Adaptive Horizon Model Predictive Control

TL;DR

Adaptive Horizon Model Predictive Control (AHMPC) dynamically adjusts the prediction horizon in MPC to enable stabilization with minimal horizon length, improving efficiency for complex or fast systems by computing control and stability conditions online.

Contribution

This paper introduces AHMPC, a novel scheme that adjusts the MPC horizon adaptively and computes terminal feedback and stability conditions online, reducing computational effort.

Findings

Achieves stabilization with smaller horizons

Enables faster control of complex systems

Reduces offline computation requirements

Abstract

Adaptive Horizon Model Predictive Control (AHMPC) is a scheme for varying as needed the horizon length of Model Predictive Control (MPC). Its goal is to achieve stabilization with horizons as small as possible so that MPC can be used on faster or more complicated dynamic processes. Beside the standard requirements of MPC including a terminal cost that is a control Lyapunov function, AHMPC requires a terminal feedback that turns the control Lyapunov function into a standard Lyapunov function in some domain around the operating point. But this domain need not be known explicitly. MPC does not compute off-line the optimal cost and the optimal feedback over a large domain instead it computes these quantities on-line when and where they are needed. AHMPC does not compute off-line the domain on which the terminal cost is a control Lyapunov function instead it computes on-line when a state is in this domain.