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We report the development of novel fault detection and isolation (FDI) methods for model-based fault detection (MB-FD) and quotient-space fault isolation (QS-FI). This FDI approach performs MB-FD and QS-FI of single or multiple concurrent faults in plants and actuators simultaneously, without a priori knowledge of fault form, type, or dynamics. To detect faults, MB-FD characterizes deviation from nominal behavior using the plant velocity and plant and actuator parameters estimated by nullspace-based adaptive identification. To isolate (i.e. identify) faults, the QS-FI algorithm compares the estimated parameters to a nominal parameter class in progressively decreasing-dimensional quotient spaces of the parameter space. A preliminary simulation study of these proposed FDI methods applied to a three degree-of-freedom uninhabited underwater vehicle plant model shows their ability to detect as well as isolate faults for the cases of both single and multiple simultaneous faults and suggests the generalizability of the MB-FD and QS-FI approaches to any well-defined second-order plant and actuator model whose parameters enter linearly: a broad class of systems which includes aerial vehicles, marine vehicles, spacecraft, and robot arms. 

Model-based approaches to navigation, control, and fault detection that utilize precise nonlinear models of vehicle plant dynamics will enable more accurate control and navigation, assured autonomy, and more complex missions for such vehicles. This paper reports novel theoretical and experimental results addressing the problem of parameter estimation of plant and actuator models for underactuated underwater vehicles operating in 6 degrees-of-freedom (DOF) whose dynamics are modeled by finite-dimensional Newton-Euler equations. This paper reports the first theoretical approach and experimental validation to identify simultaneously plant-model parameters (parameters such as mass, added mass, hydrodynamic drag, and buoyancy) and control-actuator parameters (control-surface models and thruster models) in 6-DOF. Most previously reported studies on parameter identification assume that the control-actuator parameters are known a priori. Moreover, this paper reports the first proof of convergence of the parameter estimates to the true set of parameters for this class of vehicles under a persistence of excitation condition. The reported adaptive identification (AID) algorithm does not require instrumentation of 6-DOF vehicle acceleration, which is required by conventional approaches to parameter estimation such as least squares. Additionally, the reported AID algorithm is applicable under any arbitrary open-loop or closed-loop control law. We report simulation and experimental results for identifying the plant-model and control-actuator parameters for an L3 OceanServer Iver3 autonomous underwater vehicle. We believe this general approach to AID could be extended to apply to other classes of machines and other classes of marine, land, aerial, and space vehicles.