
	Where results make sense

Topic: Model predictive control

Related Topics

Transmission Control Protocol

mind control

birth control

air traffic control

Centers for Disease Control and Prevention

control theory

gun control

quality control

Star Control

remote control

social control

control flow

In the News (Wed 15 Feb 12)

EXECUTIVE POWER

Iran

ANALYSIS

Pakistan

Family compact

	PAControl.com - MPC Model Predictive Control (Site not responding. Last check: 2007-11-06)
		MPC is widely adopted in the process industry as an effective means to deal with large multivariable constrained control problems.
		MPC has been used in industry for more than 30 years, and has become an industry standard (mainly in the petrochemical industry) due to its intrinsic capability for dealing with constraints and with multivariable systems.
		Statistical models are used not only to define control limits, but also to develop control laws that suggest the degree of manipulation to maintain the process under statistical control.
	www.pacontrol.com /MPC.html (737 words)

	Model Predictive Control
		Model Predictive Control (MPC) is widely adopted in industry as an effective means to deal with large multivariable constrained control problems.
		The main idea of MPC is to choose the control action by repeatedly solving on line an optimal control problem.
		Bemporad and M. Morari, ``Robust model predictive control: A survey,'' in Robustness in Identification and Control, A. Garulli, A. Tesi, and A. Vicino, Eds., number 245 in Lecture Notes in Control and Information Sciences, pp.
	www.dii.unisi.it /~bemporad/mpc.html (444 words)

	Control and System Dynamics (Site not responding. Last check: 2007-11-06)
		The controller, which adjusts the supply water temperature so as to maintain an estimate of the average air temperature in the building at a specified set-point, is commissioned by installing wireless sensors in the zones of the building for a short period of time during the heating season.
		Predictive control for linear systems is now a well-established discipline providing a range of techniques with guaranteed stability, feasibility and robustness.
		Predictive control is well developed in terms of handling constraints and bounded uncertainty but there is currently no framework addressing problems involving stochastic objectives and stochastic constraints.
	www.eng.ox.ac.uk /World/Research/Summary/B-Control.html (1626 words)

	LPT: Nonlinear Model Predictive Control of Batch Reactors
		Nonlinear Model Predictive Control (MPC) is an optimization-based multivariable constrained control technique using a nonlinear dynamic process model for the prediction of the process outputs.
		MPC provides a methodology to handle constraints of manipulated as well as controlled variables in a systematic way and is not limited to a certain model structure.
		Therefore, the predictive controller should be designed such that the generation of an optimal temperature profile and feed policy as well as the attenuation of disturbances is achieved in an integrated manner.
	www.lpt.rwth-aachen.de /Research/Completed/nmpc.php (1255 words)

	Real-Time Model Predictive Control for Structural Engineering
		The MPC scheme is based on an explicit use of a prediction model of the system response to obtain the control action by minimizing an objective function.
		MPC offers a general framework of posing the control problem in the time domain and can be used to integrate issues of the optimal control, stochastic control, control of processes with time delays and multivariable control.
		This includes minimization of the difference between the predicted and the target response and minimization of the control effort needed to reach this objective subjected to certain constraints, such as limits on the magnitude of the control force.
	www.nd.edu /~nathaz/research/Gang.htm (3131 words)

	Model Predictive Control Research at CSC, UMIST (Site not responding. Last check: 2007-11-06)
		Model predictive control (MPC) is already widely used in the process industries.
		An important advantage of MPC is its ability to handle input and state constraints for large scale multivariable plants.
		David Sandoz, founder of Predictive Control Ltd. and more recently Perceptive Engineering Ltd., is a visiting Professor at the Centre.
	www.csc.umist.ac.uk /research/modelpredictive (378 words)

