Fathi H. Ghorbel

In this article, we present the experimental validation of our previous research on buoyancy control devices (BCDs) that are based on proton exchange membrane (PEM) electrolysis. These devices are used to control the buoyancy and orientation of autonomous underwater vehicles by volume adjustments of the output gases (hydrogen and oxygen) from PEM electrolysis. Two different devices are designed. Using the first device, we performed experiments to demonstrate the effectiveness of using regular (hard) thrusters and (soft) PEM electrolysis for depth control. We utilized a model predictive control law for this purpose. The experimental results showed that the addition of the soft actuator brings a significant amount of energy savings. Furthermore, we designed a second device to validate the simultaneous control of the depth and orientation of BCDs that use only PEM electrolysis. Two different closed-loop controllers are designed to use two PEM electrolyzers. The experimental results demonstrated their ability to control both vertical movement and orientation simultaneously. A feasibility study on deep ocean application has been conducted and simulation results have shown that an industrial PEM-based BCD can save up to 90% of energy when an underwater vehicle operates a payload of 5 kg under 300 bar water pressure.

This paper presents a novel bio-inspired robotic jellyfish with an artificial swim bladder. To control buoyancy, reversible proton exchange membrane (PEM) electrolysis is utilized for the artificial swim bladder, while a direct current (DC) motor-driven artificial bell serves as the primary propulsion system. A physics-based model of the jellyfish robot was developed, and real-time experiments were conducted in an aquarium environment to achieve closed-loop depth control. The identification of the system was performed using the experimental results to estimate the transfer function of the system, which is then applied in the design of predictive model control (MPC). A proportional-integral-derivative (PID) control strategy is implemented for the swim bladder, and a reinforcement learning (RL)-enhanced MPC is designed for the propulsion system. The effectiveness of combining PID control with RL-driven MPC is demonstrated through simulation case studies, which utilize the physics-based model and the results of the identification of the system. These studies show high performance and robustness, even in the presence of disturbances.

A Proton Exchange Membrane (PEM) fuel cell enabled buoyancy control device (BCD) is developed to address the challenge of depth control in underwater devices by compensating for buoyancy changes during underwater manipulations. The BCD splits distilled water into hydrogen and oxygen gases, increasing the volume of balloons attached to it, providing a permanent change in buoyancy of the device, and reducing the need for motors to run constantly. Results indicate that energy savings can reach up to 85% in comparison with experiments that use DC motors only. Additionally, the response is smoother and device stability is increased when DC motors are not running. Experimental results show the trajectory profiles when the BCD is active and passive, respectively. Furthermore, the PEM electrolyzers can be used as fuel cells to charge a battery or run another mechanical device after the operation, resulting in around 28% energy savings.

Nonlinear control of Autonomous Underwater Vehicles (AUVs) via the use of thrusters has been well established. These AUVs can be used for various applications, including subsea inspection and maintenance, exploration, research, and observation. These thrusters are best suited for large thrust forces required by large movements, but require a lot of energy to operate for long periods of time. Research into Buoyancy Control Devices (BCDs) using reversible fuel cells (RFCs) has proven their viability. This paper demonstrates nonlinear control of BCD–based AUVs while picking up tools with unknown weights. An adaptive control law is derived that ensures stability and good performance throughout the completion of the desired mission. Simulation results demonstrate desired performance with low energy requirements.