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Adaptive Dynamic Programming with Applications in Optimal Control de Derong Liu

Adaptive Dynamic Programming with Applications in Optimal Control: Advances in Industrial Control

Autor Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li
en Limba Engleză Hardback – 12 ian 2017

Observăm în Adaptive Dynamic Programming with Applications in Optimal Control o structură progresivă riguroasă, concepută să ghideze cititorul de la fundamentele teoretice către implementări complexe în ingineria proceselor. Lucrarea debutează cu o retrospectivă istorică și o revizuire a conceptelor de bază, stabilind cadrul necesar pentru cele două secțiuni tehnice majore. În prima parte, autorii analizează sistemele în timp continuu, abordând controlul sistemelor neliniare afine și neafine, extinzând ulterior discuția către controlul robust și stabilizarea descentralizată a sistemelor interconectate. A doua parte a volumului se concentrează pe sistemele în timp discret, unde reținem o analiză detaliată a iterației valorii și a iterației politicii, incluzând demonstrații matematice pentru limitele de eroare și stabilitate.

Dacă Adaptive Dynamic Programming for Control de Huaguang Zhang v-a oferit cadrul teoretic general pentru sistemele cu întârziere sau comutate, această carte oferă instrumentele practice și analizele de convergență necesare pentru implementarea algoritmilor în medii industriale reale. Subliniem că, spre deosebire de alte titluri din domeniu, autorii nu se limitează la teorie, dedicând ultima secțiune aplicațiilor practice dezvoltate în propriile laboratoare, precum programarea energiei regenerabile în rețele inteligente sau controlul proceselor de gazeificare.

Această lucrare reprezintă o evoluție firească în opera lui Derong Liu. Dacă în Stability of Dynamical Systems autorul se concentra pe modele matematice fundamentale, iar în Frontiers of Intelligent Control and Information Processing explora complexitatea datelor, volumul de față sintetizează aceste direcții într-o metodologie aplicată de control inteligent. Organizarea pe capitole reflectă o tranziție logică de la algoritmi online de învățare pentru jocuri cu mai mulți jucători la soluții specifice pentru industria chimică și de alimentare cu energie.

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Specificații

ISBN-13: 9783319508139
ISBN-10: 331950813X
Pagini: 594
Ilustrații: XXX, 594 p. 203 illus., 175 illus. in color.
Dimensiuni: 155 x 235 x 35 mm
Greutate: 1.3 kg
Ediția:1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria Advances in Industrial Control

Locul publicării:Cham, Switzerland

De ce să citești această carte

Recomandăm această carte cercetătorilor și inginerilor care au nevoie de o fundamentare matematică solidă pentru aplicarea programării dinamice adaptive. Cititorul câștigă acces la algoritmi verificați de control pentru sisteme neliniare complexe și, mai important, înțelege cum pot fi aceștia aplicați în sectoare critice precum energia regenerabilă sau prelucrarea gazelor, trecând dincolo de simularile teoretice spre implementări industriale robuste.

Despre autor

Derong Liu este un specialist recunoscut în domeniul sistemelor de control și al inteligenței computaționale, contribuind semnificativ la literatura tehnică prin lucrări fundamentale despre stabilitatea sistemelor dinamice și rețelele neuronale. Alături de coautorii Qinglai Wei și Ding Wang, acesta face parte dintr-o elită academică ce explorează intersecția dintre controlul optimal și învățarea prin întărire. Expertiza sa este consolidată de implicarea în conferințe de prestigiu precum ICONIP, experiența sa reflectându-se în rigoarea cu care tratează sistemele neliniare și procesarea informației în context industrial.

Cuprins

History of Adaptive Dynamic Programming.- Part I: Continuous-Time Systems.- Optimal Control of Continuous-Time Affine Nonlinear Systems.- Optimal Control of Nonaffine Continuous-Time Systems.- Robust and Guaranteed Cost Control of Continuous-Time Nonlinear Systems.- Decentralized Stabilization and Control of Nonlinear Interconnected Systems.- Online Synchronous Optimal Learnign Algorithms for Multiplayer Nonzero-Sum Games with Unknown Dynamics.- Part II: Discrete-Time Systems.- Value Iteration Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems.- Finite Approximation Error-Based Value Iteration for Adaptive Dynamic Programming.- Policy Iteration for Optimal Control of Discrete-Time Nonlinear Systems.- Generalized Policy Iteration Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems.- Error-Bound Analysis of Adaptive Dynamic Programming Algorithms for Solving Undiscounted Optimal Control Problems.- Part III: Applications.- Adaptive Dynamic Programming for Renewable Energy Scheduling and Battery Management in Smart Homes.- Adaptive Dynamic Programming for Optimal Tracking Control of a Coal Gasification Process.- Data-Driven Neuro-Optimal Temperature Control of Water–Gas Shift Reaction.

