Energy-Efficient Pitch Control for a 1000 m-Class Underwater Glider: A Comparative Study of PID, Fuzzy, and ANFIS Controllers Based on Experimental Power Models

Underwater gliders are suited for long-duration oceanographic observation, but their endurance is bounded by onboard energy capacity. An overlooked source of energy loss is the attitude control system, which repeatedly repositions the internal moving mass to hold the desired pitch angle throughout each gliding cycle. Conventional PID and manually tuned fuzzy controllers continue driving the actuator after pitch convergence and adapt poorly to nonlinear buoyancy variations at depth. To address this, we propose an ANFIS (Adaptive Neuro-Fuzzy Inference System)-based pitch control strategy for a 1000 m-class underwater glider. A nonlinear 6-DOF dynamic simulator incorporating experimentally derived power models for the buoyancy engine and attitude controller was validated up to 100 bar. A 13-rule Sugeno-type fuzzy inference system was optimized through ANFIS hybrid learning using approximately 5500 samples from PID steady-state data. Simulation results show energy savings of 57.05% over PID and 4.98% over a manually tuned fuzzy controller, with no degradation in tracking accuracy. Sea trials confirm a reduction in moving mass displacement under real disturbance conditions, providing qualitative evidence consistent with the simulation results. Further quantitative validation of the energy reduction effect through free-running sea trials remains as future work.