Deploying a world wide force that is strategically responsive and dominant at every point on the spectrum of conflict involves the cooperative system development and use of advanced technologies that yield revolutionary capabilities to support the war-fighters needs. This presentation describes an agent based control architecture and prototype implementation developed by ARDEC that enables command and control of multiple unmanned platforms and associated mission packages for collaborative target hand-off/engagement. Current prototypes provide the ability to remotely locate, track and predict the movement of enemy targets on the battlefield using a variety of sensor systems hosted on multiple, non-homogeneous SUAVs and UGVs.
A prototype combat decision aid software suite (CDAS) is described which provides the mounted/dismounted warfighter/commander with a fully integrated and scalable decision support capability to support network centric fires and effects based operations. CDAS is based on an open architecture, software back plane concept which maximizes flexibility in tailoring CDAS functionality to meet the requirements of any sensor, shooter or command and control node within an FCS unit of action. The architecture and back plane methodology will be described in section 2 followed by a description of major application component plug-ins in section 3. CDAS has been extensively tested in a number of battle lab Concept Evaluation Program (CEP) Experiments and successfully configured to support a number of technology insertion experiments. These results will be summarized in section 4.
This paper deals with the problems of converting a continuous-time uncertain system to an equivalent discrete-time interval model and constructing a robust hybrid control law for an uncertain sampled-data system. The system matrices characterizing the state-space representation of the original uncertain materials and structures are assumed to be interval matrices. The Pade approximation method together with interval arithmetic is employed to obtain the approximate discrete-time interval models. A technique is developed to estimate the less conservative modeling errors. These modeling errors are used to modify the obtained Pade interval approximants. The resulting modified interval models are able to tightly enclose the exact discrete-time uncertain model. Various digitally redesigned nominal controllers, which are developed for digital control of continuous-time nominal systems, are extended to the corresponding interval controllers for robust digital control of continuous-time uncertain systems. Using the digitally redesigned interval controllers, the dynamic states of the digitally controlled sampled-data uncertain systems are able to closely match with those of the original analogously controlled continuous-time uncertain systems for a relatively longer sampling period.
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