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Author : Thomas Pröll
Publisher :
ISBN 13 :
Total Pages : 710 pages
Book Rating : 4.:/5 (299 download)
Download or read book Model Predictive Control Based on Nonlinear Autoregressive and Neural Network Models written by Thomas Pröll and published by . This book was released on 1993 with total page 710 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Maciej Ławryńczuk
Publisher : Springer Science & Business Media
ISBN 13 : 3319042297
Total Pages : 336 pages
Book Rating : 4.3/5 (19 download)
Download or read book Computationally Efficient Model Predictive Control Algorithms written by Maciej Ławryńczuk and published by Springer Science & Business Media. This book was released on 2014-01-24 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: · A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. · Implementation details of the MPC algorithms for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. · The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). · The MPC algorithms with neural approximation with no on-line linearization. · The MPC algorithms with guaranteed stability and robustness. · Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding.
Author : Basil Kouvaritakis
Publisher : IET
ISBN 13 : 0852969848
Total Pages : 277 pages
Book Rating : 4.8/5 (529 download)
Download or read book Non-linear Predictive Control written by Basil Kouvaritakis and published by IET. This book was released on 2001-10-26 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: The advantage of model predictive control is that it can take systematic account of constraints, thereby allowing processes to operate at the limits of achievable performance. Engineers in academia, industry, and government from the US and Europe explain how the linear version can be adapted and applied to the nonlinear conditions that characterize the dynamics of most real manufacturing plants. They survey theoretical and practical trends, describe some specific theories and demonstrate their practical application, derive strategies that provide appropriate assurance of closed-loop stability, and discuss practical implementation. Annotation copyrighted by Book News, Inc., Portland, OR
Author : Maciej Ławryńczuk
Publisher : Springer Nature
ISBN 13 : 3030838153
Total Pages : 358 pages
Book Rating : 4.0/5 (38 download)
Download or read book Nonlinear Predictive Control Using Wiener Models written by Maciej Ławryńczuk and published by Springer Nature. This book was released on 2021-09-21 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process nonlinearity. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant. A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages of neural Wiener models are demonstrated.
Author : Jiamei Deng
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (111 download)
Download or read book Predictive Control of Nonlinear Systems Using Feedback Linearisation Based on Dynamic Neural Networks written by Jiamei Deng and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Model predictive control (MPC) is an important industrial control technique. Most conventional MPC schemes use linear models. However, the use of linear models can result in a serious deterioration of control performance for many nonlinear plants. This thesis presents a hybrid control strategy integrating dynamic neural networks and feedback linearisation into a predictive control scheme. The work focuses on the handling of input constraints, the training of the dynamic neural network, and using this network as a close-loop observer. Real time experiments and simulation studies are carried out based on a single link manipulator, a two tank systems and a 3D crane system - abstract.
Author : Jiamei Deng
Publisher : LAP Lambert Academic Publishing
ISBN 13 : 9783844300093
Total Pages : 200 pages
Book Rating : 4.3/5 ( download)
Download or read book Predictive Control of Nonlinear System Based on Neural Networks written by Jiamei Deng and published by LAP Lambert Academic Publishing. This book was released on 2011-02 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model predictive control (MPC) is an important industrial control technique. Most conventional MPC schemes use linear models. However, the use of linear models can result in a serious deterioration of control performance with many types of nonlinear plants. Feedback linearisation is an important nonlinear control technique which can transform a nonlinear system into a linear system. Dynamic neural networks have the ability to approximate multi-input multi-output general nonlinear systems and have the differential equation structure. This book presents a hybrid control strategy integrating dynamic neural networks and feedback linearisation into a predictive control scheme. This book can be used as a course textbook, a source for practising control engineers with an interest in nonlinear control techniques and also a reference material for academic researchers in nonlinear control theory.
