The Experts below are selected from a list of 185070 Experts worldwide ranked by ideXlab platform
D.q. Mayne - One of the best experts on this subject based on the ideXlab platform.
-
model Predictive Control
Automatica, 2014Co-Authors: D.q. MayneAbstract:This paper recalls a few past achievements in Model Predictive Control, gives an overview of some current developments and suggests a few avenues for future research.
-
robust model Predictive Control using tubes
Automatica, 2004Co-Authors: W Langson, Sasa V. Rakovic, I Chryssochoos, D.q. MayneAbstract:A form of feedback model Predictive Control (MPC) that overcomes disadvantages of conventional MPC but which has manageable computational complexity is presented. The optimal Control problem, solved on-line, yields a 'tube' and an associated piecewise affine Control law that maintains the Controlled trajectories in the tube despite uncertainty; computational complexity is linear (rather than exponential) in horizon length. Asymptotic stability of the Controlled system is established.
-
model Predictive Control of constrained piecewise affine discrete time systems
International Journal of Robust and Nonlinear Control, 2003Co-Authors: D.q. Mayne, Sasa V. RakovicAbstract:The paper deals with model Predictive Control of a class of non-smooth nonlinear systems, namely piecewise affine systems. A novel procedure for solving the associated optimal Control problem is presented and its convergence established. Stability properties of the closed-loop system using model Predictive Control with the new algorithm are derived. Examples illustrate the proposed algorithm for determining optimal Control of constrained piecewise affine systems and the performance of the closed-loop system. Copyright © 2003 John Wiley & Sons, Ltd.
-
suboptimal model Predictive Control feasibility implies stability
IEEE Transactions on Automatic Control, 1999Co-Authors: P O M Scokaert, D.q. Mayne, James B. RawlingsAbstract:Practical difficulties involved in implementing stabilizing model Predictive Control laws for nonlinear systems are well known. Stabilizing formulations of the method normally rely on the assumption that global and exact solutions of nonconvex, nonlinear optimization problems are possible in limited computational time. In the paper, we first establish conditions under which suboptimal model Predictive Control (MPC) Controllers are stabilizing; the conditions are mild holding out the hope that many existing Controllers remain stabilizing even if optimality is lost. Second, we present and analyze two suboptimal MPC schemes that are guaranteed to be stabilizing, provided an initial feasible solution is available and for which the computational requirements are more reasonable.
-
min max feedback model Predictive Control for constrained linear systems
IEEE Transactions on Automatic Control, 1998Co-Authors: Pierre O M Scokaert, D.q. MayneAbstract:Min-max feedback formulations of model Predictive Control are discussed, both in the fixed and variable horizon contexts. The Control schemes the authors discuss introduce, in the Control optimization, the notion that feedback is present in the receding-horizon implementation of the Control. This leads to improved performance, compared to standard model Predictive Control, and resolves the feasibility difficulties that arise with the min-max techniques that are documented in the literature. The stabilizing properties of the methods are discussed as well as some practical implementation details.
Panagiotis D Christofides - One of the best experts on this subject based on the ideXlab platform.
-
networked and distributed Predictive Control methods and nonlinear process network applications
2011Co-Authors: Panagiotis D Christofides, Jinfeng Liu, David Muoz De La PeaAbstract:Networked and Distributed Predictive Control presents rigorous, yet practical, methods for the design of networked and distributed Predictive Control systems the first book to do so. The design of model Predictive Control systems using Lyapunov-based techniques accounting for the influence of asynchronous and delayed measurements is followed by a treatment of networked Control architecture development. This shows how networked Control can augment dedicated Control systems in a natural way and takes advantage of additional, potentially asynchronous and delayed measurements to maintain closed loop stability and significantly to improve closed-loop performance. The text then shifts focus to the design of distributed Predictive Control systems that cooperate efficiently in computing optimal manipulated input trajectories that achieve desired stability, performance and robustness specifications but spend a fraction of the time required by centralized Control systems. Key features of this book include: new techniques for networked and distributed Control system design; insight into issues associated with networked and distributed Predictive Control and their solution; detailed appraisal of industrial relevance using computer simulation of nonlinear chemical process networks and wind- and solar-energy-generation systems; and integrated exposition of novel research topics and rich resource of references to significant recent work. A full understanding of Networked and Distributed Predictive Control requires a basic knowledge of differential equations, linear and nonlinear Control theory and optimization methods and the book is intended for academic researchers and graduate students studying Control and for process Control engineers. The constant attention to practical matters associated with implementation of the theory discussed will help each of these groups understand the application of the books methods in greater depth.
