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Improving endurance is crucial for extending the spatial and temporaloperation range of autonomous underwater vehicles (AUVs). Considering thehardware constraints and the performance requirements, an intelligent energymanagement system is required to extend the operation range of AUVs. This paperpresents a novel model predictive control (MPC) framework for energy-optimalpoint-to-point motion control of an AUV. In this scheme, the energy managementproblem of an AUV is reformulated as a surge motion optimization problem in twostages. First, a system-level energy minimization problem is solved by managingthe trade-off between the energies required for overcoming the positivebuoyancy and surge drag force in static optimization. Next, an MPC with aspecial cost function formulation is proposed to deal with transients andsystem dynamics. A switching logic for handling the transition between thestatic and dynamic stages is incorporated to reduce the computational efforts.Simulation results show that the proposed method is able to achievenear-optimal energy consumption with considerable lower computationalcomplexity.

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