Distributed Performance Boosting through Neural Control for Plug-and-Play Networks
We address the problem of improving the performance of pre-stabilized networked systems subject to plug-and-play (PnP) operations, namely additions or removals of subsystems. While distributed baseline controllers can preserve stability after a PnP transition, augmenting them with expressive neural policies can invalidate the formal stability guarantees. To overcome this issue, we build on recently proposed Performance Boosting controllers based on the Internal Model Control (IMC) principle, which reduce closed-loop stability to a finite $\ell_2$-gain requirement on an auxiliary operator. In the distributed setting, this operator is formed by interconnecting local neural operators that ex...
| Authors | Danilo Saccani, Leonardo Massai, Giancarlo Ferrari-Trecate |
|---|---|
| Primary category | eess.SY |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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