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| 001 | 978-981-15-3231-3 | ||
| 003 | DE-He213 | ||
| 005 | 20240423125309.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 200313s2020 si | s |||| 0|eng d | ||
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_a9789811532313 _9978-981-15-3231-3 |
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_a10.1007/978-981-15-3231-3 _2doi |
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| 050 | 4 | _aTJ210.2-211.495 | |
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_a629.892 _223 |
| 100 | 1 |
_aZhang, Yinyan. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 245 | 1 | 0 |
_aMachine Behavior Design And Analysis _h[electronic resource] : _bA Consensus Perspective / _cby Yinyan Zhang, Shuai Li. |
| 250 | _a1st ed. 2020. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2020. |
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| 300 |
_aXVII, 183 p. 44 illus., 38 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _aChapter 1: Introduction to Collective Machine Behavior -- Chapter 2: Second-Order Min-Consensus -- Chapter 3: Consensus of High-Order Discrete-Time Multi-Agent Systems -- Chapter 4: Continuous-Time Biased Min-Consensus -- Chapter 5: Discrete-Time Biased Min-Consensus -- Chapter 6: Biased Consensus Based Distributed Neural Network -- Chapter 7: Predictive Suboptimal Consensus -- Chapter 8: Adaptive Near-Optimal Consensus. | |
| 520 | _aIn this book, we present our systematic investigations into consensus in multi-agent systems. We show the design and analysis of various types of consensus protocols from a multi-agent perspective with a focus on min-consensus and its variants. We also discuss second-order and high-order min-consensus. A very interesting topic regarding the link between consensus and path planning is also included. We show that a biased min-consensus protocol can lead to the path planning phenomenon, which means that the complexity of shortest path planning can emerge from a perturbed version of min-consensus protocol, which as a case study may encourage researchers in the field of distributed control to rethink the nature of complexity and the distance between control and intelligence. We also illustrate the design and analysis of consensus protocols for nonlinear multi-agent systems derived from an optimal control formulation, which do not require solving a Hamilton-Jacobi-Bellman (HJB) equation. The book was written in a self-contained format. For each consensus protocol, the performance is verified through simulative examples and analyzed via mathematical derivations, using tools like graph theory and modern control theory. The book’s goal is to provide not only theoretical contributions but also explore underlying intuitions from a methodological perspective. | ||
| 650 | 0 | _aRobotics. | |
| 650 | 0 | _aMultiagent systems. | |
| 650 | 1 | 4 | _aRobotics. |
| 650 | 2 | 4 | _aMultiagent Systems. |
| 700 | 1 |
_aLi, Shuai. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811532306 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811532320 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811532337 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-15-3231-3 |
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| 942 | _cSPRINGER | ||
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