-
supervisory Predictive Control of standalone wind solar energy generation systems
IEEE Transactions on Control Systems and Technology, 2011Co-Authors: Jinfeng Liu, Xianzhong Chen, Panagiotis D ChristofidesAbstract:This work focuses on the development of a supervisory model Predictive Control method for the optimal management and operation of hybrid standalone wind-solar energy generation systems. We design the supervisory Control system via model Predictive Control which computes the power references for the wind and solar subsystems at each sampling time while minimizing a suitable cost function. The power references are sent to two local Controllers which drive the two subsystems to the requested power references. We discuss how to incorporate practical considerations, for example, how to extend the life time of the equipment by reducing the peak values of inrush or surge currents, into the formulation of the model Predictive Control optimization problem. We present several simulation case studies that demonstrate the applicability and effectiveness of the proposed supervisory Predictive Control architecture.
-
distributed model Predictive Control of nonlinear process systems
Aiche Journal, 2009Co-Authors: David Muñoz De La Peña, Panagiotis D ChristofidesAbstract:This work focuses on a class of nonlinear Control problems that arise when new Control systems which may use networked sensors and/or actuators are added to already operating Control loops to improve closed-loop performance. In this case, it is desirable to design the pre-existing Control system and the new Control system in a way such that they coordinate their actions. To address this Control problem, a distributed model Predictive Control method is introduced where both the pre-existing Control system and the new Control system are designed via Lyapunov-based model Predictive Control. Working with general nonlinear models of chemical processes and assuming that there exists a Lyapunov-based Controller that stabilizes the nominal closed-loop system using only the pre-existing Control loops, two separate Lyapunov-based model Predictive Controllers are designed that coordinate their actions in an efficient fashion. Specifically, the proposed distributed model Predictive Control design preserves the stability properties of the Lyapunov-based Controller, improves the closed-loop performance, and allows handling input constraints. In addition, the proposed distributed Control design requires reduced communication between the two distributed Controllers since it requires that these Controllers communicate only once at each sampling time and is computationally more efficient compared to the corresponding centralized model Predictive Control design. The theoretical results are illustrated using a chemical process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009
-
Predictive Control of parabolic PDEs with boundary Control actuation
Chemical Engineering Science, 2006Co-Authors: Stevan Dubljevic, Panagiotis D ChristofidesAbstract:This work focuses on Predictive Control of linear parabolic partial differential equations (PDEs) with boundary Control actuation subject to input and state constraints. Under the assumption that measurements of the PDE state are available, various finite-dimensional and infinite-dimensional Predictive Control formulations are presented and their ability to enforce stability and constraint satisfaction in the infinite-dimensional closed-loop system is analyzed. A numerical example of a linear parabolic PDE with unstable steady state and flux boundary Control subject to state and Control constraints is used to demonstrate the implementation and effectiveness of the Predictive Controllers.
-
Predictive Control of particle size distribution in particulate processes
Chemical Engineering Science, 2006Co-Authors: Nael H Elfarra, Mingheng Li, Prashant Mhaskar, Panagiotis D ChristofidesAbstract:In this work, we focus on the development and application of Predictive-based strategies for Control of particle size distribution (PSD) in continuous and batch particulate processes described by population balance models (PBMs). The Control algorithms are designed on the basis of reduced-order models, utilize measurements of principle moments of the PSD, and are tailored to address different Control objectives for the continuous and batch processes. For continuous particulate processes, we develop a hybrid Predictive Control strategy to stabilize a continuous crystallizer at an open-loop unstable steady-state. The hybrid Predictive Control strategy employs logic-based switching between model Predictive Control (MPC) and a fall-back bounded Controller with a well-defined stability region. The strategy is shown to provide a safety net for the implementation of MPC algorithms with guaranteed stability closed-loop region. For batch particulate processes, the Control objective is to achieve a final PSD with desired characteristics subject to both manipulated input and product quality constraints. An optimization-based Predictive Control strategy that incorporates these constraints explicitly in the Controlle r design is formulated and applied to a seeded batch crystallizer. The strategy is shown to be able to reduce the total volume of the fines by 13.4% compared to a linear cooling strategy, and is shown to be robust with respect to modeling errors. 2005 Elsevier Ltd. All rights reserved.
Manfred Morari - One of the best experts on this subject based on the ideXlab platform.
-
Predictive Control for linear and hybrid systems
2017Co-Authors: Francesco Borrelli, Alberto Bemporad, Manfred MorariAbstract:Model Predictive Control (MPC), the dominant advanced Control approach in industry over the past twenty-five years, is presented comprehensively in this unique book. With a simple, unified approach, and with attention to real-time implementation, it covers Predictive Control theory including the stability, feasibility, and robustness of MPC Controllers. The theory of explicit MPC, where the nonlinear optimal feedback Controller can be calculated efficiently, is presented in the context of linear systems with linear constraints, switched linear systems, and, more generally, linear hybrid systems. Drawing upon years of practical experience and using numerous examples and illustrative applications, the authors discuss the techniques required to design Predictive Control laws, including algorithms for polyhedral manipulations, mathematical and multiparametric programming and how to validate the theoretical properties and to implement Predictive Control policies. The most important algorithms feature in an accompanying free online MATLAB toolbox, which allows easy access to sample solutions. Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced Control practitioners interested in theory and/or implementation aspects of Predictive Control.
