Distributed extremum‐seeking based resource allocation algorithm with input dead‐zone
Summary This paper studies distributed resource allocation problem for agents with input dead‐zone, which is not considered in the existing work. At first, the primal problem is transformed to an auxiliary problem by using the exact penalty method to deal with local inequality constraints. It is ass...
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| Published in | International journal of robust and nonlinear control Vol. 33; no. 6; pp. 3947 - 3960 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bognor Regis
Wiley Subscription Services, Inc
01.04.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1049-8923 1099-1239 |
| DOI | 10.1002/rnc.6593 |
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| Summary: | Summary
This paper studies distributed resource allocation problem for agents with input dead‐zone, which is not considered in the existing work. At first, the primal problem is transformed to an auxiliary problem by using the exact penalty method to deal with local inequality constraints. It is assumed that the explicit expressions of cost functions and local inequality constraints are unknown to agents but the values of the cost and constraint functions can be obtained. Under such a setup, the extremum seeking control is used to estimate the gradient information. Thus, to obtain the optimal allocation, a novel distributed algorithm is designed by the virtue of the extremum seeking control and a dynamic compensating mechanism which is used to handle the effects of the input dead‐zone. Due to a two time‐scale structure of the designed distributed algorithm, the semi‐globally practically asymptotical convergence of all agents' decisions to the optimal allocation is obtained by the singular perturbation technique. Finally, numerical examples of economic dispatch in smart grids are given to verify the effectiveness of our proposed method. |
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| Bibliography: | Funding information National Natural Science Foundation of China, Grant/Award Number: 62263031; Natural Science Foundation of Xinjiang Province, Grant/Award Number: 2022D01C694 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1049-8923 1099-1239 |
| DOI: | 10.1002/rnc.6593 |