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Distributed Model Predictive Control for Plant-Wide Systems
DISTRIBUTED MODEL PREDICTIVE CONTROL FOR PLANT-WIDE SYSTEMS In this book, experienced researchers gave a thorough explanation of distributed model predictive control (DMPC): its basic concepts, technologies, and implementation in plant-wide systems. Known for its error tolerance, high flexibility, and good dynamic performance, DMPC is a popular topic in the control field and is widely applied in many industries. To efficiently design DMPC systems, readers will be introduced to several categories of coordinated DMPCs, which are suitable for different control requirements, such as network connectivity, error tolerance, performance of entire closed-loop systems, and calculation of speed. Various real-life industrial applications, theoretical results, and algorithms are provided to illustrate key concepts and methods, as well as to provide solutions to optimize the global performance of plant-wide systems. * Features system partition methods, coordination strategies, performance analysis, and how to design stabilized DMPC under different coordination strategies. * Presents useful theories and technologies that can be used in many different industrial fields, examples include metallurgical processes and high-speed transport. * Reflects the authors' extensive research in the area, providing a wealth of current and contextual information. Distributed Model Predictive Control for Plant-Wide Systems is an excellent resource for researchers in control theory for large-scale industrial processes. Advanced students of DMPC and control engineers will also find this as a comprehensive reference text.
eBook
Distributed parameter identification algorithm for large‐scale interconnected systems
This paper deals with parameter estimation problem of large‐scale systems. A recursive distributed parameter estimation algorithm, based on the minimization of the prediction estimation error method, is developed. Specifically, the class of large‐scale systems that are composed of several interconnected sub‐systems is considered. Each interconnected sub‐system is modelled by a linear discrete‐time state space mathematical model with unknown parameters. The convergence analysis is then achieved using the Lyapunov approach. The theoretical analysis and simulation results prove the effectiveness of the proposed algorithm. A recursive method is presented for parameter identification of large‐scale interconnected systems. The proposed algorithm is dedicated to systems described by a state space model. Simulation results prove that this algorithm has a good performance.
Journal Article
Hierarchical game theoretical distributed adaptive control for large scale multi‐group multi‐agent system
This paper introduces a distributed adaptive formation control for large‐scale multi‐agent systems (LS‐MAS) that addresses the heavy computational complexity and communication traffic challenges while directly extending conventional distributed control from small scale to large scale. Specifically, a novel hierarchical game theoretic algorithm is developed to provide a feasible theory foundation for solving LS‐MAS distributed optimal formation problem by effectively integrating the mean‐field game (MFG), the Stackelberg game, and the cooperative game. In particular, LS‐MAS is divided into multiple groups geographically with each having one group leader and a significant amount of followers. Then, a cooperative game is used among multi‐group leaders to formulate distributed inter‐group formation control for leaders. Meanwhile, an MFG is adopted for a large number of intra‐group followers to achieve the collective intra‐group formation while a Stackelberg game is connecting the followers with their corresponding leader within the same group to achieve the overall LS‐MAS multi‐group formation behavior. Moreover, a hybrid actor–critic‐based reinforcement learning algorithm is constructed to learn the solution of the hierarchical game‐based optimal distributed formation control. Finally, to show the effectiveness of the presented schemes, numerical simulations and Lyapunov analysis is performed.
Journal Article
Decentralized Fuzzy Filter Design for Discrete‐Time Nonlinear Large‐Scale Systems with Uncertain Interconnections
This paper presents decentralized fuzzy filter design techniques for discrete‐time nonlinear large‐scale systems with uncertain interconnections. The subsystems of the nonlinear large‐scale system and the decentralized filters are represented using Takagi–Sugeno (T–S) fuzzy model. Based on the closed‐loop system with estimation error, the decentralized fuzzy filter design problem is addressed to guarantee the H∞ $H_{\\infty }$filtering performance. A sufficient condition is derived to satisfy both the stability condition and the H∞ $H_{\\infty }$filtering performance, and its constructive condition is reformulated into various linear matrix inequality (LMI) formats. Finally, the effectiveness of the proposed methods and procedures is demonstrated through a simulation example. The main novelties of this paper can be broadly summarized into two points: First, the decentralized fuzzy filter design techniques are effectively proposed for uncertain interconnection by using the maximum interconnection bound. Second, the decentralized fuzzy filters are designed when the premise variable is not only measurable but also non‐measurable. Figure 1 shows the estimated output of the interconnected mass‐spring‐damper mechanical system and the results of the decentralized fuzy filter. The results in the figure demonstrate the performance of the proposed filter.
