Download or read book Economic nonlinear model predictive control for intefrated and optimized non stationary operation of biotechnological processes written by and published by . This book was released on with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Economic Model Predictive Control written by Matthew Ellis and published by Springer. This book was released on 2016-07-27 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Economic Model Predictive Control of Nonlinear Process Systems Using Empirical Models written by Anas Wael Alanqar and published by . This book was released on 2015 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Economic and Distributed Model Predictive Control of Nonlinear Systems written by Mohsen Heidarinejad and published by . This book was released on 2012 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: Maximizing profit has been and will always be the primary purpose of optimal process operation. Within process control, the economic optimization considerations of a plant are usually addressed via a two-layer architecture. In general, this architecture includes: the upper layer that optimizes process operation set-points taking into account economic considerations using steady-state system models, and the lower layer (i.e., process control layer) whose primary objective is to employ feedback control systems to force the process to track the set-points. Optimizing closed-loop performance with respect to general economic considerations for nonlinear systems in a unified framework has recently become a subject of increasing theoretical interest and practical importance. In addition to a tighter integration of economics and control, advances in communication technologies have motivated augmentation of traditional point-to-point and wired local control systems with additional cheap and easy-to-install networked sensors and actuators and control systems. Networked distributed control systems can substantially improve the efficiency, flexibility, robustness and fault tolerance of an industrial control system while reducing the installation, reconfiguration and maintenance expenses at the cost of coordination and design/redesign of different control systems in the new architecture. This dissertation presents rigorous, yet practical, methods for the design of economic and distributed predictive control systems. Beginning with a review of recent results on the subject, the dissertation presents the design of Lyapunov-based economic model predictive control scheme for a broad class of nonlinear systems using state and output feedback. Then, the dissertation focuses on the development of an economic model predictive control method with guaranteed improvement in closed-loop performance compared to conventional Lyapunov-based model predictive control designs. Subsequently, the dissertation focuses on the design of a networked distributed model predictive control method for multirate uncertain systems subject to communication disruptions and measurement noise and distributed model predictive control method for switched systems to compute optimal manipulated input trajectories that achieve desired stability, performance and robustness specifications. The control methods are applied to nonlinear chemical process networks and their effectiveness and performance are evaluated through extensive computer simulations.

Download or read book Economic Nonlinear Model Predictive Control written by Timm Faulwasser and published by Foundations and Trends in Systems and Control. This book was released on 2018-01-12 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Performance and Constraint Satisfaction in Robust Economic Model Predictive Control written by Florian A. Bayer and published by Logos Verlag Berlin GmbH. This book was released on 2017 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we develop a novel framework for model predictive control (MPC) which combines the concepts of robust MPC and economic MPC. The goal of this thesis is to develop and analyze MPC schemes for nonlinear discrete-time systems which explicitly consider the influence of disturbances on arbitrary performance criteria. Instead of regarding the two aspects separately, we propose robust economic MPC approaches that integrate information which is available about the disturbance directly into the economic framework. In more detail, we develop three concepts which differ in which information about the disturbance is used and how this information is taken into account. Furthermore, we provide a thorough theoretical analysis for each of the three approaches. To this end, we present results on the asymptotic average performance as well as on optimal operating regimes. Optimal operating regimes are closely related to the notion of dissipativity, which is therefore analyzed for the presented concepts. Under suitable assumptions, results on necessity and sufficiency of dissipativity for optimal steady-state operation are established for all three robust economic MPC concepts. A detailed discussion is provided which compares the different performance statements derived for the approaches as well as the respective notions of dissipativity.

