Frontiers of Information Technology & Electronic Engineering
>> 2024,
Volume 25,
Issue 2
doi:
10.1631/FITEE.2300568
Event-triggered distributed optimization formodel-free multi-agent systems
Affiliation(s): College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China; College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China; less
Received: 2023-08-22
Accepted: 2024-02-23
Available online: 2024-02-23
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Abstract
In this paper, the problem is investigated for a class of general nonlinear modelfree . The dynamical model of each agent is unknown and only the input/output data are available. A method is employed, by which the original unknown nonlinear system is equivalently converted into a dynamic linearized model. An event-triggered consensus scheme is developed to guarantee that the consensus error of the outputs of all agents is convergent. Then, by means of the distributed gradient descent method, a novel event-triggered model-free adaptive algorithm is put forward. Sufficient conditions are established to ensure the consensus and optimality of the addressed system. Finally, simulation results are provided to validate the effectiveness of the proposed approach.
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