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460
Constrained model predictive control: Stability and optimality
 AUTOMATICA
, 2000
"... Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and t ..."
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Cited by 738 (16 self)
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Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and the first control in this sequence is applied to the plant. An important advantage of this type of control is its ability to cope with hard constraints on controls and states. It has, therefore, been widely applied in petrochemical and related industries where satisfaction of constraints is particularly important because efficiency demands operating points on or close to the boundary of the set of admissible states and controls. In this review, we focus on model predictive control of constrained systems, both linear and nonlinear and discuss only briefly model predictive control of unconstrained nonlinear and/or timevarying systems. We concentrate our attention on research dealing with stability and optimality; in these areas the subject has developed, in our opinion, to a stage where it has achieved sufficient maturity to warrant the active interest of researchers in nonlinear control. We distill from an extensive literature essential principles that ensure stability and use these to present a concise characterization of most of the model predictive controllers that have been proposed in the literature. In some cases the finite horizon optimal control problem solved online is exactly equivalent to the same problem with an infinite horizon; in other cases it is equivalent to a modified infinite horizon optimal control problem. In both situations, known advantages of infinite horizon optimal control accrue.
Control of Systems Integrating Logic, Dynamics, and Constraints
 Automatica
, 1998
"... This paper proposes a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operating constraints, denoted as Mixed Logical Dynamical (MLD) systems. These are described by linear dynamic equations subject to linear inequalities involving real and ..."
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Cited by 413 (50 self)
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This paper proposes a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operating constraints, denoted as Mixed Logical Dynamical (MLD) systems. These are described by linear dynamic equations subject to linear inequalities involving real and integer variables. MLD systems include constrained linear systems, finite state machines, some classes of discrete event systems, and nonlinear systems which can be approximated by piecewise linear functions. A predictive control scheme is proposed which is able to stabilize MLD systems on desired reference trajectories while fulfilling operating constraints, and possibly take into account previous qualitative knowledge in the form of heuristic rules. Due to the presence of integer variables, the resulting online optimization procedures are solved through Mixed Integer Quadratic Programming (MIQP), for which e#cient solvers have been recently developed. Some examples and a simulation case s...
Fast Model Predictive Control Using Online Optimization
, 2008
"... A widely recognized shortcoming of model predictive control (MPC) is that it can usually only be used in applications with slow dynamics, where the sample time is measured in seconds or minutes. A well known technique for implementing fast MPC is to compute the entire control law offline, in which c ..."
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Cited by 120 (24 self)
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A widely recognized shortcoming of model predictive control (MPC) is that it can usually only be used in applications with slow dynamics, where the sample time is measured in seconds or minutes. A well known technique for implementing fast MPC is to compute the entire control law offline, in which case the online controller can be implemented as a lookup table. This method works well for systems with small state and input dimensions (say, no more than 5), and short time horizons. In this paper we describe a collection of methods for improving the speed of MPC, using online optimization. These custom methods, which exploit the particular structure of the MPC problem, can compute the control action on the order of 100 times faster than a method that uses a generic optimizer. As an example, our method computes the control actions for a problem with 12 states, 3 controls, and horizon of 30 time steps (which entails solving a quadratic program with 450 variables and 1260 constraints) in around 5msec, allowing MPC to be carried out at 200Hz. 1
Multiagent model predictive control for transportation networks: Serial versus parallel schemes.
 Engineering Applications of Artificial Intelligence,
, 2008
"... AbstractWe consider multiagent, or distributed, control of transportation networks, like traffic, water, and power networks. These networks typically have a large geographical span, modular structure, and a large number of components that require control. We discuss the necessity of a multiagent ..."
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Cited by 61 (29 self)
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AbstractWe consider multiagent, or distributed, control of transportation networks, like traffic, water, and power networks. These networks typically have a large geographical span, modular structure, and a large number of components that require control. We discuss the necessity of a multiagent control setting in which multiple agents control parts of the network. As potential control methodology we consider Model Predictive Control (MPC) in a multiagent setting. We first outline a framework for modeling transportation networks into subsystems using external variables and then discuss issues that arise when controlling these networks with multiagent MPC. Several approaches to these issues are structured and discussed in terms of the outlined framework.