Notă biografică

Derong Liu received the Ph.D. degree in electrical engineering from the University of Notre Dame, Indiana, USA, in 1994. Dr. Liu was a Staff Fellow with General Motors Research and Development Center, from 1993 to 1995. He was an Assistant Professor with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, from 1995 to 1999. He joined the University of Illinois at Chicago in 1999, and became a Full Professor of Electrical and Computer Engineering and of Computer Science in 2006. He was selected for the “100 Talents Program” by the Chinese Academy of Sciences in 2008. He has published 16 books. Dr. Liu was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems, from 2010 to 2015. Currently, he is an elected AdCom member of the IEEE Computational Intelligence Society, he is the Editor-in-Chief of Artificial Intelligence Review, and he serves as the Vice President of Asia-Pacific Neural Network Society. He was the General Chair of 2014 IEEE World Congress on Computational Intelligence and was the General Chair of 2016 World Congress on Intelligent Control and Automation. He received the Faculty Early Career Development Award from the National Science Foundation in 1999, the University Scholar Award from University of Illinois from 2006 to 2009, the Overseas Outstanding Young Scholar Award from the National Natural Science Foundation of China in 2008, and the Outstanding Achievement Award from Asia Pacific Neural Network Assembly in 2014. He is a Fellow of the IEEE and a Fellow of the International Neural Network Society.  
Qinglai Weie="font-family: 'Courier New';"> received the Ph.D. degree in control theory and control engineering, from the Northeastern University, Shenyang, China, in 2009. From 2009 to 2011, he was a postdoctoral fellow with The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. He is currently a Professor of the institute. Prof. Wei is an Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics: Systems, Information Sciences, Neurocomputing, Optimal Control Applications and Methods, and Acta Automatica Sinica, and was an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems during 2014–2015. He was the organizing committee member of several international conferences. He was recipient of Asia Pacific Neural Networks Society (APNNS) young researcher award in 2016. He was a recipient of the Outstanding Paper Award of Acta Automatica Sinica in 2011 and Zhang Siying Outstanding Paper Award of Chinese Control and Decision Conference (CCDC) in 2015.
Ding Wang received the Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2012. He is currently an Associate Professor with The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. He has been a Visiting Scholar with the Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA, since 2015. His research interests include adaptive and learning systems, intelligent control, and neural networks. He has published over 70 journal and conference papers, and coauthored two monographs. He was the organizing committee memberof several international conferences. He was recipient of the Excellent Doctoral Dissertation Award of Chinese Academy of Sciences in 2013. He serves as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems and Neurocomputing. He is a member of IEEE, Asia-Pacific Neural Network Society (APNNS), and CAA. 

Xiong Yang received the Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2014. Dr. Yang was a recipient of the Excellent Award of Presidential Scholarship of Chinese Academy of Sciences in 2014. He was an Assistant Professor with The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, from 2014 to 2016. He is currently an Associate Professor with School of Electrical Engineering and Automation, Tianjin University.
Hongliang Li received the Ph.D. degree in control theory and control engineering from the University of Chinese Academy of Sciences in 2015. Dr. Li was a Research Scientist with IBM Research - China, Beijing, from 2015 to 2016. He joined Tencent Inc., Shenzhen, China, in 2016. He has published more than 10 journal papers on adaptive dynamic programming and reinforcement learning.

Textul de pe ultima copertă

This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins with a thorough background review of ADP making sure that readers are sufficiently familiar with the fundamentals. In the core of the book, the authors address first discrete- and then continuous-time systems. Coverage of discrete-time systems starts with a more general form of value iteration to demonstrate its convergence, optimality, and stability with complete and thorough theoretical analysis. A more realistic form of value iteration is studied where value function approximations are assumed to have finite errors. Adaptive Dynamic Programming also details another avenue of the ADP approach: policy iteration. Both basic and generalized forms of policy-iteration-based ADP are studied with complete and thorough theoretical analysis in terms of convergence, optimality, stability, and error bounds. Among continuous-time systems, the control of affine and nonaffine nonlinear systemsis studied using the ADP approach which is then extended to other branches of control theory including decentralized control, robust and guaranteed cost control, and game theory. In the last part of the book the real-world significance of ADP theory is presented, focusing on three application examples developed from the authors’ work:

• renewable energy scheduling for smart power grids;
• coal gasification processes; and
• water–gas shift reactions.

Researchers studying intelligent control methods and practitioners looking to apply them in the chemical-process and power-supply industries will find much to interest them in this thorough treatment of an advanced approach to control.

Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Caracteristici

Demonstrates the power of adaptive dynamic programming in giving a uniform treatment of affine and nonaffine nonlinear systems including regulator and tracking control Demonstrates the flexibility of adaptive dynamic programming, extending it to various fields of control theory Shows the reader how to bring the theoretical demonstrations into the real world with three application examples Includes supplementary material: sn.pub/extras