Abstract
With the rapid development of the multi-robot systems, formation control has become a fundamental challenge. Traditional approaches focus mainly on the design of control algorithms to realize specific formation patterns, while neglecting how to determine the desired formation. In this paper, the optimal formation problem based on shape theory is reformulated as a convex optimization problem. A predetermined time convergent zeroing neural dynamics (PDTZND) approach, derived from zeroing neural networks (ZNN), is proposed to efficiently solve this problem. The PDTZND approach ensures that the system error converges in a strict and predetermined time, which provides an efficient, accurate solution for optimal formation. In addition, the convergence of the proposed approach is rigorously analyzed by means of Lyapunov theory, and its validity and superiority are verified by numerical simulations and physical experiments.
| Original language | English |
|---|---|
| Pages (from-to) | 23757-23768 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Automation Science and Engineering |
| Volume | 22 |
| DOIs | |
| Publication status | Published - 2025 |
| MoE publication type | A1 Journal article-refereed |
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 62466019 and in part by the Hunan Provincial Innovation Foundation for Postgraduate under Grant CX20251614.
Keywords
- formation planning
- optimization
- predetermined time convergence
- Zeroing neural networks (ZNN)