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  • Cybernetics, Artificial Intelligence and Robotics  (3)
  • Cybernetics  (2)
  • General  (1)
  • 1
    Publication Date: 2019-07-13
    Description: The NASA Morphing Project seeks to develop and assess advanced technologies and integrated component concepts to enable efficient, multi-point adaptability in air and space vehicles. In the context of the project, the word "morphing" is defined as "efficient, multi-point adaptability" and may include macro, micro, structural and/or fluidic approaches. The project includes research on smart materials, adaptive structures, micro flow control, biomimetic concepts, optimization and controls. This paper presents an updated overview of the content of the Morphing Project including highlights of recent research results.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Type: SPIE''s 9th Annual International Symposium on Smart Structures and Materials; Mar 17, 2002 - Mar 21, 2002; San Diego, CA; United States
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  • 2
    Publication Date: 2019-07-27
    Description: Control of flexible systems under degradation or failure of sensors/actuators is considered. A Linear Matrix Inequality framework is used to synthesize H(sub infinity)-based controllers, which provide good disturbance rejection while capable of tolerating real parameter uncertainties in the system model, as well as potential degradation or failure of the control system hardware. In this approach, a one-at-a-time failure scenario is considered, wherein no more than one sensor or actuator is allowed to fail at any given time. A numerical example involving control synthesis for a two-dimensional flexible system is presented to demonstrate the feasibility of the proposed approach.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Type: AIAA Paper 01-4096 , AIAA Conference; 6-9 Aut. 2001; United States
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  • 3
    Publication Date: 2019-07-13
    Description: The design of control laws for dynamic systems with the potential for actuator failures is considered in this work. The use of Linear Matrix Inequalities allows more freedom in controller design criteria than typically available with robust control. This work proposes an extension of fault-scheduled control design techniques that can find a fixed controller with provable performance over a set of plants. Through convexity of the objective function, performance bounds on this set of plants implies performance bounds on a range of systems defined by a convex hull. This is used to incorporate performance bounds for a variety of soft and hard failures into the control design problem.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Type: AIAA Paper 2002-4940 , AIAA/AAS GN&C Conference; Aug 05, 2002 - Aug 08, 2002; Monterey, CA; United States
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  • 4
    Publication Date: 2019-07-10
    Description: This report represents an attempt to see if some of the techniques biological systems use to maximize their efficiency can be applied to the problems NASA faces in aeronautics and space exploration. It includes an internal survey of resources available at NASA Langley Research Center for biomimetics research efforts, an external survey of state of the art in biomimetics covering the Materials, Structures, Aerodynamics, Guidance and Controls areas. The Biomimetics Planning team also included ideas for potential research areas, as well as recommendations on how to implement this new program. This six-month survey was conducted in the second half of 1999.
    Keywords: General
    Type: NASA/TM-2002-211445 , L-18153 , NAS 1.15:211445
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  • 5
    Publication Date: 2019-07-13
    Description: This paper presents an iterative Linear Quadratic Gaussian (LQG) controller design approach for a linear stochastic system with an uncertain open-loop model and unknown noise statistics. This approach consists of closed-loop identification and controller redesign cycles. In each cycle, the closed-loop identification method is used to identify an open-loop model and a steady-state Kalman filter gain from closed-loop input/output test data obtained by using a feedback LQG controller designed from the previous cycle. Then the identified open-loop model is used to redesign the state feedback. The state feedback and the identified Kalman filter gain are used to form an updated LQC controller for the next cycle. This iterative process continues until the updated controller converges. The proposed controller design is demonstrated by numerical simulations and experiments on a highly unstable large-gap magnetic suspension system.
    Keywords: Cybernetics
    Type: NASA-CR-203238 , NAS 1.26:203238 , Journal of Dynamic Systems, Measurement and Control; 118; 366-372
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  • 6
    Publication Date: 2019-07-13
    Description: An algorithm is presented for identifying a state-space model of linear stochastic systems operating under known feedback controller. In this algorithm, only the reference input and output of closed-loop data are required. No feedback signal needs to be recorded. The overall closed-loop system dynamics is first identified. Then a recursive formulation is derived to compute the open-loop plant dynamics from the identified closed-loop system dynamics and known feedback controller dynamics. The controller can be a dynamic or constant-gain full-state feedback controller. Numerical simulations and test data of a highly unstable large-gap magnetic suspension system are presented to demonstrate the feasibility of this indirect identification method.
    Keywords: Cybernetics
    Type: NASA-CR-203237 , NAS 1.26:203237 , Journal of Guidance, Control, and Dynamics; 19; 4; 836-841
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