Author: Hoai-Nam Nguyen
Publisher: Springer
ISBN: 3319028278
Category : Technology & Engineering
Languages : en
Pages : 202
Get Book
Book Description
A comprehensive development of interpolating control, this monograph demonstrates the reduced computational complexity of a ground-breaking technique compared with the established model predictive control. The text deals with the regulation problem for linear, time-invariant, discrete-time uncertain dynamical systems having polyhedral state and control constraints, with and without disturbances, and under state or output feedback. For output feedback a non-minimal state-space representation is used with old inputs and outputs as state variables. Constrained Control of Uncertain, Time-Varying, Discrete-time Systems details interpolating control in both its implicit and explicit forms. In the former at most two linear-programming or one quadratic-programming problem are solved on-line at each sampling instant to yield the value of the control variable. In the latter the control law is shown to be piecewise affine in the state, and so the state space is partitioned into polyhedral cells so that at each sampling interval the cell to which the measured state belongs must be determined. Interpolation is performed between vertex control, and a user-chosen control law in its maximal admissible set surrounding the origin. Novel proofs of recursive feasibility and asymptotic stability of the vertex control law, and of the interpolating control law are given. Algorithms for implicit and explicit interpolating control are presented in such a way that the reader may easily realize them. Each chapter includes illustrative examples, and comparisons with model predictive control in which the disparity in computational complexity is shown to be particularly in favour of interpolating control for high-order systems, and systems with uncertainty. Furthermore, the performance of the two methods proves similar except in those cases when a solution cannot be found with model predictive control at all. The book concludes with two high dimensional examples and a benchmark robust model predictive control problem: the non-isothermal continuously-stirred-tank reactor. For academic control researchers and students or for control engineers interested in implementing constrained control systems Constrained Control of Uncertain, Time-Varying, Discrete-time Systems will provide an attractive low-complexity control alternative for cases in which model predictive control is currently attempted.
Author: George Leitmann
Publisher: CRC Press
ISBN: 100012472X
Category : Science
Languages : en
Pages : 229
Get Book
Book Description
This multi-authored volume presents selected papers from the Eighth Workshop on Dynamics and Control. Many of the papers represent significant advances in this area of research, and cover the development of control methods, including the control of dynamical systems subject to mixed constraints on both the control and state variables, and the development of a control design method for flexible manipulators with mismatched uncertainties. Advances in dynamic systems are presented, particularly in game-theoretic approaches and also the applications of dynamic systems methodology to social and environmental problems, for example, the concept of virtual biospheres in modeling climate change in terms of dynamical systems.
Author: Nikolay Smirnov
Publisher: Springer Nature
ISBN: 3030879666
Category : Technology & Engineering
Languages : en
Pages : 886
Get Book
Book Description
The proceedings of the 4th Stability and Control Processes Conference are focused on modern applied mathematics, stability theory, and control processes. The conference was held in recognition of the 90th birthday of Professor Vladimir Ivanovich Zubov (1930–2000). This selection of papers reflects the wide-ranging nature of V. I. Zubov’s work, which included contributions to the development of the qualitative theory of differential equations, the theory of rigid body motion, optimal control theory, and the theory of electromagnetic fields. It helps to advance many aspects of the theory of control systems, including questions of motion stability, nonlinear oscillations in control systems, navigation and reliability of control devices, vibration theory, and quantization of orbits. The disparate applications covered by the book – in mechanical systems, game theory, solid-state physics, socio-economic systems and medical and biological systems, control automata and navigation – are developments from Professor Zubov’s in-depth studies on the theory of stability of motion, the theory of automatic control and the theory of the motions of optimal processes. Stability and Control Processes presents research continuing the legacy of V. I. Zubov and updates it with sections focused on intelligence-based control. These proceedings will be of interest to academics, professionals working in industry and researchers alike.
Author: Sorin Olaru
Publisher: Springer
ISBN: 331926687X
Category : Technology & Engineering
Languages : en
Pages : 385
Get Book
Book Description
This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and design; and · applications to bioprocesses, multivehicle systems or energy management. The various contributions cover a subject spectrum including inverse optimality and more modern decentralized and cooperative formulations of receding-horizon optimal control. Readers will find fourteen chapters dedicated to optimization-based tools for robustness analysis, and decision-making in relation to feedback mechanisms—fault detection, for example—and three chapters putting forward applications where the model-based optimization brings a novel perspective. Developments in Model-Based Optimization and Control is a selection of contributions expanded and updated from the Optimisation-based Control and Estimation workshops held in November 2013 and November 2014. It forms a useful resource for academic researchers and graduate students interested in the state of the art in predictive control. Control engineers working in model-based optimization and control, particularly in its bioprocess applications will also find this collection instructive.
