Learning Model Predictive Control for iterative tasks. A Data-Driven Control Framework

TL;DR

This paper introduces a data-driven Learning Model Predictive Controller for iterative tasks that improves performance over iterations by learning from past data, ensuring safety and efficiency.

Contribution

It proposes a novel reference-free LMPC framework that constructs terminal sets and costs from previous trajectories to guarantee recursive feasibility and performance improvement.

Findings

Simulation results confirm effectiveness of the control logic.

Performance improves with each iteration.

Guarantees safety and non-increasing cost.

Abstract

A Learning Model Predictive Controller (LMPC) for iterative tasks is presented. The controller is reference-free and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and non-increasing performance at each iteration. The paper presents the control design approach, and shows how to recursively construct terminal set and terminal cost from state and input trajectories of previous iterations. Simulation results show the effectiveness of the proposed control logic.