In this paper, we discuss our work developing the Dual Spacecraft System (DSS). The DSS structure enables the launch of two independent, small-to-medium class payloads on a single United Launch Alliance (ULA) Atlas V launch vehicle. The DSS makes extensive use of existing components with well-understood capabilities. The structure itself consists of two back-to-back Centaur Forward Adapters with the optional addition of between one and four stub adapters to provide flexibility in the volumes of the upper and lower payload envelopes. The Centaur Forward Adapter is a combination of a cylindrical adapter and a conical adapter attached together with a common ring. In the DSS application, the cylindrical halves of the two forward adapters mate together. Besides the simplest solution with no additional plugs, there are several potential configurations available which utilize up to four cylindrical plugs, the stub adapters. This combination of hardware creates a clamshell which contains the lower payload. The Atlas V 14 foot payload fairing completely encloses the DSS. The DSS encloses the lower payload and provides structural support for the upper payload; the DSS reacts loads only from the upper payload during vehicle flight. The forward interface of the DSS is the 62 inch Standard Interface Specification (SIS) payload interface, permitting use of existing payload adapters. The aft interface attaches to the Atlas launch vehicle through a standard cylindrical payload adapter.

In this study, an adaptive scheme for autonomous underwater vehicle systems is developed that utilizes a model of the complex nonlinear dynamics and control of the vehicle to enable detection of sensor faults and failures. Our framework for design of fault identification and risk management, incorporates a neural network-based nonlinear observer to monitor the input and output of the control system for detection of a variety of faults in the sensors.The training occurs online and parameters of the recurrent neural network are updated by an Extended Kalman Filter. The fault detection and Identification system was developed and integrated for a nonlinear model of a Remus-100 underwater vehicle. The results obtained from the numerical simulation shows the system's ability for prompt detection and isolation of a variety of sensor faults. Further study is needed for development of experimental validation and verification and computational efficiency of the proposed algorithm.

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