Journal Article
Adaptive fuzzy decentralised output feedback control of pure-feedback large-scale stochastic non-linear systems with unknown dead zone
In this study, a robust adaptive fuzzy decentralised backstepping output feedback control approach is proposed for a class of uncertain non-linear stochastic large-scale systems in pure-feedback form. The non-linear large-scale systems under study have unknown non-linear functions, unknown dead-zone and immeasurable states. Fuzzy logic systems are used to approximate the unknown non-linear functions, and a K-filters state observer is designed for estimating the unmeasured states. Based on the information of the bounds of the dead-zone slopes as well as treating the time-varying inputs coefficients as a system uncertainty, a robust adaptive fuzzy decentralised output feedback control approach is constructed via the backstepping recursive design technique. It is shown that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semi-globally uniformly ultimately bounded in probability, and the observer errors and the output of the system can be regulated to a small neighbourhood of the origin by choosing design parameters appropriately. A simulation example is provided to show the effectiveness of the proposed approach.
Journal Article
Selected Topics in Column Generation
Dantzig-Wolfe decomposition and column generation, devised for linear programs, is a success story in large-scale integer programming. We outline and relate the approaches, and survey mainly recent contributions, not yet found in textbooks. We emphasize the growing understanding of the dual point of view, which has brought considerable progress to the column generation theory and practice. It stimulated careful initializations, sophisticated solution techniques for the restricted master problem and subproblem, as well as better overall performance. Thus, the dual perspective is an ever recurring concept in our \"selected topics.\"
Journal Article
Reliability of Large and Complex Systems
Reliability of Large and Complex Systems, previously titled Reliability of Large Systems, is an innovative guide to the current state and reliability of large and complex systems.In addition to revised and updated content on the complexity and safety of large and complex mechanisms, this new edition looks at the reliability of nanosystems, a key.
eBook
Stability and Control of Large-Scale Dynamical Systems
Modern complex large-scale dynamical systems exist in virtually every aspect of science and engineering, and are associated with a wide variety of physical, technological, environmental, and social phenomena, including aerospace, power, communications, and network systems, to name just a few. This book develops a general stability analysis and control design framework for nonlinear large-scale interconnected dynamical systems, and presents the most complete treatment on vector Lyapunov function methods, vector dissipativity theory, and decentralized control architectures. Large-scale dynamical systems are strongly interconnected and consist of interacting subsystems exchanging matter, energy, or information with the environment. The sheer size, or dimensionality, of these systems necessitates decentralized analysis and control system synthesis methods for their analysis and design. Written in a theorem-proof format with examples to illustrate new concepts, this book addresses continuous-time, discrete-time, and hybrid large-scale systems. It develops finite-time stability and finite-time decentralized stabilization, thermodynamic modeling, maximum entropy control, and energy-based decentralized control. This book will interest applied mathematicians, dynamical systems theorists, control theorists, and engineers, and anyone seeking a fundamental and comprehensive understanding of large-scale interconnected dynamical systems and control.
eBook
An Overview of Phonetic Encoding Algorithms
This paper presents an overview of the phonetic encoding algorithms designed to determine the similarity of words in sound (pronunciation). Phonetic encoding algorithms are divided into the algorithms for comparing words and the algorithms for determining the distance between words. Word comparison algorithms, such as SoundEx, NYSIIS, Daitch–Mokotoff, Metaphone, and Polyphone, as well as algorithms for determining the distance between words, such as Levenshtein, Jaro, and N-grams, are described. For each algorithm, the advantages and shortcomings are discussed, and an analog for the Russian language is given. For eliminating the common shortcomings of phonetic encoding algorithms, the idea suggested in this paper is to use not the letter sequences of words, but the sequences of their elementary sounds. In this case, word recognition, record linkage, and word indexing by sounds are expected to improve.
Journal Article