Energy Management for Autonomous Underwater Vehicles using Economic Model Predictive Control

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. To characterize the energy-to-go, a thorough analysis has been conducted on the globally optimized vehicle trajectory computed using the direct collocation (DC) method for our test-bed AUV, DROP-Sphere. Based on the two operation modes observed from our analysis, the energy-to-go is decomposed into two components: (i) dynamic and (ii) static costs. This breakdown facilitates the estimation of the energy-to-go, improving the AUV energy efficiency. Simulation is conducted using a six-degrees-of-freedom dynamic model identified from DROP-Sphere. The proposed method for AUV control results in a near-optimal energy consumption with considerably less computation time compared to the DC method and substantial energy saving compared to a line-of-sight based MPC method.