RLO-MPC: Robust Learning-Based Output Feedback MPC for Improving the Performance of Uncertain Systems in Iterative Tasks

TL;DR

This paper introduces RLO-MPC, a novel control method combining robust learning-based MPC with output feedback to handle uncertainties and partial observations in iterative tasks, ensuring stability and improved performance.

Contribution

It extends previous RL-MPC frameworks to cases with partial, noisy observations, providing theoretical guarantees and practical validation for uncertain systems.

Findings

Ensures recursive feasibility and stability in uncertain, partially observed systems.

Proves theoretical performance guarantees over iterations.

Demonstrates effectiveness through simulation and quadrotor experiments.

Abstract

In this work we address the problem of performing a repetitive task when we have uncertain observations and dynamics. We formulate this problem as an iterative infinite horizon optimal control problem with output feedback. Previously, this problem was solved for linear time-invariant (LTI) system for the case when noisy full-state measurements are available using a robust iterative learning control framework, which we refer to as robust learning-based model predictive control (RL-MPC). However, this work does not apply to the case when only noisy observations of part of the state are available. This limits the applicability of current approaches in practice: First, in practical applications we typically do not have access to the full state. Second, uncertainties in the observations, when not accounted for, can lead to instability and constraint violations. To overcome these limitations, we propose a combination of RL-MPC with robust output feedback model predictive control, named robust learning-based output feedback model predictive control (RLO-MPC). We show recursive feasibility and stability, and prove theoretical guarantees on the performance over iterations. We validate the proposed approach with a numerical example in simulation and a quadrotor stabilization task in experiments.