Real-Time Model Predictive Control for Energy Management in Autonomous Underwater Vehicle
It addresses the energy management problem for AUVs, which is crucial for extending their operational range, but the results are simulation-based and incremental.
This paper presents a novel MPC framework for energy-optimal motion control of AUVs, achieving near-optimal energy consumption with considerably lower computational complexity compared to existing methods.
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.