Reference Condensation for Model Predictive Control with Preview

TL;DR

This paper introduces reference condensation, a method to compress preview trajectories into a single setpoint, reducing complexity in model predictive control without sacrificing tracking performance.

Contribution

The paper proposes a novel reference condensation technique that maintains control accuracy while keeping parameter complexity independent of preview horizon.

Findings

Weighted condensation closely matches full preview performance.

Parameter dimension remains constant regardless of preview horizon.

Numerical experiments demonstrate effectiveness on aircraft and double integrator models.

Abstract

In model predictive control (MPC), preview information can greatly improve tracking. Including preview information does, however, increase the parameter dimension linearly with the preview horizon, which increases online cost and, more importantly, the complexity of explicit MPC. We introduce reference condensation, a method that compresses a future reference trajectory into a single setpoint through a linear map. For the unconstrained tracking problem, the map follows from a least-squares projection. For receding-horizon MPC, we also study a weighted variant that prioritizes the first applied control. Numerical experiments on a double integrator and a higher-order aircraft example show that the weighted condensation closely matches full preview while keeping the parameter dimension independent of the preview horizon.