Modeling Intelligent Control of Distributed Cooperative Inferencing
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Modeling Intelligent Control of Distributed Cooperative Inferencing

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Published by Storming Media .
Written in English

Subjects:

  • TEC025000

Book details:

The Physical Object
FormatSpiral-bound
ID Numbers
Open LibraryOL11852425M
ISBN 101423581911
ISBN 109781423581918

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AFIT/DS/ENG/ Modeling Intelligent Control of Distributed Cooperative Inferencing DISSERTATION Edward Michael Williams Major, USAF AFIT/DS/ENG/ Illustrative example: command and control of networked vehicles 4 Dimensions of cooperative control 5 Distributed control and computation 5 Adversarial interactions 11 Uncertain evolution 14 Complexity management 15 Future directions 16 Acknowledgements 17 References 17 Part II Distributed Control and. Book. Jan ; J. -L. Peterson A Petri-net coordination model for an intelligent mobile robot This paper describes the design and implementation of distributed cooperative control of Author: Gen'ichi Yasuda.   This dimensional decomposition allows the reader to assess the multi-faceted landscape of cooperative control. Cooperative Control of Distributed Multi-Agent Systems is organized into four main themes, or dimensions, of cooperative control: distributed control and computation, adversarial interactions, uncertain evolution and complexity management.

Abstract In this paper, the distributed cooperative control problem is considered for multiple type (1,2) nonholonomic mobile robots. Firstly, a local change of coordinates and feedback is proposed to transform the original nonholonomic system to a new trans-formed system. Secondly, a distributed controller for.   This paper introduces a distributed control scheme tailor-made to the task of letting a swarm of mobile robots push an object through a planar environment. Crucially, there is no centralized control instance or inter-robot hierarchy, and therefore, all decisions are made in a distributed manner. A cooperative policy iteration algorithm is presented to achieve the optimal distributed synchronization control law for the agent of each node, where the coupled Hamilton-Jacobi equations for optimal synchronization control of heterogeneous multiagent differential games can be avoided. in the main areas of this book, viz. Control Systems, Modeling, Computer Science, IT and Intelligent distributed computing will become the base for the growth of an innovative generation of.

Distributed Robust Cooperative Control for Multi-Agent Systems with Matching Uncertainties With Zhongkui Li, Duan Zhisheng This chapter considers the distributed consensus problem of multiagent systems with identical nominal linear dynamics but subject to different matching uncertainties. Yalin Wang received her Ph.D. in Control Science and Engineering from Central South University, China in , and was with Shenzhen ZTE corporation from to She is currently an associate professor in the School of Information Science & Engineering, Central South University. Her research interests include modeling and optimal control of complex industrial process, intelligent control.   He works in feedback control, intelligent systems, cooperative control systems, and nonlinear systems. He is author of 7 US patents, numerous journal special issues, journal papers, and 20 books, including Optimal Control, Aircraft Control, Optimal Estimation, and Robot Manipulator Control which are used as university textbooks worldwide.   Manufacturing control model describes production process as transformation process of planned resource volume R 0 (t) neccesary for product manufacturing into planned output N 0 (t) and output intensity N ˙ 0 (t), and also process of production ce-based control in the form of feedback is in the correcting of factual output intensity N ˙ (t) according to distance value ɛ N (t.