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Formation Tracking with Prescribed Performance and Collision Avoidance
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AI
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DOI：10.1007/s40313-026-01257-x.png)
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Abstract
En 中文
Performance-prescribed formation control represents a pivotal advancement in bridging theoretical frameworks with practical implementation for multi-agent cooperative control. However, existing control schemes predominantly focus on single performance metrics and/or neglect the unknowns in system models, thereby limiting their applicability in real-world systems. Aiming at these limitations, this paper pursues the formation tracking not only with the guarantee of prescribed performance but with a safe distance between each follower and the leader. Remarkably, the multi-agent systems under investigation permit severe unknowns and heterogeneities, reflected by almost completely unknown system nonlinearities and the unknown bound of the leader trajectory. Though the considered systems provide coarse model information, the desired formation tracking is achieved by employing the method of funnel control. Specifically, by utilizing the reciprocal of the distance between relative states and the performance boundary, a continuous control protocol (rather than one with the sign function in some related works) is designed. In particular, for the collision avoidance, the time-varying function that characterizes the performance boundary is chosen to satisfy a condition related to the safe distance and desired formation offset. With the aid of a subtly selected Lyapunov function candidate, it turns out that with the designed protocol, the desired formation tracking is achieved. An example for simplified drone planar motion model is given finally to illustrate the effectiveness of the proposed control scheme.
Keywords:
Unknown heterogeneous nonlinearities
Performance-prescribed formation tracking
Collision avoidance
Funnel control
Fully distributed protocol
Journal
J
IF:
1.3
Papers:
92
Citations:
1.1K
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Cited Papers