A hybrid-driven underwater glider model, hydrodynamics estimation, and an analysis of the motion control

• Published in: Ocean engineering Vol. 81; pp. 111 - 129
• Main Authors: Isa, Khalid, Arshad, M.R., Ishak, Syafizal
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.05.2014; Elsevier
• Subjects: Applied sciences; Computational fluid dynamics; Computer science; control theory; systems; Computer simulation; Control theory. Systems; Earth, ocean, space; Exact sciences and technology; External geophysics; Fluid flow; Geophysics. Techniques, methods, instrumentation and models; Gliders; Hybrid-driven underwater glider; Hydrodynamics; Marine; Mathematical models; Modeling; Motion control; Predictive control; Robotics; Underwater; Hydrodynamics; Hybrid-driven underwater glider; Modeling; Motion control; Autonomous system; Moving robot; Oceanographic survey; hydrodynamics; Submarine vehicle; Robotics
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2014.02.002

An autonomous hybrid-driven glider is a new class of autonomous underwater glider that integrates the concept of a buoyancy-driven underwater glider and a conventional autonomous underwater vehicle (AUV). This glider has multi-functionality that enables it to overcome the speed and maneuverability limitations of buoyancy-driven gliders. Thus, this paper presents a mathematical model of the hybrid-driven glider, an estimation of the hydrodynamics and an analysis of the motion control. A Newtonian approach has been used to model the glider under the influence of water current. In addition, the hydrodynamics were estimated by using a Strip theory and a computational fluid dynamic (CFD). In order to analyze the motion of the glider, a Neural Network Predictive Control (NNPC) has been designed and its performance has been compared with the Model Predictive Control (MPC) and Linear Quadratic Regulator (LQR). The simulation results demonstrated that the Neural Network Predictive Control (NNPC) produced better control performance than the performance of the MPC and LQR when dealing with disturbances. In addition, the results also demonstrate the hydrodynamic response of the glider over the velocity, angle of attack and sideslip angle. •We have modeled a hybrid-driven autonomous underwater glider that able to control the wings and a rudder independently.•We analyzed the hydrodynamic characteristics of the glider by using the CFD analysis.•We designed the Neural Network Predictive Control (NNPC) for the motion control system of the glider.•We analyzed and compared the NNPC performance with Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC).•The NNPC control system produced the best performance.