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Neural controllers for plug-and-play networked systems can now be trained with unconstrained gradient descent while guaranteeing stability, overcoming a fundamental trade-off.
A DMPC framework in which each agent is assigned a connectivity contract: a local region prescribing where its predicted positions may lie over the prediction horizon, and it is proved that the resulting closed-loop system maintains connectivity, avoids collisions, and respects local state and input constraints.