  Multi-Objective Learning Model Predictive Control is a novel data-driven
control scheme which improves a system's closed-loop performance with respect
to several control objectives over iterations of a repeated task. At each task
iteration, collected system data is used to construct terminal components of a
Model Predictive Controller. The formulation presented in this paper ensures
that closed-loop control performance improves between successive iterations
with respect to each objective. We provide proofs of recursive feasibility and
performance improvement, and show that the converged policy is Pareto optimal.
Simulation results demonstrate the applicability of the proposed approach.
