Editorial Decentralized Modeling, Analysis, Control, and...
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Editorial Decentralized Modeling, Analysis, Control, and Application of Distributed Dynamic Systems Ning Cai, 1,2 Roberto Sabatini, 3 Xi-Wang Dong, 4 M. Junaid Khan, 5 and Yao Yu 6 1 School of Electrical Engineering, Northwest University for Nationalities, Lanzhou, China 2 Key Laboratory of National Language Intelligent Processing, Gansu, China 3 School of Engineering-Aerospace and Aviation Discipline, RMIT University, Melbourne, VIC, Australia 4 School of Automation Science and Electrical Engineering, Beihang University, Beijing, China 5 School of PN Engineering, National University of Sciences and Technology, Islamabad, Pakistan 6 School of Automation, University of Science and Technology Beijing, Beijing, China Correspondence should be addressed to Ning Cai; [email protected] Received 29 November 2016; Accepted 29 November 2016 Copyright © 2016 Ning Cai et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Distributed dynamic systems are large-scale systems con- stituted by numerous subsystems deployed in distributed manner. In many cases, there are three fundamental charac- teristics for these subsystems. e first characteristic is auton- omy. e subsystems are usually autonomous or semiau- tonomous and can oſten exist and operate independently. e second characteristic is homogeneity. e subsystems oſten share similarity and play relatively equal roles. e last and most important characteristic is interactivity. e subsystems should connect to a network topology collectively, such that they are able to communicate by exchanging information or substance and cooperate with each other. Since the beginning of the current century, studies on the modeling, simulation, analysis, and synthesis about dis- tributed dynamic systems have gradually formed a high- lighted direction in the area of control and systems science, meanwhile receiving more and more attention from increas- ing number of scientific and application disciplines. Relevant research includes time and frequency domain methods, state space analysis, filtering, prediction, optimization, system identification, robust control, and stochastic control, just to mention a few. is new trend can be attributed to the joint advancements of scientific methodology, engineering technology, and control theory. Along with the development of modern computing technique, there appear signs for the scientific methodology in systems science to return back to reductionism from holism, especially for modeling and analysis. In the meantime, mankind is rapidly stepping into a networked world. e notion of complex and dynamical net- works keeps on penetrating nearly all kinds of domains, rang- ing from communication networks, power networks, man- ufacturing networks, biological networks, and even social networks. For the networked distributed systems, the set of essential theoretical concepts of systems analysis and syn- thesis which are already fully developed on isolated systems such as stability, equilibrium, controllability/observability, and tracking control acquire new connotations and con- tents, which need to be extended profoundly. Moreover, methodologies based on networked configurations could also facilitate offering solutions to various difficult tasks. Nonetheless, extra complexity usually accompanies these systems due to the nonlinearities, distributed parameters and states, and high order. It is responsible and challenging for the control community to promote the advancement of both theories and technologies for modeling, analyzing, and controlling of networked distributed systems. is special issue is composed of seven research articles, representing some of the recent advances around the theme of decentralized modeling, analysis, or synthesis on either directly controlling various distributed dynamic systems or utilizing these systems to solve other problems. Most of these articles seek solutions for engineering tasks with different Hindawi Publishing Corporation Journal of Control Science and Engineering Volume 2016, Article ID 8985017, 2 pages http://dx.doi.org/10.1155/2016/8985017
Transcript of Editorial Decentralized Modeling, Analysis, Control, and...
EditorialDecentralized Modeling, Analysis, Control, and Application ofDistributed Dynamic Systems
Ning Cai,1,2 Roberto Sabatini,3 Xi-Wang Dong,4 M. Junaid Khan,5 and Yao Yu6
1School of Electrical Engineering, Northwest University for Nationalities, Lanzhou, China2Key Laboratory of National Language Intelligent Processing, Gansu, China3School of Engineering-Aerospace and Aviation Discipline, RMIT University, Melbourne, VIC, Australia4School of Automation Science and Electrical Engineering, Beihang University, Beijing, China5School of PN Engineering, National University of Sciences and Technology, Islamabad, Pakistan6School of Automation, University of Science and Technology Beijing, Beijing, China
Correspondence should be addressed to Ning Cai; [email protected]
Received 29 November 2016; Accepted 29 November 2016
Copyright © 2016 Ning Cai et al.This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Distributed dynamic systems are large-scale systems con-stituted by numerous subsystems deployed in distributedmanner. In many cases, there are three fundamental charac-teristics for these subsystems.The first characteristic is auton-omy. The subsystems are usually autonomous or semiau-tonomous and can often exist and operate independently.Thesecond characteristic is homogeneity. The subsystems oftenshare similarity and play relatively equal roles. The last andmost important characteristic is interactivity.The subsystemsshould connect to a network topology collectively, such thatthey are able to communicate by exchanging information orsubstance and cooperate with each other.