	Control Engineering - Model Predictive Control gets a helping hand (Site not responding. Last check: 2007-11-06)
		The fundamental principle of Model Predictive Control (MPC) is that a model is used to predict future process behaviour and the controller computes actuator moves using this model over a future horizon.
		All of this controller actuation is unnecessary as the MPC is responding to a sensor fault—not a genuine process disturbance—resulting in a move away from the optimum and a measurable economic loss.
		Overall the benefits are seen by improved controller operation and increased service factor, brought about by improved sensor integrity, automatic controller re-tuning in-line with changing process conditions, and a greater amount of meaningful information provided to process operators about the actual state of the process beneath the controller.
	www.manufacturing.net /CTL/article/CA486477.html (1819 words)

	Nonlinear Model Predictive Control
		Model Predictive Control (MPC) is a methodology that refers to a class of control algorithms in which a dynamic model of the plant is used to predict and optimize the future behaviour of the process.
		This has motivated the development of Nonlinear Model Predictive Control (NMPC), where a more accurate (nonlinear) model of the plant is used for prediction and optimization (see for instance [2],[3] for the state of the art and future directions on NMPC).
		Some works where this approach has been followed are for instance: [6] where a nonlinear predictive control scheme based on radial basis functions models is proposed, and [7] where the NMPC is based on a Hammerstein model.
	www.fceia.unr.edu.ar /~jcgomez/nmpc.html (824 words)

	MathWorks: Model Predictive Control Toolbox (Site not responding. Last check: 2007-11-06)
		These techniques were developed to address the practical issues associated with the control of large, multivariable processes where there are constraints on the manipulated and controlled variables.
		Model predictive control methods are typically used in chemical engineering and other continuous process control industries.
		The model predictive control approach uses an explicit linear dynamic model of the plant to predict the effect of future moves in manipulated variables.
	www.ccr.jussieu.fr /ccr/Documentation/Calcul/matlab5v11/docs/00002/00281.htm (312 words)

	On Constrained Infinite- Horizon Model Predictive Control (Site not responding. Last check: 2007-11-06)
		This, in particular, is a unique property for this MPC formulation and may prove to be its key advantage over alternative model predictive controllers that have in the past attempted to satisfy nominal stability/ feasibility by imposing restrictive assumptions on the predicted state and/ or control sequences.
		This, in turn, would allow model predictive control to be accepted as a general framework within which most of the modern control design methodologies can be cast while addressing an important practical issue of inequality constraints, imposed on system variables.
		On the other hand, in order to reduce the dimensionality of the corresponding optimisation problem from infinite to finite, a certain condition is imposed on the terminal predicted state, dictating the lower bound on the prediction horizon length required to guarantee equivalence to the infinite- horizon formulation.
	www-control.eng.cam.ac.uk /Seminars/abstracts/marjanovic.html (412 words)

	Monitoring of Model Predictive Control System - Abstract
		Model Predictive Control (MPC) systems are widely used in the petrochemical industry for difficult multiple input, multiple output (MIMO) control problems.
		MPC provides improved control performance compared to a multi-loop system by accounting for hard and soft constraints and the interactions between controlled and manipulated variables.
		Thus, if the MPC system is monitored in order to detect the presence of faults, one is assessing the performance of the MPC system fairly; if no fault exists, the system is operating as it was designed, which may or may not result in acceptable performance.
	www.chemengr.ucsb.edu /~ceweb/faculty/seborg/research/fl_abstract.html (523 words)

	Experience-Based Model Predictive Control Using Reinforcement Learning - Rudy Negenborn, Bart De Schutter, Marco ... (Site not responding. Last check: 2007-11-06)
		Model predictive control (MPC) is becoming an increasingly popular method to select actions for controlling dynamic systems.
		Traditionally MPC uses a model of the system to be controlled and a performance function to characterize the desired behavior of the system.
		In this paper we propose the use of MPC to control systems that can be described as Markov decision processes.
	www.negenborn.net /pubs/model_predictive_control/model_predictive_control.htm (265 words)

