Fault Tolerant Control of Autonomous Underwater Vehicles
Creators: name: Macatangay, Xan
Publisher: name: RMIT University
Year: 2025
Subjects: classificationCode: 400702
subject: Automation engineering
classificationCode: 400705
subject: Control engineering

description: Underwater vehicles are complex nonlinear systems that operate in the difficulties and uncertainties presented by the underwater environment. This includes limited communication and sensor capabilities, varying water conditions, external disturbances, and actuator faults. The growing interest in using autonomous underwater vehicles (AUVs) for longer, more remote, or more dangerous operations has raised the need for underwater vehicle controllers that are robust to system uncertainties. To account for uncertainties in the controlled system, model-based AUV control laws have been developed to compensate or approximate the forces and moments acting on the vehicle which are not accounted for in the control model. Examples of such control law techniques include sliding mode control, adaptive control, and more recently control using machine learning. These control law techniques have been applied to produce controllers for a variety of AUV systems, but the variety in actuator configurations and control objectives prevents the quantitative comparison between many of the proposed controllers in the literature. Recent works have also been exploring the control of AUVs with hybrid actuator configurations - a combination of multiple thrusters and control surfaces. These configurations enable the vehicle to operate in a larger range of velocities and provide increased actuator redundancy that can be utilised for actuator fault tolerance methods. To achieve this, new control laws and control allocation methods must be adapted from the literature for traditional AUV actuator configurations. 

This research began with the development of a nonlinear control design method for axisymmetric AUVs with a hybrid actuator configuration. The method utilises the available actuators to perform six degree of freedom trajectory tracking. By smoothly transitioning the controller’s desired orientation from the reference orientation to a drag-optimal orientation, the position tracking objective is achieved for a larger velocity range while enabling independent orientation control in fully actuated conditions. A Lyapunov analysis and simulations of the controlled system demonstrate the controller’s tolerance to constant external disturbances and parameter discrepancies in the control model.

The hybrid AUV model is then used to determine the fault tolerance of a model-based PID control method, which is applied to the hybrid AUV and simulated under faulty conditions. A fault compensating control allocation method for the system is proposed, and the control law is augmented with proposed saturation compensation terms that complement the control reallocation. A Lyapunov analysis and simulations are again conducted to demonstrate the stability of the final controlled system. The heave and depth trajectory tracking performance of the initial and final controllers are compared to determine the effectiveness of proposed control law and control allocation methods. 

To develop a comprehensive understanding of the control performance benefits of fault compensation and uncertainty tolerance techniques not yet seen in the literature, a quantitative comparison of the trajectory tracking and path following performance from a set of control design methods is conducted. A selected set of control laws based on sliding mode control, adaptive control, and using machine learning are implemented on the same hybrid AUV model. The control laws are implemented in combination with static and fault compensating control allocation methods. The performance of the resulting controller variations for six degree of freedom trajectory tracking are compared in terms of transient response and steady-state tracking errors.

The main contribution of this thesis is the investigation and development of actuator fault and uncertainty tolerant control design methods for AUVs with hybrid actuator configurations. Both the control law and control allocation methods are utilised to consider the control loss from faults, and the actuator saturation resulting from control reallocation. Variations of the control design method based on the recent AUV control literature are developed to utilise the uncertainty tolerance of sliding mode control, adaptive control, and control utilising machine learning. A final quantitative comparison of the control performance produced by each of the method variations demonstrates the versatility and limitations of the proposed fault and uncertainty tolerant control design methods for AUVs with hybrid actuator configurations.