Input-to-state Stable Approximate Nonlinear Model Predictive Control with Realtime Feasibility
arXiv:2607.28353
The paper presents a lightweight approximate robust nonlinear model predictive control scheme that uses input-to-state control Lyapunov functions and robust control barrier functions to enable real‑time quadratic program solutions on embedded hardware, demonstrated on constrained spacecraft control.
Abstract
In this paper, a computationally lightweight approximate robust nonlinear model predictive control (NMPC) law is proposed based on a pair of input-to-state control Lyapunov function and robust control barrier function. The result builds upon and augments a recently introduced nominal infinitesimal- horizon NMPC scheme which permits small-sized quadratic programs to compute the feedback law for nonlinear constraint systems on embedded hardware in real time. Numerical experiments for nonlinear constrained spacecraft control and comparison to other robust NMPC schemes from the literature demonstrate the effectiveness of the proposed scheme.
Submitted to European Journal of Control