	Robust Model Predictive Control (Site not responding. Last check: 2007-11-06)
		Model predictive control has been widely used in industry since it was introduced in the late 70s.
		The optimization problem considered consists of a worst case quadratic performance criterion over a finite set of linear discrete-time models subject to inequality constraints on the states and control signals.
		Related work is to appear in a special issue of Journal of Process Control, edited by Y. Arkun and S. Shah, for papers presented at the 1997 IFAC Conference on Advanced Process Control, June 1997, Banff, Boyd, Crusius, and Hansson (1997).
	www-isl.stanford.edu /groups/MURI/annual98/node63.html (297 words)

	Encyclopedia article: Model predictive control (Site not responding. Last check: 2007-11-06)
		It usually relies on linear (additional info and facts about linear) models of the process.
		Control theory (additional info and facts about Control theory)
		Control engineering (additional info and facts about Control engineering)
	www.absoluteastronomy.com /encyclopedia/m/mo/model_predictive_control.htm (33 words)

	Process Control of David Kazmer (Site not responding. Last check: 2007-11-06)
		Including an outer loop for quality control (often implemented by a human operator), a three level control architecture for polymer processing is established as identified by Wang et.
		The control force may be applied to the valve pin in a number of ways, including pneumatic actuators, hydraulic actuators, and others.
		Self regulation of the melt pressure at these high control forces would be delivered when higher melt pressures are supplied at the valve inlet, as is observed for the molding trials that are represented with the cross symbols.
	kazmer.uml.edu /Research/polymer_process_control.htm (4415 words)

	[No title] (Site not responding. Last check: 2007-11-06)
		The course is intended for students and engineers that want to learn about the theory and practice of Model Predictive Control (MPC) and of optimization of constrained linear systems and hybrid dynamical systems.
		MPC was first proposed by industry to deal with the control of multivariable systems with a large number of inputs and outputs subject to constraints.
		In the last few years a theoretical basis for MPC has emerged for providing stability and robustness guarantees, for dealing with hybrid systems, and for dealing with fast sampling processes.
	www.control.isy.liu.se /calendar/shortcourses/bemporad.html (242 words)

	[No title]
		A model of asynchronous distributed computation is developed which requires very weak assumptions on the ordering of computations,the timing of information exchange,the amount of local information needed at each computation node, and the initial condition for the algorithm.
		Most of the existing results for these models are generalized and several new results are obtained relating mostly to the convergence of the dynamic programming algorithm and the existence of optimal stationary policies.
		The notion of a sufficiently informative function, which parallels the notion of a sufficient statistic of optimal control is introduced, and the possible decomposition of the optimal controller into an estimator and an actuator is demonstrated.
	web.mit.edu /dimitrib/www/publ.html (14142 words)

	IST Publications Page (Site not responding. Last check: 2007-11-06)
		A nonlinear model predictive control scheme for the stabilization of setpoint families.
		Dynamic modeling and nonlinear model predictive control of a fluid catalytic cracking unit.
		The tradeoff between modelling complexity and real-time feasibility in nonlinear model predictive control.
	www.ist.uni-stuttgart.de /publications (4006 words)

	Parameter Estimation and Output Feedback Nonlinear Model Predictive Control of an Industrial Batch Polymerization System (Site not responding. Last check: 2007-11-06)
		It is recognized that nonlinear model predictive control (NMPC) is an excellent candidate as a key enabling technology for the continued success of the chemical industries.
		The application of NMPC for the setpoint tracking and end-point property control of an industrial batch reactor is illustrated, with special emphasis related to challenges in the practical application.
		The reduced model is fitted to the experimental data from the real plant using maximum likelihood estimation.
	aiche.confex.com /aiche/2005/preliminaryprogram/abstract_25471.htm (643 words)

