Receding Horizon Control: Model Predictive Control for State Models (Advanced Textbooks in Control a

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  • Bestselling Series
  • Receding Horizon Control : Model Predictive Control for State Models
  • Find a copy in the library
  • Stanford Libraries

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    The Glass Cage Nicholas Carr. Arduino Bots and Gadgets Kimmo Karvinen.

    Bestselling Series

    Receding Horizon Control (RHC) introduces the essentials of a successful feedback Advanced Textbooks in Control and Signal Processing. Free Preview . © Receding Horizon Control. Model Predictive Control for State Models. bahana-line.com: Receding Horizon Control: Model Predictive Control for State Models (Advanced Textbooks in Control and Signal Processing): W. H. Kwon.

    Arduino Wearables Tony Olsson. Other books in this series. Robust and Adaptive Control Eugene Lavretsky. Introduction to Optimal Estimation Jonathan K.

    Receding Horizon Control : Model Predictive Control for State Models

    Modelling and Control of Robot Manipulators L. Model Predictive Control Mark Cannon. Control of Dead-time Processes Julio E. System Identification Karel J.

    Discrete-time Stochastic Systems Torsten Soderstrom. Back cover copy Receding Horizon Control introduces the essentials of a successful feedback strategy that has emerged in many industrial fields: Receding horizon control RHC has a number of advantages over other types of control: The text builds understanding starting with optimal controls for simple linear systems and working through constrained systems to nonlinear cases.

    RHC is applied to discrete-time systems for better understanding and easier computer application. Its diverse techniques are unified using the state-space framework.

    Find a copy in the library

    Worked examples and exercises throughout the book allow you to practise as you go. Graduate students following masters and doctoral courses in control theory and engineering will find Receding Horizon Control to be an excellent companion to tuition and research. Tutors and academics researching model predictive control can use this not only as a scholarly textbook but as a co-ordinated reference for its wide range of receding horizon schemes.

    He has an excellent reputation as a leading control engineer and an important member of IFAC. He has specialised in model predictive control for many years. Professor Kwon was a research associate at Brown University in and from to he was an adjunct assistant professor at the University of Iowa. Since , which includes a period from to when he was a visiting assistant professor at Stanford University, he has been with Seoul National University.

    During his career Professor Kwon has published more than 60 international journal papers and approximately international conference papers. He was a key founder of the Korea Automatic Control Conference, now in its fourteenth year, in which about people participate every year. Coordinating multiple optimization-based controllers: New opportunities and challenges. Image-guided modeling of virus growth and spread. Two classes of quasi-steady-state model reductions for stochastic kinetics.

    Distributed model predictive control of large-scale systems. Model predictive control of Si-Ge thin film chemical vapor deposition. Fast, large-scale model predictive control by partial enumeration. Model-based object recognition to measure crystal size and shape distributions from in situ video images. Rawlings, and Jakob Stoustrup. Application of autocovariance least-squares method for model predictive control of hybrid ventilation in livestock stable.

    Improved state estimation using a combination of moving horizon estimator and particle filters. Model predictive control of thermal comfort and indoor air quality in livestock stable. An iterative, direct closed-loop identification method for model refinement: Application to interaction estimation. Stochastic simulation of catalytic surface reactions in the fast diffusion limit. State estimation approach for determining composition and growth rate of Si Ge chemical vapor deposition utilizing real-time ellipsometric measurements.

    Identification for Decentralized Model Predictive Control. Implementable distributed model predictive control with guaranteed performance properties. Particle filtering and moving horizon estimation. An algorithm for analyzing noisy, in situ images of high-aspect-aspect ratio crystals to monitor particle size distribution. Industrial crystallization process control. The autocovariance least-squares methods for estimating covariances: Application to model-based control of chemical reactors.

