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This paper addresses the problem of actuator fault detection for a Remotely Operated Vehicle widely used in navy and marine industries. Firstly, the dynamical model for the Remotely Operated Vehicle is presented. Then this model is used to develop a diagnostic system based on a nonlinear (Thau) observer for residual generation, and on a sequential change detection algorithm for residual evaluation. The diagnostic system is tested through simulation trials in which a fault is injected into one of the actuators. The analysis of the results proves that the developed detection system can correctly detect a fault on one of the actuators by using only measurements of sway, surge and yaw (and their speeds), thus representing an effective solution to the problem of actuator fault detection for the Remotely Operated Vehicle.

Coordinated fleet of Autonomous Underwater Gliders (AUGs) can provide significant benefit to a number of marine applications including ocean sampling, mapping, surveillance and communication. Traditional techniques for navigating underwater vehicles have been designed for single-vehicle operations and do not scale well to multi-vehicle operations and missions. In this paper a navigation system for a fleet of AUGs is developed based on Networked Decentralized Model Predictive Control (ND-MPC). The proposed approach coordinates a group of point-mass mobile agents to achieve a desired formation, while avoiding collisions between themselves. In order to obtain collision free paths, the approach integrates the required collision avoidance constraints. The fleet localization is performed by sensor fusion using adaptive extended Kalman filtering. The free collision and convergence properties are verified through simulations results. The proposed approach can be generalized to formation of heterogeneous autonomous agents.

A fleet of unmanned marine vehicles moving together in a prescribed pattern forms a robotic system for cooperative tasks. The problem is to control several marine vehicles such that, after transients, they form a required formation and move along a desired trajectory. The capability to accomplish the mission even in case of fault, is a fundamental requirement for these kind of vehicles. A completely decentralized nonlinear predictive control integrated with a fault tolerant strategy is here proposed for allowing safe cooperation. Based both on the local information and the acquired one through a LAN, each marine vehicle selects the desired formation to keep and plans its future actions. The proposed solution can be applied to different classes of unmanned marine vehicles.

This paper proposes an actuator fault-tolerant control scheme designed for a class of nonlinear systems, and then applied to an underwater Remotely Operated Vehicle (ROV) used for inspection purposes. Fault detection is achieved using a Variable Structure System (VSS) observer, while a sliding-mode based approach has been used both for fault isolation and ROV control, after the application of an input decoupling nonlinear state transformation to the ROV model. Finally, control reconfiguration is performed exploiting the inherent redundancy of actuators. An extensive simulation study has been also performed, supporting the effectiveness of the proposed approach.

This paper proposes an actuator fault-tolerant control scheme, composed of the usual modules performing detection, isolation, accommodation, designed for a class of nonlinear systems, and then applied to an underwater remotely operated vehicle (ROV) used for inspection purposes. Detection is in charge of a residual generation module, while a sliding-mode based approach has been used both for ROV control and fault detection, after the application of an input decoupling nonlinear state transformation to the ROV model. Finally, control reconfiguration is performed exploiting the inherent redundancy of actuators. An extensive simulation study has been also performed, supporting the effectiveness of the proposed approach.

This paper addresses the problem of fault detection and isolation for helicopters used as Unmanned Aerial Vehicles. First a model for a reduced-scale helicopter is presented, then this model is linearized and used to design unknown input observers for fault diagnosis. The developed system is tested and analyzed. The results prove that it represents an effective solution to fault diagnosis problems in Unmanned Aerial Vehicles.

A fleet of Autonomous Underwater Vehicles (AUVs) moving together in a prescribed pattern can form an efficient data acquisition network. The problem is to control several AUVs such that after transients, they form a required formation and move along a desired trajectory. The capability to accomplish the mission even in case of faults, is a fundamental requirement for these kind of missions. A completely decentralized predictive control and FDI strategy is here proposed for allowing cooperation. Trough an underwater communication channel, each vehicle broadcasts its position, its future behavior and its actuator/sensor fault situation. Based both on the local and the received information, each vehicle selects the desired formation to keep and plans its future actions. Simulation results are provided to validate the approach.

This paper presents a set of results on the design and on the experimental validations of a Sensor Fault Detection System (SFDS) for a smart wheelchair to prevent simples navigation sensor faults and hence to reduce the risk of safety hazards. The design has been performed by the Structural Analysis technique which uses graph theory to determine which redundancy exists in the sensory system and thus shows the possibility to detect and handle the navigation sensor faults. The considered navigation sensor equipment includes odometric, gyroscopic and laser range measures. The experimental tests have been developed by a smart wheelchair equipped with a navigation system.