We propose a data-driven Model Predictive Control (MPC) framework that employs a transformer encoder to generate multi-step predictions. To handle the nonconvex attention mechanism, we derive difference of convex (DC) representations of the transformer encoder components and embed them in a successive convex programming (SCP) iteration. Recursive feasibility and convergence of the SCP iterates are guaranteed, and each iterate yields a solution estimate satisfying the problem constraints. Under mild assumptions, the SCP iteration converges to a locally optimal solution of the MPC problem. The approach is illustrated on a benchmark nonlinear control problem.
@article{arxiv.2605.14846,
title = {Successive convex optimization for transformer encoder model predictive control},
author = {Xingxiao Chen and Mark Cannon},
journal= {arXiv preprint arXiv:2605.14846},
year = {2026}
}