Download or read book Optimal Process Operation by Using Economics Optimizing Nonlinear Model Predictive Control written by Elrashid Abdelrahman Noureldin Idris and published by . This book was released on 2014-03-25 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Economic Model Predictive Control of Chemical Processes written by Omar Santander and published by . This book was released on 2015 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of any chemical process is to transform raw materials into more valuable products subject to not only physical and environmental but also economic and safety constraints. To meet all these constraints in the presence of disturbances the processes must be controlled. Although nowadays there are many available control techniques available Model Predictive Control (MPC) is widely used in industry due to its many advantages such as optimal handling of interactions in multivariable systems and process constraints. Generally, the MPC strategy is implemented within a hierarchical structure, where it receives set points or targets from the Real Time Optimization (RTO) layer and then maintains the process at these targets by calculating optimal control moves. However, often the set point from the RTO may not be the best optimal operation or it may not be reachable thus motivating the integration of the RTO and MPC calculations into one single computation layer. This work focuses on this idea of integrating RTO and MPC into one single optimization problem thus resulting in an approach referred in literature as Economic Model Predictive Control (EMPC). The term “Economic” is used to reflect that the objective function used for optimization includes an economic objective generally used in RTO calculations. In this thesis, we propose an EMPC algorithm which calculates manipulated variables values to optimize an objective consisting of a combination of a steady state and a dynamic economic cost. A weight factor is used to balance the contributions of each of these two terms. Also, the cost is defined such as when the best economic steady state is reached the objective is only influenced by the dynamic economic cost. An additional feature of the proposed algorithm is that the asymptotic stability is satisfied online by enforcing four especial constraints within the optimization problem: 1-positive definiteness of the matrix P defining the Lyapunov function, 2- contraction of the Lyapunov function with respect to set point changes, 3- contraction of the matrix P with respect to time and 4- Lyapunov stability condition. The last constraint both ensures decreasing of the Lyapunov function and also accounts for the robustness of the algorithm with respect to model error (uncertainty). A particular novelty of this algorithm is that it constantly calculates a best set point with respect to which stability is ensured by the aforementioned constraints. In contrast to other algorithms reported in the literature, the proposed algorithm does not require terminal constraints or terms in the cost that penalize deviations from fixed set points that often lead to conservative closed loop performance. To account for unmeasured disturbances entering the process, changes in parameters are also explored and the algorithm is devised to compensate for these changes through parameter updating. Accordingly, the parameters are included as additional decision variables within the optimization problem without the need for an external observer. The stability of the parameters estimation is ensured through the set point constraint mentioned above. To demonstrate the capabilities of the proposed algorithm, it is tested on two case studies: a simpler one involving a system of 4 nonlinear ODEs describing an isothermal nonlinear reactor and a larger problem involving a non-isothermal Williams-Otto reactor with parallel reactions. The dynamics of the latter reactor consists of a set of nonlinear ODE describing the evolution of the process temperature and concentration of the different species. The simulations for the isothermal reactor showed that the proposed algorithm not only outperformed (in terms of an economic function) alternative formulations, but addressed all their limitations. In addition, when there was a parameter modification, this was adapted in a finite time. In terms of the non-isothermal reactor, the simulations demonstrated that not only the best steady state could be computed, but also the states were steered to it satisfying the online stability property.

Download or read book Distributed and economic model predictive control beyond setpoint stabilization written by Matthias A. Müller and published by Logos Verlag Berlin GmbH. This book was released on 2014 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Economic Nonlinear Model Predictive Control written by Timm Faulwasser and published by . This book was released on 2018 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Economic Model Predictive Control Theory Computational Efficiency and Application to Smart Manufacturing written by Matthew Ellis and published by . This book was released on 2015 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: The chemical industry is a vital sector of the US economy. Maintaining optimal chemical process operation is critical to the future success of the US chemical industry on a global market. Traditionally, economic optimization of chemical processes has been addressed in a two-layer hierarchical architecture. In the upper layer, real-time optimization carries out economic process optimization by computing optimal process operation set-points using detailed nonlinear steady-state process models. These set-points are used by the lower layer feedback control systems to force the process to operate on these set-points. While this paradigm has been successful, we are witnessing an increasing need for dynamic market and demand-driven operations for more efficient process operation, increasing response capability to changing customer demand, and achieving real-time energy management. To enable next-generation market-driven operation, economic model predictive control (EMPC), which is an model predictive control scheme formulated with a stage cost that represents the process economics, has been proposed to integrate dynamic economic optimization of processes with feedback control. Motivated by these considerations, novel theory and methods needed for the design of computationally tractable economic model predictive control systems for nonlinear processes are developed in this dissertation. Specifically, the following considerations are addressed: a) EMPC structures for nonlinear systems which address: infinite-time and finite-time closed-loop economic performance and time-varying economic considerations such as changing energy pricing; b) two-layer (hierarchical) dynamic economic process optimization and feedback control frameworks that incorporate EMPC with other control strategies allowing for computational efficiency; and c) EMPC schemes that account for real-time computation requirements. The EMPC schemes and methodologies are applied to chemical process applications. The application studies demonstrate the effectiveness of the EMPC schemes to maintain process stability and improve economic performance under dynamic operation as well as to increase efficiency, reliability and profitability of processes, thereby contributing to the vision of Smart Manufacturing.

