of physically relevant models online from process operating data by forcing the process state to nonroutine operating conditions for short periods of time to obtain data that can aid in selecting model structures believed to have physical significance for the process and, subsequently, identifying their parameters. Specifically, we develop EMPC designs for which the objective function and constraints can be changed for short periods of time to obtain data to aid in model structure selection. For one of the developed designs, we incorporate Lyapunov-based stability constraints that allow closed-loop stability and recursive feasibility to be proven even as the online "experiments" are performed. This new design is applied to a chemical process example to demonstrate its potential to facilitate physics-based model identification without loss of closed-loop stability. This work therefore reverses a question that has been of interest to the control community (i.e., how new techniques for developing models from data can be useful for control of chemical processes) to ask how control may be utilized to impact the use of these techniques for the identification of physically relevant process dynamic models that can aid in improving process operation and control for economic and safety purposes.
Recent Advances In Model Predictive Control
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Author : Timm Faulwasser
language : en
Publisher: Springer Nature
Release Date : 2021-04-17
Recent Advances In Model Predictive Control written by Timm Faulwasser and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-17 with Science categories.
This book focuses on distributed and economic Model Predictive Control (MPC) with applications in different fields. MPC is one of the most successful advanced control methodologies due to the simplicity of the basic idea (measure the current state, predict and optimize the future behavior of the plant to determine an input signal, and repeat this procedure ad infinitum) and its capability to deal with constrained nonlinear multi-input multi-output systems. While the basic idea is simple, the rigorous analysis of the MPC closed loop can be quite involved. Here, distributed means that either the computation is distributed to meet real-time requirements for (very) large-scale systems or that distributed agents act autonomously while being coupled via the constraints and/or the control objective. In the latter case, communication is necessary to maintain feasibility or to recover system-wide optimal performance. The term economic refers to general control tasks and, thus, goes beyond the typically predominant control objective of set-point stabilization. Here, recently developed concepts like (strict) dissipativity of optimal control problems or turnpike properties play a crucial role. The book collects research and survey articles on recent ideas and it provides perspectives on current trends in nonlinear model predictive control. Indeed, the book is the outcome of a series of six workshops funded by the German Research Foundation (DFG) involving early-stage career scientists from different countries and from leading European industry stakeholders.
Using Nonlinear Model Predictive Control For Dynamic Decision Problems In Economics
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Author : Lars Grüne
language : en
Publisher:
Release Date : 2015
Using Nonlinear Model Predictive Control For Dynamic Decision Problems In Economics written by Lars Grüne and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
This paper presents a new approach to solve dynamic decision models in economics. The proposed procedure, called Nonlinear Model Predictive Control (NMPC), relies on the iterative solution of optimal control problems on finite time horizons and is well established in engineering applications for stabilization and tracking problems. Only quite recently, extensions to more general optimal control problems including those appearing in economic applications have been investigated. Like Dynamic Programming (DP), NMPC does not rely on linearization techniques but uses the full nonlinear model and in this sense provides a global solution to the problem. However, unlike DP, NMPC only computes one optimal trajectory at a time, thus avoids to grid the state space and for this reason the computational demand grows much more moderate than for DP. In this paper we explain the basic idea of NMPC together with some implementational details and illustrate its ability to solve dynamic decision problems in economics by means of numerical simulations for various examples, including stochastic problems, models with multiple equilibria and regime switches in the dynamics.
Economic Model Predictive Control
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Author : Matthew Ellis
language : en
Publisher: Springer
Release Date : 2016-07-27
Economic Model Predictive Control written by Matthew Ellis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-27 with Technology & Engineering categories.
This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes: Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes.In addition to being mathematically rigorous, these methods accommodate key practical issues, for example, direct optimization of process economics, time-varying economic cost functions and computational efficiency. Numerous comments and remarks providing fundamental understanding of the merging of process economics and feedback control into a single framework are included. A control engineer can easily tailor the many detailed examples of industrial relevance given within the text to a specific application. The authors present a rich collection of new research topics and references to significant recent work making Economic Model Predictive Control an important source of information and inspiration for academics and graduate students researching the area and for process engineers interested in applying its ideas.
Linear And Nonlinear Distributed Economic Model Predictive Control
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Author : Jaehwa Lee
language : en
Publisher:
Release Date : 2013
Linear And Nonlinear Distributed Economic Model Predictive Control written by Jaehwa Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Economic Model Predictive Control Of Nonlinear Process Systems Using Empirical Models
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Author : Anas Wael Alanqar
language : en
Publisher:
Release Date : 2015
Economic Model Predictive Control Of Nonlinear Process Systems Using Empirical Models written by Anas Wael Alanqar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first-principles or though system identification techniques. However, in industrial practice, it may be difficult in general to obtain an accurate first-principles model of the process. Motivated by this, in the present work, Lyapunov-based economic model predictive control (LEMPC) is designed with an empirical model that allows for closed-loop stability guarantees in the context of nonlinear chemical processes. Specifically, when the linear model provides a sufficient degree of accuracy in the region where time-varying economically optimal operation is considered, conditions for closed-loop stability under the LEMPC scheme based on the empirical model are derived. The LEMPC scheme is applied to a chemical process example to demonstrate its closed-loop stability and performance properties as well as significant computational advantages.
Nonlinear Model Based Process Control
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Author : Rashid M. Ansari
language : en
Publisher: Springer
Release Date : 2000-04-12
Nonlinear Model Based Process Control written by Rashid M. Ansari and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-04-12 with Science categories.
The work in this text entails the development of non-linear model-based multivariable control algorithms and strategies and their use in an integrated approach to control strategy, which incorporates a process model, an inferential model and a multi-variable control algorithm in one framework.