RLO-MPC: Robust Learning-Based Output Feedback MPC for Improving the Performance of Uncertain Systems in Iterative Tasks
This work addresses the limitation of existing robust MPC methods that require full-state measurements, making it applicable to practical systems with partial noisy observations, though it is incremental as it builds on prior robust learning-based MPC frameworks.
The authors tackled the problem of performing repetitive tasks with uncertain observations and dynamics by proposing RLO-MPC, a method combining robust learning-based MPC with output feedback, and demonstrated recursive feasibility, stability, and performance guarantees, validated through simulation and quadrotor experiments.
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.