Fixed-Time Optimization of Perturbed Multi-Agent Systems under the Resource Constraints
Source abstract
Abstract
In this paper, a novel fixed-time distributed optimization algorithm is proposed to solve the multi-agent collaborative optimization (MSCO) problem with local inequality constraints, global equation constraints and unknown disturbances. At first, a penalty function method is used to eliminate the local inequality constraints and transform the original problem into a problem without local constraints. Then, a novel three-stage control scheme is designed to achieve a robust fixed-time convergence. In the first stage, a fixed-time reaching law is given to completely eliminate the effect of unknown disturbances with the aid of the integral sliding mode control method; in the second stage, a suitable interaction strategy is provided such that the whole system could satisfy the global constraints in fixed-time; in the third stage, a fixed-time gradient optimization algorithm of the multi-agent system is presented, with which the states of all the agents will converge to the minimum value of the global objective in a fixed-time. Finally, the effectiveness of the proposed control strategy is verified in the problem of wind farm co-generation with 60 wind turbines.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Distributed Control Multi-Agent Systems · Adaptive Dynamic Programming Control · UAV Applications and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Automation, Hohai University, Nanjing 211100, China
Funding
National Natural Science Foundation of China · “SCBS” plan of Jiangsu Province
References (28)
Show source-provided references
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