Adaptive signal processing.

This study is performed in tandem with numerous experiments performed by the U.S. Navy to characterize the ocean environment in the South Florida region. The research performed in this study includes signal processing steps for isolating ocean phenomena, such as internal waves, in the magnetic field. Raw magnetometer signals, one on shore and one underwater, are processed and removed of common distortions. They are then run through a series of filtering techniques, including frequency domain cancellation (FDC). The results of the filtered magnetic residual are compared to similarly processed Acoustic Doppler Current Profiler (ADCP) data to correlate whether a magnetic signature is caused by ocean phenomena.

In this research, a wind feedforward (FF) controller has been developed to augment closed loop feedback controllers for the position and heading station keeping control of Unmanned Surface Vehicles (USVs). The performance of the controllers was experimentally tested using a 16 foot USV in an outdoor marine environment. The FF controller was combined with three nonlinear feedback controllers, a Proportional–Derivative (PD) controller, a Backstepping (BS) controller, and a Sliding mode (SM) controller, to improve the station-keeping performance of the USV. To address the problem of wind model uncertainties, adaptive wind feedforward (AFF) control schemes are also applied to the FF controller, and implemented together with the BS and SM feedback controllers. The adaptive law is derived using Lyapunov Theory to ensure stability. On-water station keeping tests of each combination of FF and feedback controllers were conducted in the U.S. Intracoastal Waterway in Dania Beach, FL USA. Five runs of each test condition were performed; each run lasted at least 10 minutes. The experiments were conducted in Sea State 1 with an average wind speed of between 1 to 4 meters per second and significant wave heights of less than 0.2 meters. When the performance of the controllers is compared using the Integral of the Absolute Error (IAE) of position criterion, the experimental results indicate that the BS and SM feedback controllers significantly outperform the PD feedback controller (e.g. a 33% and a 44% decreases in the IAE, respectively). It is also found that FF is beneficial for all three feedback controllers and that AFF can further improve the station keeping performance. For example, a BS feedback control combined with AFF control reduces the IAE by 25% when compared with a BS feedback controller combined with a non-adaptive FF controller. Among the eight combinations of controllers tested, SM feedback control combined with AFF control gives the best station keeping performance with an average position and heading error of 0.32 meters and 4.76 degrees, respectively.

Research on collaboration among unmanned platforms is essential to improve the applications for autonomous missions, by expanding the working environment of the robotic systems, and reducing the risks and the costs associated with conducting manned operations. This research is devoted to enable the collaboration between an Unmanned Surface Vehicle (USV) and an Autonomous Underwater Vehicle (AUV), by allowing the first one to launch and recover the second one. The objective of this dissertation is to identify possible methods to launch and recover a REMUS 100 AUV from a WAM-V 16 USV, thus developing this capability by designing and implementing a launch and recovery system (LARS). To meet this objective, a series of preliminary experiments was first performed to identify two distinct methods to launch and recover the AUV: mobile and semi-stationary. Both methods have been simulated using the Orcaflex software. Subsequently, the necessary control systems to create the mandatory USV autonomy for the purpose of launch and recovery were developed. Specifically, a series of low-level controllers were designed and implemented to enable two autonomous maneuvers on the USV: station-keeping and speed & heading control. In addition, a level of intelligence to autonomously identify the optimal operating conditions within the vehicles' working environment, was derived and integrated on the USV. Lastly, a LARS was designed and implemented on the vehicles to perform the operation following the proposed methodology. The LARS and all subsystems developed for this research were extensively tested through sea-trials. The methodology for launch and recovery, the design of the LARS and the experimental findings are reported in this document.