Recursive Feasibility without Terminal Constraints via Parent-Child MPC Architecture

Abstract

This paper proposes a novel hierarchical model predictive control (MPC) framework, called the Parent-Child MPC architecture, to steer nonlinear systems under uncertainty towards a target set, balancing computational complexity and guaranteeing recursive feasibility and stability without relying on conservative terminal constraints in online decision-making. By coupling a small-horizon Child MPC layer with one or more large-horizon Parent MPC layers, the architecture ensures recursive feasibility and stability through adjustable stage-wise constraints derived from tube-based control. As is demonstrated in our case studies, compared to traditional MPC methods, the proposed Parent-Child MPC architecture enhances performance and computational efficiency, reduces conservativeness, and enables scalable planning for certain nonlinear systems.