Submersibles--Automatic control

This thesis describes a general three-dimensional Obstacle Avoidance approach for the Autonomous Underwater Vehicle (AUV) using a forward-looking high-frequency active sonar system. This approach takes into account obstacle distance and AUV speed to determine the vehicle's heading, depth and speed. Fuzzy logic has been used to avoid the abrupt turn of the AUV in the presence of obstacles so that the vehicle can maneuver smoothly in the underwater environment. This approach has been implemented as an important part of the overall AUV software system. Using this approach, multiple objects could be differentiated automatically by the program through analyzing the sonar returns. The current vehicle state and the path of navigation of the AUV are self-adjusted depending on the location of the obstacles that are detected. A minimum safety distance is always maintained between the AUV and any object. Extensive testing of the program has been performed using several simulated AUV on-board systems undergoing different types of missions.

The design of an Autonomous Undersea Vehicle (AUV) control system is a significant challenge in-light of the highly uncertain nature of the ocean environment together with partially known nonlinear vehicle dynamics. This thesis describes a Neural Network architecture called Cerebellar Model Arithmetic Computer (CMAC). CMAC is used to control a model of an Autonomous Underwater Vehicle. The AUV model consists of two input parameters, the rudder and stern plane deflections, controlling six output parameters; forward velocity, vertical velocity, pitch angle, side velocity, roll angle, and yaw angle. Properties of CMAC and results of computer simulations for identification and control of the AUV model are presented.

Response time to a threat or incident for coastline security is an area needing improvement. Currently, the U.S. Coast Guard is tasked with monitoring and responding to threats in coastal and port environments using boats or planes, and SCUBA divers. This can significantly hinder the response time to an incident. A solution to this problem is to use autonomous underwater vehicles (AUVs) to continuously monitor a port. The AUV must be able to navigate the environment without colliding into objects for it to operate effectively. Therefore, an obstacle avoidance system (OAS) is essential to the activity of the AUV. This thesis describes a systematic approach to characterize the OAS performance in terms of environments, obstacles, SONAR configuration and signal processing methods via modeling and simulation. A fuzzy logic based OAS is created using the simulation. Subsequent testing of the OAS demonstrates its effectiveness in unknown environments.

The Morpheus, the latest generation of AUV developed at Florida Atlantic University was designed to be as modular as possible, and to handle longer, more complicated missions. The software must now reflect this improvement: it should be as dynamic as possible, must adapt to the different missions and emphasize flexibility and scalability. It must allow for complex behaviors, failure detection and handling and multiple cooperative missions. On the other hand, the new high-level controller has to remain accessible to the non expert user. To achieve these goals, a new architecture, based on the Convenient Hierarchical Autonomous State Machine formalism, was implemented using Python. The system is modeled as a set of concurrent processes communicating through shared memory to accommodate a variety of sensor payloads from one mission to the next. New control tools can be integrated dynamically into the architecture in the form of modules implementing new behaviors.

Accurate Autonomous Underwater Vehicles positioning requires an appropriate control design which takes into account the nonlinear coupling between the different degrees of freedom. Assuming a vehicle equipped with two side-thruster modules including two tunnel thrusters each, the control problem will be split into an outer control loop handling the motion of the vehicle, and an inner control loop designed to track the thrust commanded to each thruster. A multivariable Lyapunov function based approach, characterized by robustness properties with respect to parametric uncertainties and linearly bounded control output, will be proposed for the outer-loop and simulation results will be discussed. Regarding the low-level control framework, the performance of nine different controllers including conventional PI, sliding mode fuzzy controllers, and adaptive schemes such as model reference and sliding mode adaptive controllers, will be compared through theoretical derivations and experimental results. Such a comparison will show the advantages of the adaptive schemes in terms of tuning, robustness, and tracking performances.

A method of on-line monitoring AUV onboard systems is described. This algorithm determines deviations from normal operating conditions based on a damage level calculated from recursive least squares system identification performed on the system under consideration, followed by a gradient detection technique which extracts significant changes in identified model parameters System damage types are characterized together with likely system responses to such failures. Extensive testing of the algorithm is performed using several simulated AUV on-board systems undergoing different types of failures while carrying out different mission scenarios.