Economic nonlinear model predictive control for intefrated and optimized non-stationary operation of biotechnological processes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Economic nonlinear model predictive control for intefrated and optimized non-stationary operation of biotechnological processes write by . This book was released on . Economic nonlinear model predictive control for intefrated and optimized non-stationary operation of biotechnological processes available in PDF, EPUB and Kindle.
Economic Model Predictive Control of Nonlinear Process Systems Using Empirical Models
Economic Model Predictive Control of Nonlinear Process Systems Using Empirical Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Economic Model Predictive Control of Nonlinear Process Systems Using Empirical Models write by Anas Wael Alanqar. This book was released on 2015. Economic Model Predictive Control of Nonlinear Process Systems Using Empirical Models available in PDF, EPUB and Kindle. 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.
Economic Model Predictive Control
Economic Model Predictive Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Economic Model Predictive Control write by Matthew Ellis. This book was released on 2016-07-27. Economic Model Predictive Control available in PDF, EPUB and Kindle. 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.
Distributed and economic model predictive control: beyond setpoint stabilization
Distributed and economic model predictive control: beyond setpoint stabilization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Distributed and economic model predictive control: beyond setpoint stabilization write by Matthias A. Müller. This book was released on 2014. Distributed and economic model predictive control: beyond setpoint stabilization available in PDF, EPUB and Kindle. In this thesis, we study model predictive control (MPC) schemes for control tasks which go beyond the classical objective of setpoint stabilization. In particular, we consider two classes of such control problems, namely distributed MPC for cooperative control in networks of multiple interconnected systems, and economic MPC, where the main focus is on the optimization of some general performance criterion which is possibly related to the economics of a system. The contributions of this thesis are to analyze various systems theoretic properties occurring in these type of control problems, and to develop distributed and economic MPC schemes with certain desired (closed-loop) guarantees. To be more precise, in the field of distributed MPC we propose different algorithms which are suitable for general cooperative control tasks in networks of interacting systems. We show that the developed distributed MPC frameworks are such that the desired cooperative goal is achieved, while coupling constraints between the systems are satisfied. Furthermore, we discuss implementation and scalability issues for the derived algorithms, as well as the necessary communication requirements between the systems. In the field of economic MPC, the contributions of this thesis are threefold. Firstly, we analyze a crucial dissipativity condition, in particular its necessity for optimal steady-state operation of a system and its robustness with respect to parameter changes. Secondly, we develop economic MPC schemes which also take average constraints into account. Thirdly, we propose an economic MPC framework with self-tuning terminal cost and a generalized terminal constraint, and we show how self-tuning update rules for the terminal weight can be derived such that desirable closed-loop performance bounds can be established.
Economic Nonlinear Model Predictive Control
Economic Nonlinear Model Predictive Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Economic Nonlinear Model Predictive Control write by Timm Faulwasser. This book was released on 2018-01-12. Economic Nonlinear Model Predictive Control available in PDF, EPUB and Kindle. 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.
