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Sampling-Optimization-Based Adaptive Formation Tracking With Trajectory Protection
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AI
DOI：10.1109/JSYST.2023.3305358.png)
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Abstract
En 中文
This article studies an adaptive formation tracking problem with trajectory protection. The adaptive formation tracking problem for each sampling tracking point is transformed into a distributed constrained optimization problem. A distributed-optimization-based strategy is proposed to search for the optimal formation configuration. Considering the trajectory protection, some auxiliary variables are transmitted directly in the communication process of the multiagent system rather than the sensitive information, i.e., the sampling tracking point. Interestingly, before the achievement of the optimal formation configuration, the information of the sampling tracking point will not be disclosed, even though the eavesdropper is omnipotent, and meanwhile, the accuracy of tracking is not influenced. Then, a sampling-optimization-based adaptive formation tracking with trajectory protection (SOAFT-TP) algorithm is proposed to achieve adaptive formation tracking in the obstacle-existence environment with trajectory protection. It modifies the formation configuration adaptively to respond to the real-time environment by updating the constraint parameters online. Finally, the theoretical results of the SOAFT-TP algorithm are verified by a simulation example.
Keywords:
Adaptive formation tracking
distributed-optimization-based strategy
sampling-optimization-based adaptive formation tracking with trajectory protection (SOAFT-TP) algorithm
trajectory protection
Journal
I
IF:
2.4
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4.5K
Citations:
387
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