Robust Maneuver Planning With Scalable Prediction Horizons: A Move Blocking Approach

TL;DR

This paper introduces a scalable, computationally efficient move blocking MPC method with constraint reduction, enabling long-horizon maneuvers like helicopter landing on limited hardware.

Contribution

It presents a novel tubebased shrinking horizon MPC with move blocking and constraint reduction, allowing long prediction horizons on resource-constrained systems.

Findings

Order of magnitude reduction in computation time.

Successful demonstration with 300-step horizon helicopter landing.

Slight increase in trajectory cost due to constraint reduction.

Abstract

Implementation of Model Predictive Control (MPC) on hardware with limited computational resources remains a challenge. Especially for long-distance maneuvers that require small sampling times, the necessary horizon lengths prevent its application on onboard computers. In this paper, we propose a computationally efficient tubebased shrinking horizon MPC that is scalable to long prediction horizons. Using move blocking, we ensure that a given number of decision inputs is efficiently used throughout the maneuver. Next, a method to substantially reduce the number of constraints is introduced. The approach is demonstrated with a helicopter landing on an inclined platform using a prediction horizon of 300 steps. The constraint reduction decreases the computation time by an order of magnitude with a slight increase in trajectory cost.