Since the beginning of the current century, studies onthe modeling, simulation, analysis, and synthesis about dis-tributed dynamic systems have gradually formed a high-lighted direction in the area of control and systems science,meanwhile receiving more and more attention from increas-ing number of scientific and application disciplines. Relevantresearch includes time and frequency domain methods, statespace analysis, filtering, prediction, optimization, systemidentification, robust control, and stochastic control, just tomention a few. This new trend can be attributed to thejoint advancements of scientific methodology, engineeringtechnology, and control theory. Along with the developmentof modern computing technique, there appear signs for thescientific methodology in systems science to return back
to reductionism from holism, especially for modeling andanalysis. In the meantime, mankind is rapidly stepping into anetworked world.The notion of complex and dynamical net-works keeps on penetrating nearly all kinds of domains, rang-ing from communication networks, power networks, man-ufacturing networks, biological networks, and even socialnetworks. For the networked distributed systems, the set ofessential theoretical concepts of systems analysis and syn-thesis which are already fully developed on isolated systemssuch as stability, equilibrium, controllability/observability,and tracking control acquire new connotations and con-tents, which need to be extended profoundly. Moreover,methodologies based on networked configurations couldalso facilitate offering solutions to various difficult tasks.Nonetheless, extra complexity usually accompanies thesesystems due to the nonlinearities, distributed parametersand states, and high order. It is responsible and challengingfor the control community to promote the advancement ofboth theories and technologies for modeling, analyzing, andcontrolling of networked distributed systems.
This special issue is composed of seven research articles,representing some of the recent advances around the themeof decentralized modeling, analysis, or synthesis on eitherdirectly controlling various distributed dynamic systems orutilizing these systems to solve other problems. Most of thesearticles seek solutions for engineering tasks with different
Hindawi Publishing CorporationJournal of Control Science and EngineeringVolume 2016, Article ID 8985017, 2 pageshttp://dx.doi.org/10.1155/2016/8985017
2 Journal of Control Science and Engineering
backgrounds. For instance, among them, two papers dealwith control strategies for maneuvering vehicles, two addressthe process control of steel plate manufacturing, one buildsneuron circuits, and one paper concerns the control ofbrushless DC motors. A brief description of the papersfollows.
In the paper “Decentralized Robust Adaptive OutputFeedback Stabilization for Interconnected Nonlinear Systemswith Uncertainties,” Q. Yang et al. synthesized a strategyof decentralized robust adaptive output feedback stabiliza-tion for a class of large-scale nonlinear systems comprisedof interconnected subsystems with uncertainties, based onthe adaptive nonlinear damping technique. Their schemeis implemented by integrating a robust output feedbackcontroller with a state observer, both being decentralized andadaptive, and can practically realize stabilization or tracking.
L. Yuan et al. investigated the synchronization reliabilityof neuronal circuits connected with ring type in the paper“Synchronization ofNeuronal Circuits with RingConnectionon PSpice.” In this paper, analog signals are converted intodigital signals, so that the synchronization rhythm and phasesynchronization can be detected from the sampled time seriesfor membrane potential of Hindmarsh-Rose neurons. It isconfirmed by the authors that the synchronization behaviorand stability of the network depend on the coupling intensitygreatly, and it never means stronger intensity can ensurestable synchronization under bursting state.
S. Shen and P. Zhou proposed a scheme for chaos syn-chronization of fractional-order brushless DC motors inthe paper entitled “Synchronization of the Fractional-OrderBrushless DC Motors Chaotic System.” The previous workof the authors has shown that, under certain conditions,the nonlinear model of brushless DC motors is a chaoticsystem of fractional-order, and synchronization is requiredbetween different systems. In the paper, the authors designeda feedback controller based on relative output informationto ensure asymptotic synchronizations and theoretically val-idated its effectiveness.
In the paper “A Novel Relative Navigation Control Strat-egy Based onRelation SpaceMethod forAutonomousUnder-groundArticulated Vehicles,” F. Dou et al. designed a schemefor relative navigation control of trackless articulated under-ground vehicles based on the relation space method. Anoting highlight of the technique is the employing of aself-organizing, competitive neural network to identify theinformation of the surrounding space of the vehicles, whichis important for programming an optimal driving directionwith the known spatial geometric relationships.
V. Pshikhopov et al. presented a decentralized controlscheme of homogeneous vehicles in obstructed environment.The information of obstacles is transformed into repellersbased on artificial potential field approach, with the unstablemodes being exploited to dynamically produce the appropri-ate repelling forces at different positions.
F. Zhang et al. developed a distributedly deployed tech-nological package of equipment for the intermediate coolingcontrol during hot rolling of steel plate in production line inthe paper “Development of Intermediate CoolingTechnologyand Its Control for Two-stand Plate Rolling.” The overall
framework of the package comprehensively combines feed-forward, feedback, and adaptive algorithms.
The paper “Rolling Force Prediction in Heavy PlateRolling Based on Uniform Differential Neural Network” byF. Zhang et al. provided a uniform differential evolution algo-rithm with fine performance in high dimensional functionoptimization for optimizing the structure and weight valueof neural network. The algorithm was used to improve theaccuracy of rolling force prediction in the process controlsystem of medium and heavy plate.
Acknowledgments
We would like to thank all the authors and co-authors whosubmitted their work for consideration to this special issue aswell as the anonymous reviewers for their efforts to ensure thestandard of published papers.
Ning CaiRoberto SabatiniXi-Wang Dong
M. Junaid KhanYao Yu
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