Author: Sophie Tarbouriech
Publisher: Springer
ISBN:
Category : Computers
Languages : en
Pages : 208
Get Book
Book Description
In practical control problems, many constraints have to be handled in order to design controllers which operate in a real environment. By combining results on robust control and saturating control, this book attempts to provide positive help for practical situations and, as one of the first books to merge the two control fields, it should generate considerable interest in scientific/acad emic circles. The ten chapters, which deal with stabilization and control of both linear and nonlinear systems, are each independent in their approach - some deal purely with theoretical results whilst others concentrate on ways in which the theory can be applied. The book's unity is secured by the desire to formulate control design requirements through constraints on input and model uncertainty description.
Author: Lifeng Ma
Publisher: John Wiley & Sons
ISBN: 1118929470
Category : Mathematics
Languages : en
Pages : 320
Get Book
Book Description
Unifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented phenomena Establishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performance Includes case studies of several nonlinear stochastic systems Investigates the phenomena of incomplete information, including missing/degraded measurements, actuator failures and sensor saturations Considers both time-invariant systems and time-varying systems Exploits newly developed techniques to handle the emerging mathematical and computational challenges
Author: Baocang Ding
Publisher: John Wiley & Sons
ISBN: 1119471311
Category : Science
Languages : en
Pages : 308
Get Book
Book Description
Model Predictive Control Understand the practical side of controlling industrial processes Model Predictive Control (MPC) is a method for controlling a process according to given parameters, derived in many cases from empirical models. It has been widely applied in industrial units to increase revenue and promoting sustainability. Systematic overviews of this subject, however, are rare, and few draw on direct experience in industrial settings. Assuming basic knowledge of the relevant mathematical and algebraic modeling techniques, the book’s title combines foundational theories of MPC with a thorough sense of its practical applications in an industrial context. The result is a presentation uniquely suited to rapid incorporation in an industrial workplace. Model Predictive Control readers will also find: Two-part organization to balance theory and applications Selection of topics directly driven by industrial demand An author with decades of experience in both teaching and industrial practice This book is ideal for industrial control engineers and researchers looking to understand MPC technology, as well as advanced undergraduate and graduate students studying predictive control and related subjects.
Author: Magdi Mahmoud
Publisher: BoD – Books on Demand
ISBN: 9535108751
Category : Computers
Languages : en
Pages : 260
Get Book
Book Description
This volume brings about the contemporary results in the field of discrete-time systems. It covers papers written on the topics of robust control, nonlinear systems and recent applications. Although the technical views are different, they all geared towards focusing on the up-to-date knowledge gain by the researchers and providing effective developments along the systems and control arena. Each topic has a detailed discussions and suggestions for future perusal by interested investigators.
Author: Yu Kang
Publisher: Springer
ISBN: 9811038600
Category : Technology & Engineering
Languages : en
Pages : 193
Get Book
Book Description
This book focuses on the stability analysis of Markovian jump systems (MJSs) with various settings and discusses its applications in several different areas. It also presents general definitions of the necessary concepts and an overview of the recent developments in MJSs. Further, it addresses the general robust problem of Markovian jump linear systems (MJLSs), the asynchronous stability of a class of nonlinear systems, the robust adaptive control scheme for a class of nonlinear uncertain MJSs, the practical stability of MJSs and its applications as a modelling tool for networked control systems, Markovian-based control for wheeled mobile manipulators and the jump-linear-quadratic (JLQ) problem of a class of continuous-time MJLSs. It is a valuable resource for researchers and graduate students in the field of control theory and engineering.
Author: Ding Baocang
Publisher: CRC Press
ISBN: 1439859671
Category : Technology & Engineering
Languages : en
Pages : 117
Get Book
Book Description
Modern Predictive Control explains how MPC differs from other control methods in its implementation of a control action. Most importantly, MPC provides the flexibility to act while optimizing—which is essential to the solution of many engineering problems in complex plants, where exact modeling is impossible. The superiority of MPC is in its numerical solution. Usually, MPC is employed to solve a finite-horizon optimal control problem at each sampling instant and obtain control actions for both the present time and a future period. However, only the current control move is applied to the plant. This complete, step-by-step exploration of various approaches to MPC: Introduces basic concepts of systems, modeling, and predictive control, detailing development from classical MPC to synthesis approaches Explores use of Model Algorithmic Control (MAC), Dynamic Matrix Control (DMC), Generalized Predictive Control (GPC), and Two-Step Model Predictive Control Identifies important general approaches to synthesis Discusses open-loop and closed-loop optimization in synthesis approaches Covers output feedback synthesis approaches with and without a finite switching horizon This book gives researchers a variety of models for use with one- and two-step control. The author clearly explains the variations between predictive control methods—and the root of these differences—to illustrate that there is no one ideal MPC and that one should remain open to selecting the best possible model in each unique circumstance.
