Abstract
This article focuses on the distributed quantized optimization problem of continuous-time multiagent systems (MASs) over switching graphs. By proposing a dynamic encoding–decoding scheme, a distributed protocol via sampled and quantized data is developed, which can obtain an exact optimal solution, rather than an approximate optimal solution. Compared with existing works on quantized distributed optimization of MASs, the protocol presented in this article does not require the global information on the communication graph or the initial state. Besides, in this article, the gradients of the cost functions are not required to be bounded functions. A simulation example is finally presented to illustrate the effectiveness of the proposed distributed protocol.
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