    Distributed output feedback MPC for power system control. Equivalence of MPC disturbance models identified from data. A partial enumeration strategy for fast large-scale linear model predictive control. A new autocovariance least-squares method for estimating noise covariances. Automatica , 42 2: On the origins of approximations for stochastic chemical kinetics. Dynamics of viral infections: Incorporating both the intracellular and extracellular levels. Critical evaluation of extended Kalman filtering and moving horizon estimation.

    Stability and optimality of distributed model predictive control. Model-based control methodologies for catalytic surface reactions. On the stochastic simulation of particulate systems. A candidate to replace PID control: Closed-loop behavior of nonlinear model predictive control. Optimal operation of a seeded pharmaceutical crystallization with growth-dependent dispersion. Nonlinear model predictive control via feasibility-perturbed sequential quadratic programming.

    A new robust model predictive control method. Numerical methods for large-scale moving horizon estimation and control. Plant-wide optimal control with decentralized MPC. A fast, easily tuned, SISO, model predictive controller. Identification for decentralized MPC. Target linearization and model predictive control of polymerization processes. Parameter estimation for industrial polymerization processes.

    Existence and computation of infinite horizon model predictive control with active steady-state input constraints. Online monitoring of MPC disturbance models from closed-loop data. Constrained state estimation for nonlinear discrete-time systems: Stability and moving horizon approximations.

    Disturbance models for offset-free model predictive control. Ten years of Octave -- recent developments and plans for the future. Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics.

    Stanford Libraries

    Efficient moving horizon estimation and nonlinear model predictive control. A moving horizon approach. Crystallization of para-xylene in scraped-surface crystallizers. Particle-shape monitoring and control in crystallization processes. Constrained linear state estimation - a moving horizon approach. Automatica , 37 Automatica , 37 3: Rawlings, and Rahul Bindlish.

    Feasible real-time nonlinear model predictive control. Tutorial overview of model predictive control. Constrained model predictive control: Automatica , 36 6: Linear programming and model predictive control. Nonlinear moving horizon estimation. Authors' reply to letter to the editor.

    Feasibility issues in linear model predictive control. Steady states and constraints in model predictive control. Suboptimal model predictive control feasibility implies stability. Nonlinear predictive control and moving horizon estimation - an introductory overview. Frank, editor, Advances in Control: Stability of constrained linear moving horizon estimation. Model predictive control technology. On the application of interior point methods to model predictive control. Predictive control of sheet- and film-forming processes. Constrained linear quadratic regulation.

    Batch crystallization of a photochemical: Modelling, control and filtration. Efficient interior point methods for model predictive control. Efficient implementation of model predictive control for sheet and film forming processes. Parameter estimation in a dynamic model of a copolymerization process. In American Control Conference , pages , Optimization problems in model predictive control.

    On infeasibilities in model predictive control. Seborg, editors, Nonlinear Process Control , pages Discrete-time stability with perturbations: Application to model predictive control. Automatica , 33 3: Identification and control of sheet forming processes. Infinite horizon linear quadratic control with constraints. Segregated fermentation model for growth and differentiation of Bacillus licheniformis. Rawlings and I-Lung Chien. Gage control of film and sheet-forming processes. Model identification for crystallization: Theory and experimental verification. A moving horizon-based approach for least-squares state estimation.

    Receding horizon control and discontinuous state feedback stabilization. Control , 62 5: Estimation and control of sheet and film forming processes. Stability of model predictive control under perturbations. Implementable model predictive control in the state space. Octave--a high level interactive language for numerical computations. Topics in model predictive control. Nonlinear moving horizon state estimation. Model identification and control strategies for batch cooling crystallizers. Nonlinear model predictive control: Stability of neural net based model predictive control.

    The stability of constrained receding horizon control with state estimation. Gage control of film and sheet forming processes. DuPont Accession Report , August 27, Stability of constrained receding horizon control. Constrained state estimation and discontinuous feedback in model predictive control.

    In European Control Conference , pages , Receding horizon recursive state estimation. Receding horizon control with an infinite horizon.