Download or read book Economic Model Predictive Control Using Data Based Empirical Models written by ANAS W. I. ALANQAR and published by . This book was released on 2017 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasingly competitive and continuously changing world economy has made it necessary to exploit the economic potential of chemical processes which has led engineers to economically optimize process operation to provide long-term economic growth. Approaches for increasing the profitability of industrial processes include directly incorporating process economic considerations into the system's operation and control policy. A fairly recent control strategy, termed economic model predictive control (EMPC), is capable of coordinating dynamic economic plant optimization with a feedback control policy to allow real-time energy management. The key underlying assumption to design and apply an EMPC is that a rocess/system dynamic model is available to predict the future process state evolution. Constructing models of dynamical systems is done either through first-principles and/or from process input/output data. First-principle models attempt to account for the essential mechanisms behind the observed physico-chemical phenomena. However, arriving at a first-principles model may be a challenging task for complex and/or poorly understood processes in which system identification serves as a suitable alternative. Motivated by this, the first part of my doctoral research has focused on introducing novel economic model predictive control schemes that are designed utilizing models obtained from advanced system identification methods. Various system identification schemes were investigated in the EMPC designs including linear modeling, multiple models, and on-line model identification. On-line model identification is used to obtain more accurate models when the linear empirical models are not capable of capturing the nonlinear dynamics as a result of significant plant disturbances and variations, actuator faults, or when it is desired to change the region of operation. An error-triggered on-line model identification approach is introduced where a moving horizon error detector is used to quantify prediction error and trigger model re-identification when necessary. The proposed EMPC schemes presented great economic benefit, precise predictions, and significant computational time reduction. These benefits indicate the effectiveness of the proposed EMPC schemes in practical industrial applications. The second part of the dissertation focuses on EMPC that utilizes well-conditioned polynomial nonlinear state-space (PNLSS) models for processes with nonlinear dynamics. A nonlinear system identification technique is introduced for a broad class of nonlinear processes which leads to the construction of polynomial nonlinear state-space dynamic models which are well-conditioned with respect to explicit numerical integration methods. This development allows using time steps that are significantly larger than the ones required by nonlinear state-space models identified via existing techniques. Finally, the dissertation concludes by investigating the use of EMPC in tracking a production schedule. Specifically, given that only a small subset of the total process state vector is typically required to track certain production schedules, a novel EMPC is introduced scheme that forces specific process states to meet the production schedule and varies the rest of the process states in a way that optimizes process economic performance

Download or read book Integrated Real time Optimization and Model Predictive Control Under Parametric Uncertainties written by Veronica Aderonke Adetola and published by . This book was released on 2008 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: The actualization of real-time economically optimal process operation requires proper integration of real-time optimization (RTO) and dynamic control. This dissertation addresses the integration problem and provides a formal design technique that properly integrates RTO and model predictive control (MPC) under parametric uncertainties. The task is posed as an adaptive extremum-seeking control (ESC) problem in which the controller is required to steer the system to an unknown setpoint that optimizes a user-specified objective function. The integration task is first solved for linear uncertain systems. Then a method of determining appropriate excitation conditions for nonlinear systems with uncertain reference setpoint is provided. Since the identification of the true cost surface is paramount to the success of the integration scheme, novel parameter estimation techniques with better convergence properties are developed. The estimation routine allows exact reconstruction of the system's unknown parameters in finite-time. The applicability of the identifier to improve upon the performance of existing adaptive controllers is demonstrated. Adaptive nonlinear model predictive controllers are developed for a class of constrained uncertain nonlinear systems. Rather than relying on the inherent robustness of nominal MPC, robustness features are incorporated in the MPC framework to account for the effect of the model uncertainty. The numerical complexity and/or the conservatism of the resulting adaptive controller reduces as more information becomes available and a better uncertainty description is obtained. Finally, the finite-time identification procedure and the adaptive MPC are combined to achieve the integration task. The proposed design solves the economic optimization and control problem at the same frequency. This eliminates the ensuing interval of "no-feedback" that occurs between economic optimization interval, thereby improving disturbance attenuation.

Download or read book Nonlinear Model Predictive Control of Processes with Incomplete State Measurements written by Mohammed Alhajeri and published by . This book was released on 2018 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of two main parts. The first part provides a comprehensive review of the tuning guidelines that have been introduced for model predictive controllers (MPCs) since 2010. The second part deals with nonlinear control of single-input single-output processes with manipulated-input-saturation constraints, incomplete state measurements, and an unstable steady-state operating point. A nonlinear controller is proposed. It includes an input-output linearizing state feedback controller, which is also an analytical solution to a shortest prediction horizon, continuous-time MPC optimization problem. It uses a closed-loop nonlinear reduced-order state observer to estimate unmeasured state variables. As it handles input constraints optimally, it exhibits no integrator windup. Given the closed-loop stability of the control system, it guarantees zero steady-state error (offset) in the presence of constant process disturbances and process-model mismatch. Its application and performance are illustrated by applying the controller to two nonlinear chemical process examples, a chemical reactor and a bioreactor, via numerical simulations.

Download or read book Using Nonlinear Model Predictive Control for Dynamic Decision Problems in Economics written by Lars Grüne and published by . This book was released on 2015 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Nonlinear Model Predictive Control and Dynamic Real Time Optimization for Large scale Processes written by Rui Huang and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Model Predictive Control and Optimal Operation of Continuous and Batch Nonlinear Processes written by Gangadhar Gattu and published by . This book was released on 1994 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: