Real-Time Model Predictive Control for Energy Management in Autonomous Underwater Vehicle

TL;DR

This paper introduces a real-time model predictive control framework that optimizes energy use for autonomous underwater vehicles, significantly extending their operational range while maintaining computational efficiency.

Contribution

The paper presents a novel two-stage MPC approach that combines static energy minimization with dynamic control, reducing computational complexity for AUV energy management.

Findings

Achieves near-optimal energy consumption in simulations.

Reduces computational complexity compared to existing methods.

Effectively manages transition between static and dynamic control stages.

Abstract

Improving endurance is crucial for extending the spatial and temporal operation range of autonomous underwater vehicles (AUVs). Considering the hardware constraints and the performance requirements, an intelligent energy management system is required to extend the operation range of AUVs. This paper presents a novel model predictive control (MPC) framework for energy-optimal point-to-point motion control of an AUV. In this scheme, the energy management problem of an AUV is reformulated as a surge motion optimization problem in two stages. First, a system-level energy minimization problem is solved by managing the trade-off between the energies required for overcoming the positive buoyancy and surge drag force in static optimization. Next, an MPC with a special cost function formulation is proposed to deal with transients and system dynamics. A switching logic for handling the transition between the static and dynamic stages is incorporated to reduce the computational efforts. Simulation results show that the proposed method is able to achieve near-optimal energy consumption with considerable lower computational complexity.