Tube-Based Model Predictive Control with Random Fourier Features for Nonlinear Systems
Abstract
This paper presents a computationally efficient approach for robust Model Predictive Control of nonlinear systems by combining Random Fourier Features with tube-based MPC. Tube-based Model Predictive Control provides robust constraint satisfaction under bounded model uncertainties arising from approximation errors and external disturbances. The Random Fourier Features method approximates nonlinear system dynamics by solving a numerically tractable least-squares problem, thereby reducing the approximation error. We develop the integration of RFF-based residual learning with tube MPC and demonstrate its application to an autonomous vehicle path-tracking problem using a nonlinear bicycle model. Compared to the linear baseline, the proposed method reduces the tube size by approximately 50%, leading to less conservative behavior and resulting in around 70% smaller errors in the test scenario. Furthermore, the proposed method achieves real-time performance while maintaining provable robustness guarantees.
Cite
@article{arxiv.2511.16425,
title = {Tube-Based Model Predictive Control with Random Fourier Features for Nonlinear Systems},
author = {Ákos M. Bokor and Tamás Dózsa and Felix Biertümpfel and Ádám Szabó},
journal= {arXiv preprint arXiv:2511.16425},
year = {2025}
}
Comments
Submitted to IEEE IV 2026, The IEEE Intelligent Vehicles Symposium