Author : Maciej Lawrynczuk
Publisher :
ISBN 13 : 9783319042305
Total Pages : 342 pages
Book Rating : 4.0/5 (423 download)
Download or read book Computationally Efficient Model Predictive Control Algorithms written by Maciej Lawrynczuk and published by . This book was released on 2014-02-28 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Partha Pratim Kanjilal
Publisher : IET
ISBN 13 : 9780863411939
Total Pages : 542 pages
Book Rating : 4.4/5 (119 download)
Download or read book Adaptive Prediction and Predictive Control written by Partha Pratim Kanjilal and published by IET. This book was released on 1995 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides unified coverage of the principles and methods of various disciplines' approaches to prediction and control of processes expressed by discrete-time models, especially adaptive prediction, for students, researchers, and practitioners in the field. Chapters on methods of adaptive prediction for linear and non-linear processes, such as input-output model based prediction and Kalman filter predictors, avoid complex mathematical symbols and expressions, and contain examples and case studies. Includes introductory material on process models and parameter estimation, plus reference appendices and data sets. Annotation copyright by Book News, Inc., Portland, OR
Author : Jinfeng Liu
Publisher : MDPI
ISBN 13 : 303897420X
Total Pages : 231 pages
Book Rating : 4.0/5 (389 download)
Download or read book New Directions on Model Predictive Control written by Jinfeng Liu and published by MDPI. This book was released on 2019-01-16 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "New Directions on Model Predictive Control" that was published in Mathematics
Author : Krzysztof Patan
Publisher : Springer
ISBN 13 : 303011869X
Total Pages : 231 pages
Book Rating : 4.0/5 (31 download)
Download or read book Robust and Fault-Tolerant Control written by Krzysztof Patan and published by Springer. This book was released on 2019-03-16 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust and Fault-Tolerant Control proposes novel automatic control strategies for nonlinear systems developed by means of artificial neural networks and pays special attention to robust and fault-tolerant approaches. The book discusses robustness and fault tolerance in the context of model predictive control, fault accommodation and reconfiguration, and iterative learning control strategies. Expanding on its theoretical deliberations the monograph includes many case studies demonstrating how the proposed approaches work in practice. The most important features of the book include: a comprehensive review of neural network architectures with possible applications in system modelling and control; a concise introduction to robust and fault-tolerant control; step-by-step presentation of the control approaches proposed; an abundance of case studies illustrating the important steps in designing robust and fault-tolerant control; and a large number of figures and tables facilitating the performance analysis of the control approaches described. The material presented in this book will be useful for researchers and engineers who wish to avoid spending excessive time in searching neural-network-based control solutions. It is written for electrical, computer science and automatic control engineers interested in control theory and their applications. This monograph will also interest postgraduate students engaged in self-study of nonlinear robust and fault-tolerant control.
Author : Alexandra Grancharova
Publisher : Springer
ISBN 13 : 3642287808
Total Pages : 241 pages
Book Rating : 4.6/5 (422 download)
Download or read book Explicit Nonlinear Model Predictive Control written by Alexandra Grancharova and published by Springer. This book was released on 2012-03-22 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation and the possibility to design embedded control systems with low software and hardware complexity. This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations: ؠ Nonlinear systems described by first-principles models and nonlinear systems described by black-box models; - Nonlinear systems with continuous control inputs and nonlinear systems with quantized control inputs; - Nonlinear systems without uncertainty and nonlinear systems with uncertainties (polyhedral description of uncertainty and stochastic description of uncertainty); - Nonlinear systems, consisting of interconnected nonlinear sub-systems. The proposed mp-NLP approaches are illustrated with applications to several case studies, which are taken from diverse areas such as automotive mechatronics, compressor control, combustion plant control, reactor control, pH maintaining system control, cart and spring system control, and diving computers.
Author : Lars Grüne
Publisher : Springer Science & Business Media
ISBN 13 : 0857295012
Total Pages : 364 pages
Book Rating : 4.8/5 (572 download)
Download or read book Nonlinear Model Predictive Control written by Lars Grüne and published by Springer Science & Business Media. This book was released on 2011-04-11 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Rolf Findeisen
Publisher : Springer
ISBN 13 : 3540726993
Total Pages : 644 pages
Book Rating : 4.5/5 (47 download)
Download or read book Assessment and Future Directions of Nonlinear Model Predictive Control written by Rolf Findeisen and published by Springer. This book was released on 2007-09-08 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thepastthree decadeshaveseenrapiddevelopmentin the areaofmodelpred- tive control with respect to both theoretical and application aspects. Over these 30 years, model predictive control for linear systems has been widely applied, especially in the area of process control. However, today’s applications often require driving the process over a wide region and close to the boundaries of - erability, while satisfying constraints and achieving near-optimal performance. Consequently, the application of linear control methods does not always lead to satisfactory performance, and here nonlinear methods must be employed. This is one of the reasons why nonlinear model predictive control (NMPC) has - joyed signi?cant attention over the past years,with a number of recent advances on both the theoretical and application frontier. Additionally, the widespread availability and steadily increasing power of today’s computers, as well as the development of specially tailored numerical solution methods for NMPC, bring thepracticalapplicabilityofNMPCwithinreachevenforveryfastsystems.This has led to a series of new, exciting developments, along with new challenges in the area of NMPC.