An Overview of Nonlinear Model Predictive Control Applications
 Nonlinear Predictive Control
, 2000
"... . This paper provides an overview of nonlinear model predictive control (NMPC) applications in industry, focusing primarily on recent applications reported by NMPC vendors. A brief summary of NMPC theory is presented to highlight issues pertinent to NMPC applications. Five industrial NMPC implem ..."
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Cited by 60 (1 self)
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. This paper provides an overview of nonlinear model predictive control (NMPC) applications in industry, focusing primarily on recent applications reported by NMPC vendors. A brief summary of NMPC theory is presented to highlight issues pertinent to NMPC applications. Five industrial NMPC implementations are then discussed with reference to modeling, control, optimization, and implementation issues. Results from several industrial applications are presented to illustrate the benefits possible with NMPC technology. A discussion of future needs in NMPC theory and practice is provided to conclude the paper. 1. Introduction The term Model Predictive Control (MPC) describes a class of computer control algorithms that control the future behavior of a plant through the use of an explicit process model. At each control interval the MPC algorithm computes an openloop sequence of manipulated variable adjustments in order to optimize future plant behavior. The first input in the optima...
An Introduction to Nonlinear Model Predictive Control
 21ST BENELUX MEETING ON SYSTEMS AND CONTROL, 2002, VELDHOVEN
, 2002
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A tutorial on linear and bilinear matrix inequalities
, 2000
"... This is a tutorial on the mathematical theory and process control applications of linear matrix inequalities (LMIs) and bilinear matrix inequalities (BMIs). Many convex inequalities common in process control applications are shown to be LMIs. Proofs are included to familiarize the reader with the ma ..."
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Cited by 44 (0 self)
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This is a tutorial on the mathematical theory and process control applications of linear matrix inequalities (LMIs) and bilinear matrix inequalities (BMIs). Many convex inequalities common in process control applications are shown to be LMIs. Proofs are included to familiarize the reader with the mathematics of LMIs and BMIs. LMIs and BMIs are applied to several important process control applications including control structure selection, robust controller analysis and design, and optimal design of experiments.
Increasing Customer Value of Industrial Control Performance Monitoring— Honeywell Experience
 6th International Conference Chemical Process Control, AIChE Symp., Series 326
, 2002
"... Within the process industries there is a significant installed base of regulatory and multivariable model predictive controllers. These controllers in many cases operate very poorly. This paper documents the current state of industrial controller performance, identifies the sources and ramificatio ..."
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Cited by 38 (0 self)
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Within the process industries there is a significant installed base of regulatory and multivariable model predictive controllers. These controllers in many cases operate very poorly. This paper documents the current state of industrial controller performance, identifies the sources and ramifications of this poor performance, and discusses required attributes of a Process Control Monitoring System (PCMS). Finally, research directions are suggested.
State and output feedback nonlinear model predictive control: An overview
 EUROPEAN JOURNAL OF CONTROL
, 2003
"... The purpose of this paper is twofold. In the first part we give a review on the current state of nonlinear model predictive control (NMPC). After a brief presentation of the basic principle of predictive control we outline some of the theoretical, computational, and implementational aspects of this ..."
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Cited by 37 (2 self)
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The purpose of this paper is twofold. In the first part we give a review on the current state of nonlinear model predictive control (NMPC). After a brief presentation of the basic principle of predictive control we outline some of the theoretical, computational, and implementational aspects of this control strategy. Most of the theoretical developments in the area of NMPC are based on the assumption that the full state is available for measurement, an assumption that does not hold in the typical practical case. Thus, in the second part of this paper we focus on the output feedback problem in NMPC. After a brief overview on existing output feedback NMPC approaches we derive conditions that guarantee stability of the closedloop if an NMPC state feedback controller is used together with a full state observer for the recovery of the system state.
Online Control for SelfManagement in Computing Systems
 In Proc. of 10th IEEE RealTime and Embedded Technology and Applications Symposium
, 2004
"... Local Local Control input State Meas. ..."
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