Trajectory Optimization Strategies for Supercavitating Underwater Vehicles

Abstract

Supercavitating vehicles are characterized by substantially reduced hydrodynamic drag, in comparison with fully wetted underwater vehicles. Drag is localized at the nose of the vehicle, where a cavitator generates a cavity that completely envelopes the body, at the fins, and on the vehicle after-body. This unique loading configuration, the complex and non-linear nature of the interaction forces between vehicle and cavity, the unsteady behavior of the cavity itself and memory effects associated with its formation process make the control and maneuvering of supercavitating vehicles particularly challenging. This study presents an initial effort towards the evaluation of optimal trajectories for this class of underwater vehicles. Flight trajectories and maneuvering strategies for supercavitating vehicles are obtained through the solution of an optimal control problem. Given a cost function, and general constraints and bounds on states and controls, the solution of the optimal control problem yields control time histories that maneuver the vehicle according to the desired strategy, together with the associated flight path. The optimal control problem is solved using the direct transcription method, which does not require the derivation of the equations of optimal control and leads to the solution of a discrete parameter optimization problem. Examples of maneuvers and resulting trajectories are given to demonstrate the effectiveness of the proposed methodology and the generality of the formulation.

Improved Migration Genetic Algorithm Optimization for Variable-Speed Falling Trajectory of Mobile Crossbeam in Composite Hydraulic Press With Pressure Shock

“…where, t c = t s + t 0 . Inputting ( 16) and ( 17) into ( 13) to (15), the target trajectory with boundary conditions can be obtained.…”

“…Because of high pertinence and the ability of obtaining the optimal solution in advance, trajectory planning technology can avoid large-scale experiments and easily implement in engineering It has been applied in industrial manipulator [13], elevator [14], underwater equipment [15] aircraft [16] and other fields to suppress vibration Many researches had used trajectory planning to solve structural vibration problems Moriello et al [17] reduces vibration of industrial manipulator using exponential B-spline trajectories generated by dynamic filters. Aribowo and Terashima [18] proposed the free via points using cubic spline optimization with combined with input shaping to reduce motion time and vibration of robot arms.…”

Research on Lateral Maneuverability of a Supercavitating Vehicle Based on RBFNN Adaptive Sliding Mode Control with Rolling Restriction and Planing Force Avoidance

“…With the continuous development of supercavitating technology, the speed of an underwater vehicle has increased tremendously by generating a supercavity to envelop the vehicle, which is called the supercavitating vehicle. Due to the existence of the supercavity, most regions of the supercavitating vehicle do not contact the surrounding flow field so as to dramatically reduce the skin friction drag, which allows for high speeds in comparison to a conventional underwater vehicle [2,3]. The hydrodynamic performance of a supercavitating vehicle is significantly different from that of a conventional underwater vehicle due to the nonlinear interaction and penetration between the vehicle body and supercavity.…”

“…Generally, a cavitator and tail fins are required together as actuators to control the motion of a supercavitating vehicle. Therefore, the motion control and maneuverability of a supercavitating vehicle present severe challenges [2].…”

A modified control parametrization method for the numerical solution of bang–bang optimal control problems

“…These methods are both fast and accurate, but tend to suffer from great sensitivity to initialization (Cristiani and Martinon, 2010). On the other hand, direct methods, based on discretization and nonlinear programming, have been used extensively in a variety of optimal control problems (Ruzzene et al, 2008;Yousefi et al, 2011;Foroozandeh and Shamsi, 2012). Their advantage over indirect methods is their wider radius of convergence to an optimal solution.…”