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Daniel Guggenheim School of Aerospace Engineering

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Now showing 1 - 3 of 3
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    Adaptive Control of Evolving Gossamer Structures
    (Georgia Institute of Technology, 2006-08) Yang, Bong-Jun ; Calise, Anthony J. ; Craig, James I. ; Whorton, Mark S.
    A solar sail is an example of a gossamer structure that is proposed as an propulsion system for future space missions. Since it is a large scale flexible structure that requires a long time for its deployment, active control may be required to prevent it from deviating into a non-recoverable state. In this paper, we conceptually address control of an evolving flexible structure using a growing double pendulum model. Controlling an evolving system poses a major challenge to control design because it involves time-varying parameters, such as inertia and stiffness. By employing a neural network based adaptive control, we illustrate that the evolving double pendulum can be effectively regulated when fixed-gain controllers are deficient due to presence of time-varying parameters.
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    Adaptive Control for a Microgravity Vibration Isolation System
    (Georgia Institute of Technology, 2005-08) Yang, Bong-Jun ; Calise, Anthony J. ; Craig, James I. ; Whorton, Mark S.
    Most active vibration isolation systems that try to a provide quiescent acceleration environment for space-science experiments have utilized linear design methods. In this paper, we address adaptive control augmentation of an existing classical controller that combines a high-gain acceleration inner-loop feedback together with a low-gain position outer-loop feedback to regulate the platform about its center position. The control design considers both parametric and dynamic uncertainties because the isolation system must accommodate a variety of payloads having different inertial and dynamic characteristics. An important aspect of the design is the accelerometer bias. Two neural networks are incorporated to adaptively compensate for the uncertainties within the acceleration and the position loop. A novel feature in the design is that high-band pass and low pass filters are applied to the error signal used to adapt the weights in the neural network and the adaptive signals, so that the adaptive processes operate over targeted ranges of frequency. This prevents the inner and outer loop adaptive processes from interfering with each other. Simulations show that adaptive augmentation improves the performance of the existing acceleration controller and at the same time reduces the maximal position deviation and thus also improves the position controller.
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    Adaptive Control for a Microgravity Vibration Isolation System
    (Georgia Institute of Technology, 2005) Yang, Bong-Jun ; Calise, Anthony J. ; Craig, James I. ; Whorton, Mark S.
    Most active vibration isolation systems that try to a provide quiescent acceleration environment for space-science experiments have utilized linear design methods. In this report, we address adaptive control augmentation of an existing classical controller that combines a high-gain acceleration inner-loop feedback together with a low-gain position outer-loop feedback to regulate the platform about its center position. The control design considers both parametric and dynamic uncertainties because the isolation system must accommodate a variety of payloads having different inertial and dynamic characteristics. We show how adaptive control is beneficial in three important aspects in design of a controller for uncertain systems: performance, robustness, and transient responses. First, performance is treated in the setting that an accelerometer and an actuator is located at the same location, as is the current hardware configuration for g-LIMIT. Second, robustness for the control system becomes more of an issue when the sensor is non-collocated with the actuator. We illustrate that adaptive control can stabilize otherwise unstable dynamics due to the presence of unmodelled dynamics. Third, transient responses of the position of the isolation system are significantly influenced by a high-gain acceleration controller when it includes integral action. An important aspect of the g-LIMIT is the accelerometer bias and the deviation of the platform it causes as a result of integral control. By employing adaptive neural networks for both the inner-loop and outer-loop controllers, we illustrate that adaptive control can improve both steady-state responses and transient responses in position. A feature in the design is that high-band pass and low pass filters are applied to the error signal used to adapt the weights in the neural network and the adaptive signals, so that the adaptive processes operate over targeted ranges of frequency. This prevents the inner and outer loop adaptive processes from interfering with each other.