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Nonlinear Model Based Process Control, R. Berber; Costas Kravaris


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Автор: R. Berber; Costas Kravaris
Название:  Nonlinear Model Based Process Control
ISBN: 9789401061407
Издательство: Springer
Классификация:




ISBN-10: 9401061408
Обложка/Формат: Soft cover
Страницы: 910
Вес: 1.47 кг.
Дата издания: 12.02.2012
Серия: Nato Science Series E:
Язык: English
Издание: Softcover reprint of
Иллюстрации: Biography
Размер: 234 x 156 x 46
Читательская аудитория: Professional & vocational
Основная тема: Industrial Chemistry/Chemical Engineering
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Proceedings of the NATO Advanced Study Institute, Antalya, Turkey, August 10-20, 1997


Nonlinear Model Predictive Control

Автор: Grune
Название: Nonlinear Model Predictive Control
ISBN: 0857295004 ISBN-13(EAN): 9780857295002
Издательство: Springer
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Цена: 22359.00 р.
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Описание: Nonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. NMPC schemes with and without stabilizing terminal constraints are detailed and intuitive examples illustrate the performance of different NMPC variants. An introduction to nonlinear optimal control algorithms gives insight into how the nonlinear optimisation routine – the core of any NMPC controller – works. An appendix covering NMPC software and accompanying software in MATLAB® and C++(downloadable from www.springer.com/ISBN) enables readers to perform computer experiments exploring the possibilities and limitations of NMPC.

Model-Based Control of Particulate Processes

Автор: Christofides P.D.
Название: Model-Based Control of Particulate Processes
ISBN: 1402009364 ISBN-13(EAN): 9781402009365
Издательство: Springer
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Цена: 23508.00 р.
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Описание: The interest in control of particulate processes has been triggered by the need to achieve tight distributed control of size distributions that greatly influence particulate product properties and quality. Drawing from recent advances in dynamics of infinite-dimensional systems and nonlinear control theory, control of particulate processes using population balances has evolved into a very active research area within the field of process control. This book - the first of its kind - presents general methods for the synthesis of nonlinear, robust and constrained feedback controllers for broad classes of particulate process models and illustrates their applications to industrially-important crystallization, aerosol and thermal spray processes. The controllers use a finite number of measurement sensors and control actuators to achieve stabilization of the closed-loop system, output tracking, attenuation of the effect of model uncertainty and handling of actuator saturation.Beginning with an introduction to control of particulate processes, the book discusses nonlinear order reduction and nonlinear, robust and constrained control of particulate spatially-homogeneous processes, and nonlinear control of spatially-homogeneous particulate processes. The synthesis of the controllers is performed by using geometric and Lyapunov-based control techniques. The book includes comparisons of the methods followed for controller synthesis with other approaches and discussions of practical implementation issues that can help researchers and engineers understand the development and application of the methods in greater depth. The methods are applied to continuous and batch crystallization processes, a titania aerosol reactor and a thermal spray process to regulate product size distribution. The resulting benefits in closed-loop performance, robustness and actuator saturation handling compared to other techniques for control of particulate processes are demonstrated through computer simulations.The book assumes a basic knowledge about population balances and nonlinear control.Researchers and graduate students in process control, particle technology and control systems theory, applied mathematicians and process control engineers will find this book a useful resource.

Open-Source Robotics and Process Control Cookbook,

Автор: Lewin Edwards
Название: Open-Source Robotics and Process Control Cookbook,
ISBN: 0750677783 ISBN-13(EAN): 9780750677783
Издательство: Elsevier Science
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Цена: 8925.00 р.
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Описание: Shows how to develop robust, dependable real-time systems for robotics and other control applications, using open-source tools. This book demonstrates efficient and low-cost embedded hardware and software design techniques, based on Linux as the development platform and operating system and the Atmel AVR as the primary microcontroller.

Model Predictive Control

Автор: Camacho
Название: Model Predictive Control
ISBN: 1852336943 ISBN-13(EAN): 9781852336943
Издательство: Springer
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Цена: 9781.00 р.
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Описание: The second edition of "Model Predictive Control" provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners.

Mechanics and Model-Based Control of Advanced Engineering Systems

Автор: Alexander K. Belyaev; Hans Irschik; Michael Kromme
Название: Mechanics and Model-Based Control of Advanced Engineering Systems
ISBN: 3709115701 ISBN-13(EAN): 9783709115701
Издательство: Springer
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Цена: 20896.00 р.
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Описание: Mechanics and Model-Based Control of Advanced Engineering Systems collects 32 contributions presented at the International Workshop on Advanced Dynamics and Model Based Control of Structures and Machines, which took place in St. Petersburg, Russia in July 2012.

Developments in Model-Based Optimization and Control

Автор: Sorin Olaru; Alexandra Grancharova; Fernando Lobo
Название: Developments in Model-Based Optimization and Control
ISBN: 3319266853 ISBN-13(EAN): 9783319266855
Издательство: Springer
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Цена: 16979.00 р.
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Описание:

Introduction.- Part I. Complexity and Structural Properties of Linear Model Predictive Control.- 1. Complexity Certifications of First Order Inexact Lagrangian Methods for General Convex Programming: Application to Real-time MPC.- 2. Fully Inverse Parametric Linear/Quadratic Programming Problems via Convex Liftings.- 3. Implications of Inverse Parametric Optimization in Model Predictive Control.- Part II. Distributed-coordinated and Multi-objective Features of Model Predictive Control.- 4. Distributed Robust Model Predictive Control of Interconnected Polytopic Systems.- 5. Optimal Distributed-Coordinated Approach for Energy Management in Multisource Electric Power Generation Systems.- 6. Evolutionary-game-based Dynamical Tuning for Multi-objective Model Predictive Control.- Part III. Collaborative Model Predictive Control.- 7. A Model Predictive Control-based Architecture for Cooperative Path-following of Multiple Unmanned Aerial Vehicles.- 8. Predictive Control for Path Following. From Trajectory Generation to the Parameterization of the Discrete Tracking Sequences.- 9. Formation Reconfiguration using Model Predictive Control Techniques for Multi-Agent Dynamical Systems.- Part IV. Applications of Optimization-based Control and Identification.- 10. Optimal Operation of a Lumostatic Microalgae Cultivation Process.- 11. Bioprocesses Parameter Estimation by Heuristic Optimization Techniques.- 12. Real-time Experimental Implementation of Predictive Control Schemes in a Small-scale Pasteurization Plant.- Part V. Optimization-based Analysis and Design for Particular Classes of Dynamical Systems.- 13. An Optimization-based Framework for Impulsive Control Systems.- 14. Robustness Issues in Control of Bilinear Discrete-Time Systems - Applied to the Control of Power Converters.- 15. On the LPV Control Design and its Applications to Some Classes of Dynamical Systems.- 16. Ultimate Bounds and Robust Invariant Sets for Linear Systems with State-dependent Disturbances.- 17. RPI Approximations of the mRPI Set Characterizing Linear Dynamics with Zonotopic Disturbances.



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