Author : Marcus Reble
Publisher : Logos Verlag Berlin GmbH
ISBN 13 : 3832533818
Total Pages : 159 pages
Book Rating : 4.8/5 (325 download)
Download or read book Model Predictive Control for Nonlinear Continuous-Time Systems with and Without Time-Delays written by Marcus Reble and published by Logos Verlag Berlin GmbH. This book was released on 2013 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this thesis is the development of novel model predictive control (MPC) schemes for nonlinear continuous-time systems with and without time-delays in the states which guarantee asymptotic stability of the closed-loop. The most well-studied MPC approaches with guaranteed stability use a control Lyapunov function as terminal cost. Since the actual calculation of such a function can be difficult, it is desirable to replace this assumption by a less restrictive controllability assumption. For discrete-time systems, the latter assumption has been used in the literature for the stability analysis of so-called unconstrained MPC, i.e., MPC without terminal cost and terminal constraints. The contributions of this thesis are twofold. In the first part, we propose novel MPC schemes with guaranteed stability based on a controllability assumption, whereas we extend different MPC schemes with guaranteed stability to nonlinear time-delay systems in the second part. In the first part of this thesis, we derive explicit stability conditions on the prediction horizon as well as performance guarantees for unconstrained MPC. Starting from this result, we propose novel alternative MPC formulations based on combinations of the controllability assumption with terminal cost and terminal constraints. One of the main contributions is the development of a unifying MPC framework which allows to consider both MPC schemes with terminal cost and terminal constraints as well as unconstrained MPC as limit cases of our framework. In the second part of this thesis, we show that several MPC schemes with and without terminal constraints can be extended to nonlinear time-delay systems. Due to the infinite-dimensional nature of these systems, the problem is more involved and additional assumptions are required in the controller design. We investigate different MPC schemes with and without terminal constraints and/or terminal cost terms and derive novel stability conditions. Furthermore, we pay particular attention to the calculation of the involved control design parameters.
Author : Raffaele Soloperto
Publisher : Logos Verlag Berlin GmbH
ISBN 13 : 383255744X
Total Pages : 172 pages
Book Rating : 4.8/5 (325 download)
Download or read book Learning-based Model Predictive Control with closed-loop guarantees written by Raffaele Soloperto and published by Logos Verlag Berlin GmbH. This book was released on 2023-11-13 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: The performance of model predictive control (MPC) largely depends on the accuracy of the prediction model and of the constraints the system is subject to. However, obtaining an accurate knowledge of these elements might be expensive in terms of money and resources, if at all possible. In this thesis, we develop novel learning-based MPC frameworks that actively incentivize learning of the underlying system dynamics and of the constraints, while ensuring recursive feasibility, constraint satisfaction, and performance bounds for the closed-loop. In the first part, we focus on the case of inaccurate models, and analyze learning-based MPC schemes that include, in addition to the primary cost, a learning cost that aims at generating informative data by inducing excitation in the system. In particular, we first propose a nonlinear MPC framework that ensures desired performance bounds for the resulting closed-loop, and then we focus on linear systems subject to uncertain parameters and noisy output measurements. In order to ensure that the desired learning phase occurs in closed-loop operations, we then propose an MPC framework that is able to guarantee closed-loop learning of the controlled system. In the last part of the thesis, we investigate the scenario where the system is known but evolves in a partially unknown environment. In such a setup, we focus on a learning-based MPC scheme that incentivizes safe exploration if and only if this might yield to a performance improvement.
Author : Helen Durand
Publisher : Foundations and Trends (R) in Systems and Control
ISBN 13 : 9781680834321
Total Pages : 68 pages
Book Rating : 4.8/5 (343 download)
Download or read book Economic Model Predictive Control written by Helen Durand and published by Foundations and Trends (R) in Systems and Control. This book was released on 2018-06-19 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: Economic Model Predictive Control (EMPC) is a control strategy that moves process operation away from the steady-state paradigm toward a potentially time-varying operating strategy to improve process profitability. The EMPC literature is replete with evidence that this new paradigm may enhance process profits when a model of the chemical process provides a sufficiently accurate representation of the process dynamics. Systems using EMPC often neglect the dynamics associated with equipment and are often neglected when modeling a chemical process. Recent studies have shown they can significantly impact the effectiveness of an EMPC system. Concentrating on valve behavior in a chemical process, this monograph develops insights into the manner in which equipment behavior should impact the design process for EMPC and to provide a perspective on a number of open research topics in this direction. Written in tutorial style, this monograph provides the reader with a full literature review of the topic and demonstrates how these techniques can be adopted in a practical system.
Author : Stephen A. Billings
Publisher : John Wiley & Sons
ISBN 13 : 1118535553
Total Pages : 611 pages
Book Rating : 4.1/5 (185 download)
Download or read book Nonlinear System Identification written by Stephen A. Billings and published by John Wiley & Sons. This book was released on 2013-07-29 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) model The orthogonal least squares algorithm that allows models to be built term by term where the error reduction ratio reveals the percentage contribution of each model term Statistical and qualitative model validation methods that can be applied to any model class Generalised frequency response functions which provide significant insight into nonlinear behaviours A completely new class of filters that can move, split, spread, and focus energy The response spectrum map and the study of sub harmonic and severely nonlinear systems Algorithms that can track rapid time variation in both linear and nonlinear systems The important class of spatio-temporal systems that evolve over both space and time Many case study examples from modelling space weather, through identification of a model of the visual processing system of fruit flies, to tracking causality in EEG data are all included to demonstrate how easily the methods can be applied in practice and to show the insight that the algorithms reveal even for complex systems NARMAX algorithms provide a fundamentally different approach to nonlinear system identification and signal processing for nonlinear systems. NARMAX methods provide models that are transparent, which can easily be analysed, and which can be used to solve real problems. This book is intended for graduates, postgraduates and researchers in the sciences and engineering, and also for users from other fields who have collected data and who wish to identify models to help to understand the dynamics of their systems.
