Topology-Preserving Second-Order Consensus: A Strategic Compensation Approach
Published in Conference on Decision and Control, 2023
The interaction topology plays a significant role in the collaboration of multi-agent systems. How to preserve the topology against inference attacks has become an imperative task for security concerns. In this paper, we propose a distributed topology-preserving algorithm for second-order multiagent systems by adding noisy inputs. The major novelty is that we develop a strategic compensation approach to overcome the noise accumulation issue in the second-order dynamic process while ensuring the exact second-order consensus. Specifically, we design two distributed compensation strategies that make the topology more invulnerable against inference attacks. Furthermore, we derive the relationship between the inference error and the number of observations by taking the ordinary least squares estimator as a benchmark. Extensive simulations are conducted to verify the topology-preserving performance of the proposed algorithm.
Recommended citation: Wang, Zitong, Yushan Li, Xiaoming Duan, and Jianping He. “Topology-Preserving Second-Order Consensus: A Strategic Compensation Approach.” In 2023 62nd IEEE Conference on Decision and Control (CDC), pp. 399-404. IEEE, 2023.