-
nonlinear offset free model Predictive Control
Automatica, 2012Co-Authors: Manfred Morari, Urs MaederAbstract:This paper addresses offset-free reference tracking of asymptotically constant reference signals using Model Predictive Control. Existing results for linear models are extended to general nonlinear models. The core of the proposed method employs a disturbance model and an observer to estimate its state. Typical disturbance models are shown and the implications of using them are discussed. Conditions are given for which this setup eliminates the tracking error asymptotically. Basically, we prove that error free output estimation and error free nominal tracking imply offset-free Model Predictive Control.
-
robust model Predictive Control a survey
Lecture Notes in Control and Information Sciences, 1999Co-Authors: Alberto Bemporad, Manfred MorariAbstract:This paper gives an overview of robustness in Model Predictive Control (MPC). After reviewing the basic concepts of MPC, we survey the uncertainty descriptions considered in the MPC literature, and the techniques proposed for robust constraint handling, stability, and performance. The key concept of “closedloop prediction” is discussed at length. The paper concludes with some comments on future research directions.
-
state space interpretation of model Predictive Control
Automatica, 1994Co-Authors: Jay H Lee, Manfred Morari, Carlos E GarciaAbstract:A model Predictive Control technique based on a step response model is developed using state estimation techniques. The standard step response model is extended so that integrating systems can be treated within the same framework. Based on the modified step response model, it is shown how the state estimation techniques from stochastic optimal Control can be used to construct the optimal prediction vector without introducing significant additional numerical complexity. In the case of integrated or double integrated white noise disturbances filtered through general first-order dynamics and white measurement noise, the optimal filter gain is parametrized explicitly in terms of a single parameter between 0 and 1, thus removing the requirement for solving a Riccati equation and equipping the Control system with useful on-line tuning parameters. Parallels are drawn to the existing MPC techniques such as Dynamic Matrix Control (DMC), Internal Model Control (IMC) and Generalized Predictive Control (GPC).
J A Rossiter - One of the best experts on this subject based on the ideXlab platform.
-
model based Predictive Control a practical approach
2017Co-Authors: J A RossiterAbstract:Introduction Common Linear Models Used in Model Predictive Control Prediction in Model Predictive Control Predictive Control-The Basic Algorithm Examples - Tuning Predictive Control and Numerical Conditioning Stability Guarantees and Optimising Performance Closed-Loop Paradigm Constraint Handling and Feasibility Issues in MPC Improving Robustness-The Constraint Free Case The Relationship Between Modelling and the Robustness of MPC Robustness of MPC During Constraint Handling and Invariant Sets Optimisation and Computational Efficiency in Predictive Control Predictive Functional Control Multirate Systems Modelling for Predictive Control Appendices Conclusion
-
a numerically robust state space approach to stable Predictive Control strategies
Automatica, 1998Co-Authors: J A Rossiter, B. Kouvaritakis, M J RiceAbstract:Recent work with Predictive Control strategies has shown that the stable GPC (SGPC) approach has significant computational and numerical advantages. However, SGPC is cast in the transfer function framework which limits its application. Here we develop a means of extending the results to state-space models and show that improved numerical conditioning can be obtained for many stable-Predictive Control strategies.
Eva žacekova - One of the best experts on this subject based on the ideXlab platform.
-
Building modeling as a crucial part for building Predictive Control
Energy and Buildings, 2013Co-Authors: Samuel Privara, Carina Sagerschnig, Frauke Oldewurtel, Zdeněk Krňoul, Jiří Cigler, Eva žacekovaAbstract:Recent results show that a Predictive building automation can be used to operate buildings in an energy and cost effective manner with only a small retrofitting requirements. In this approach, the dynamic models are of crucial importance. As industrial experience has shown, modeling is the most time-demanding and costly part of the automation process. Many papers devoted to this topic actually deal with modeling of building subsystems. Although some papers identify a building as a complex system, the provided models are usually simple two-zones models, or extremely detailed models resulting from the use of building simulation software packages. These are, however, not suitable for Predictive Control. The objective of this paper is to share the years-long experience of the authors in building modeling intended for Predictive Control of the building's climate. We provide an overview of identification methods for buildings and analyze their applicability for subsequent Predictive Control. Moreover, we propose a new methodology to obtain a model suitable for the use in a Predictive Control framework combining the building energy performance simulation tools and statistical identification. The procedure is based on the so-called co-simulation that has appeared recently as a feature of various building simulation software packages.
