Robust output-feedback MPC for nonlinear systems with applications to robotic exploration

Scott Brown, Mohammad Khajenejad, Aamodh Suresh and Sonia Martínez
IEEE Control Systems Letters, to appear 2025

Abstract:

This paper introduces a novel method for robust output-feedback model predictive control (MPC) for a class of nonlinear discrete-time systems. We propose a novel interval-valued predictor which, given an initial estimate of the state, produces intervals which are guaranteed to contain the future trajectory of the system. By parameterizing the control input with an initial stabilizing feedback term, we are able to reduce the width of the predicted state intervals compared to existing methods. We demonstrate this through a numerical comparison where we show that our controller performs better in the presence of large amounts of noise. Finally, we present a simulation study of a robot navigation scenario, where we incorporate a time-varying entropy term into the cost function in order to autonomously explore an uncertain area.

File: (forthcoming)

Bib-tex entry:

@article{SB-MK-AS-SM:25-cssl,
author = {S. Brown and M. Khajenejad and A. Suresh and S. Mart{\'\i}nez},
title = {Robust output-feedback MPC for nonlinear systems with applications to robotic exploration},
journal= {IEEE Control Systems Letters},
pages = {},
volume = {},
number = {},
note = {To appear},
year = {2025}
}