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Distributed Robust Optimization Algorithms Over Uncertain Network Graphs
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AI
DOI：10.1109/TCYB.2020.3027415.png)
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Abstract
En 中文
This article investigates the robustness issues of a set of distributed optimization algorithms, which aim to approach the optimal solution to a sum of local cost functions over an uncertain network. The uncertain communication network consists of transmission channels perturbed by additive deterministic uncertainties, which can describe quantization and transmission errors. A new robust initialization-free algorithm is proposed for the distributed optimization problem of multiple Euler-Lagrange systems, and the explicit relationship of the feedback gain of the algorithm, the communication topology, the properties of the cost function, and the radius of the channel uncertainties is established in order to reach the optimal solution. This result provides a sufficient condition for the selection of the feedback gain when the uncertainty size is less than the unity. As a special case, we discuss the impact of communication uncertainties on the distributed optimization algorithms for first-order integrator networks.
Keywords:
Cost function
Uncertainty
Communication networks
Robustness
Protocols
Vehicle dynamics
Communication channel
cooperative control
distributed optimization
robustness
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W