Robust adaptive NMPC using ellipsoidal tubes

TL;DR

This paper introduces a robust, computationally efficient nonlinear MPC algorithm that uses ellipsoidal tubes to handle uncertainties and disturbances, ensuring safety and stability in learning-based control systems.

Contribution

The paper presents a novel ellipsoidal tube-based NMPC method that accounts for uncertainties and disturbances, with guarantees of recursive feasibility and practical stability.

Findings

Scales favorably with system dimensions in simulations

Guarantees recursive feasibility and stability

Handles uncertainties via ellipsoidal sets

Abstract

We propose a computationally efficient nonlinear Model Predictive Control (NMPC) algorithm for safe, learning-based control. The system model is represented as an affine combination of basis functions with unknown parameters, and is subject to additive set-bounded disturbances. Our algorithm employs successive linearization around nominal predicted trajectories and accounts for uncertainties in predicted states due to linearization, model errors, and disturbances using ellipsoidal sets. The ellipsoidal tube-based approach ensures that constraints on control inputs and system states are satisfied. Robustness to uncertainty is ensured using bounds on linearization errors and a backtracking line search. We show that the ellipsoidal embedding of model uncertainty scales favourably with system dimensions in numerical simulations. The algorithm incorporates set membership parameter estimation, and provides guarantees of recursive feasibility and input-to-state practical stability.