Title: Real-Time Model Predictive Control for Energy Management in Autonomous Underwater Vehicle,
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.