# Multi-Objective Learning Model Predictive Control

arXiv: https://arxiv.org/abs/2405.11698 · HTML: https://mlsanity.com/paper/2405.11698/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2024.

## Built on by (most cited first, 1 of 1)

- [Deterministic Pareto-Optimal Policy Synthesis for Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2606.26397/) (2026)

## Abstract (arXiv)

Multi-Objective Learning Model Predictive Control is a novel data-driven control scheme which improves a linear system's closed-loop performance with respect to several convex 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.