This paper presents an active, model-based FTC (fault tolerant control) method for the dynamic positioning of underwater vehicles with thruster redundancy. Unlike conventional appraoches that rely heavily on state and parameter estimation, the proposed scheme directly utilizes the vehicle’s motion control error (MCE) to construct a residual for detecting thruster faults and failures during the steady state operation of the control system. One of the primary challenges in thruster fault identification is the unavailability of the actual control input under fault conditions. However, by conducting a detailed and rigorous analysis of the MCE variation trends associated with thruster faults, valuable information about this unknown control input can be extracted. This insight forms the foundation of the proposed FTC strategy. As for control reconfiguration, it’s straightforward since the thrust losses can be directly estimated through the fault identification process. Numerical studies with the real world vehicle model are carried out to validate the effectiveness of the proposed method.

This paper considers the autonomous navigation technology for an AUV (autonomous underwater vehicle) swimming in the underwater jacket structure environment. For P-SURO II AUV [1], there is a FLS (forward looking sonar) mounted in front of the platform. In addition to its initial purpose of obstacle detection, this sonar also can be used to sort of position aiding for the hybrid INS (inertial navigation system) which is constructed as the main navigation algorithm for the vehicle [2]. In the case of underwater jacket structure environment, if the structure information is known in priori, then we can estimate the vehicle's current position error according to the sonar structure image. This estimated position error is further used for position aiding for INS. In this paper, we also present the obstacle detection and path planning algorithm using this FLS. Some of the experimental tests in the sea environment are carried out to demonstrate the proposed underwater localization and path planning methods for the vehicle's autonomous swimming.

With the increase in developments in underwater infrastructure, the demand for development of autonomous vehicle navigation system in structured environment is also increased. However, the localization in a structured environment is a challenging problem due to signal uncertainties and distortions. In order to overcome these problems, we propose the camera and sonar aided integrated navigation system. In the proposed sensor-fusion-based localization scheme, the AUV estimates its own position continuously using artificial landmarks. The artificial landmarks for image sonar is deployed along the path to guide the AUV to the structure. The active vision markers are installed on the jacket structure, and they function as both landmarks and waypoints. This approach prevents the inherent drift of dead-reckoning velocities and collision with structures. The proposed approach was verified through a real sea experiment. The AUV conducted the full autonomous navigation from the dock to the jacket structure, and then returned to the dock without collision or significant localization error. These results show the feasibility of full autonomous navigation in a structured environment.

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions, a high performance control system of an AUV is needed to have the capacities of learning and adaptation to the variations of the AUV dynamics. In this paper, a linearly parameterized neural network is used to approximate the uncertainties of the vehicle dynamics, where the basis function vector of the network is constructed according to the vehicle physical properties. The proposed controller guarantees uniform boundedness of the vehicle trajectory tracking errors and network weights estimation errors based on the Lyapunov stability theory, where the network reconstruction errors and disturbances in the vehicle dynamics are bounded by an unknown constant. Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.