Energy Management for Autonomous Underwater Vehicles Using Economic Model Predictive Control

Niankai Yang · Dongsik Chang · Mohammad Reza Amini · Matthew Johnson-Roberson · Jing Sun

Fig. 1: Schematic of the DROP-Sphere — FIGURE FROM THE PAPER

TL;DR

An economic model predictive controller for underwater vehicles minimizes energy use by pairing a short-horizon cost with an energy-to-go estimate for reaching the goal.

BRIEF

Autonomous underwater vehicles can only stay out as long as their batteries last, so how they move matters. This work designs a controller that chooses thrust commands to minimize the energy spent, rather than just tracking a path. It predicts energy use over a short horizon and adds a terminal cost that estimates the energy still needed to reach the goal. Accounting for energy beyond the horizon lets the controller plan with the whole trip in mind, with the aim of extending vehicle endurance.

Abstract

This paper investigates the problem of energy-optimal control for autonomous underwater vehicles (AUVs). To improve the endurance of AUVs, we propose a novel energy-optimal control scheme based on the economic model predictive control (MPC) framework. We first formulate a cost function that computes the energy spent for vehicle operation over a finite-time prediction horizon. Then, to account for the energy consumption beyond the prediction horizon, a terminal cost that approximates the energy to reach the goal (energy-to-go) is incorporated into the MPC cost function.