	Control Engineering - Software allows practical, model-predictive control (Site not responding. Last check: 2007-11-06)
		These capabilities allow PredictPro to provide practical model-predictive control, which enables control of an entire collection of process units as a single entity, rather than managing them as individual loops or variables.
		Because Predict and PredictPro are fully embedded in DeltaV, users can implement pre-engineered components and function blocks to quickly develop multivariable control strategies, validate, test, and deploy.
		Using these advanced control techniques again enables variability in key process variables to be dramatically reduced.
	www.manufacturing.net /CTL/article/CA372395.html (359 words)

	validmod (Model Predictive Control Toolbox) (Site not responding. Last check: 2007-11-06)
		Validates an impulse response model for a new set of data.
		Model validation is a very important part of building a model.
		; two plots -- plot of actual and predicted output, and plot of output residual -- are produced for
	www.weizmann.ac.il /matlab/toolbox/mpc/validmod.html (102 words)

	Minisymposium "Nonlinear Model Predictive Control" (Site not responding. Last check: 2007-11-06)
		Nonlinear Model Predictive Control (NMPC) is an emerging feedback control technique that uses a nonlinear dynamic process model for online optimization of predicted future process behaviour.
		By performing this prediction and optimization repeatedly on a moving horizon, NMPC allows to provide feedback to disturbances and to perform setpoint changes efficiently.
		Challenges for industrial NMPC applications comprise the reliable solution of large scale nonlinear optimal control problems in real-time, online state and parameter estimation, stability of the closed loop, and the question of robustness and how to address model-plant mismatch.
	www.iwr.uni-heidelberg.de /~Moritz.Diehl/ECMI_NMPC/ecmi.html (315 words)

	Model Predictive Control II (Site not responding. Last check: 2007-11-06)
		Model Predictive Control II Model Predictive Control II This will be covered soon, maybe even today.
		And here it is! I did this in MS Word since the Coweb page was down for much the day while I was working on it.
		Model Predictive Control I last edited on 6 December 2000 at 10:29 am by 24-241-85-28.hsacorp.net.
	coweb.cc.gatech.edu /process/145 (85 words)

	imp2step (Model Predictive Control Toolbox) (Site not responding. Last check: 2007-11-06)
		Constructs a multi-input multi-output model in MPC step format from multi-input single-output impulse response matrices.
		, etc., a model in MPC step format is constructed.
		The limit on the number of impulse response matrices
	www.weizmann.ac.il /matlab/toolbox/mpc/imp2step.html (111 words)

	Elsevier.com - Journal of Process Control (Site not responding. Last check: 2007-11-06)
		This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems.
		Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at process control.
		Engineers in the process industries (including bulk chemicals, oil refining, pulp and paper, petro-chemicals, pharmaceuticals, biochemicals, food processing, minerals processing, agrochemicals and speciality chemicals); academics specialising in the control aspects of process engineering; regulatory bodies; government laboratories.
	www.elsevier.com /locate/jprocont (254 words)

	[No title]
		Zhao, H., J. Guiver, R. Neelakantan and L. Biegler, A nonlinear industrial model predictive controller using integrated PLS and neural net state-space model, Control Engineering Practice, 9, p.
		Zhao, H., J. Guiver, R. Neelakantan and L. Biegler, A Nonlinear Industrial Model Predictive Controller Using Integrated PLS and Neural Net State Space Models, Proc.
		Staus, G., L. Biegler, and B. Ydstie, Global Optimization for Identification, Proceedings of the 36th IEEE Conference on Decision and Control, p.
	dynopt.cheme.cmu.edu /papers/papers.html (2463 words)

	addictioninfo.ca - Life process model of addiction (Site not responding. Last check: 2007-11-06)
		Find life process model of addiction and more at Lycos Search.
		Read about life process model of addiction in the free online encyclopedia and dictionary.
		Find life process model of addiction at one of the best sites the Internet has to offer!
	www.addictioninfo.ca /Life-process-model-of-addiction/reference/search (262 words)

Try your search on: Qwika (all wikis)