Shared-Partition Multi-Output Hinge Regression Trees for Explicit MPC Distillation in Chassis Control

This letter presents Shared-Partition Multi-Output Hinge Regression Trees (SP-HRT) for explicit MPC distillation. Unlike independent single-output trees, SP-HRT learns a vector-valued tree with a common state-space partition, ensuring consistent switching across coupled control channels. We provide a shared-partition approximation result and characterize the regionwise affine closed-loop dynamics of this policy. On an 8-state vehicle chassiscontrol task, SP-HRT yields a compact and fast learned explicit policy among the considered baselines. Paired with a predictive monitor for selective online-MPC fallback, SP-HRT recovers online-MPC-level feasibility at the nominal operating point while substantially reducing average deployment latency.