New Millennium ST6 Autonomous Rendezvous Experiment (ARX)
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Engineering topics
Publications and source records attributed to Wong, E. C..
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This paper presents an overview of the New Millennium Space Technology 6 (ST6) Autonomous Rendezvous Experiment (ARX) mission and system.
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To achieve in-flight wobble compensation for Galileo, wobble identification is implemented using star scanner data or automatic gain control (AGC) signal as measurement in all-spin mode. The star scanner provides spacecraft attitude in inertial space while the AGC signal provides the spacecraft pointing relative to earth. A linear observation model is defined for each sensor which is being applied to a Kalman Estimator. It can be shown from simulation that better result can be achieved using a combined set of data than any one sensor alone due to correlation reduction among error sources.
The requirements, design, and expected performance of the Attitude Control Subsystem for the spin-stabilized Extreme Ultraviolet Explorer Satellite are presented. In the sky-mapping phase, closed-loop magnetic control keeps the spin axis pointed toward the sun. In the spectroscopy phase, the attitude control loop is closed via the ground. The satellite's attitude and spin rate are determined using periodically downlinked star data. An attitude control algorithm generates commands to be uplinked to the satellite for spin axis precession and spin rate control. Computer simulations of the satellite dynamic response, pointing error, and stability during spin axis precession are presented, and parameters that affect the pointing performance are evaluated.
Previously cited in issue 21, p. 3639, Accession no. A81-44132
Linear discrete stochastic control systems containing unknown multiple time delays, plant parameters and noise variances are considered. An algorithm is established which uses the maximum-likelihood technique to identify the unknown parameters. An estimated likelihood function is evaluated based on the previous parameter estimates, which in turn generates a new descent direction vector to update the unknown parameters. The delays and plant parameters are identified in their respective parameter spaces. An example of a second-order stochastic system has been implemented by digital simulation to demonstrate the applicability of the algorithm.
A scheme has been developed and verified for closed-loop tracking and pointing space-borne science instruments at small celestial bodies, such as comets and asteroids, during high velocity encounters. To overcome ephemeris uncertainties for these bodies, the scheme involves sequential estimation of flyby model parameters. The design consists of a two-axis gimballed platform mounted on a three-axis stabilized spacecraft. A platform-mounted optical tracker provides closed-loop target measurements and precision micro-step actuators enable high-rate platform slewing. For comet missions which involve dust particle impact disturbances, a dual-mode attitude control scheme is presented for minimizing transient response time.
A batch mode process which identifies three reference stars within a rotor-mounted star scanner's field-of-view based on the criteria of intensity and geometry was established. The sequential mode which continuously tracks the reference stars provides star transit times and estimates of rotor's spin rate. A least-square estimator was formulated which sequentially determines the spacecraft attitude from sucessive star crossings by minimizing the error in the star and scanner slit normal orthogonality. This spacecraft attitude also provides intermittent updates for the gyro propagated inertial attitude of the despun science platform. Simulation results are presented, showing successful star identification and attitude convergence in the presence of nutation and star transit time uncertainty.
Design and performance evaluations of an onboard inertial attitude determination system for a dual-spin planetary spacecraft are presented. A quaternion integration algorithm and a rate estimator sequentially determine the scan platform's inertial attitude and rate from the drift and misalignment compensated gyro outputs. A least-squares estimator algorithm processes the star transit data from a rotor-mounted star scanner, and provides periodic updates of the scan platform's attitude as a means of correcting the drift of the quaternion integration algorithm. Computer simulated algorithm performance in the presence of nutation and sensor noise are presented.
The paper deals with the problem of system identifiability for a linear dynamical system. Two theorems are given relating the sufficient condition of system identifiability for certain linear structures to the total number of inputs and outputs. The principle of least squares, that seeks the minimization of a cost function is employed to carry out the system identification process. To illustrate the concept of the paper, a structural model of a beam with point masses is examined. Parameter identification methods are studied and a random search technique is introduced.
An entirely autonomous attitude determination algorithm has been developed for the dual spin Galileo spacecraft in its mission to Jupiter. A batch mode process is established which identifies three stars within the scanner's field-of-view based on the criteria of intensity and geometry. This is followed by a continuous star acquisition procedure which provides star transit times and a spacecraft spin rate estimate. A least-squares estimator then sequentially determines the spacecraft's attitude from successive star crossings by minimizing an error derived using the necessary condition of star and scanner slit normal orthogonality. Simulation results are presented, showing successful star identification and attitude convergence in the presence of nutation and star transit time uncertainty.
An identification algorithm that uses the maximum likelihood technique to identify the unknown time delays, plant parameters, and noise covariances of linear discrete stochastic systems is presented. Cases of additive white noise and colored measurement noises are considered. The likelihood function is evaluated using either a minimum-variance (Kalman) filter or a minimal-order observer. The Kalman filter is used in the identification algorithm to provide minimum-variance estimates. The minimal-order observer is a lower-dimensional and computationally simpler filter, and is advantageous especially for systems with long delays. It provides a less optimal solution to the minimum-mean-square state estimation problem. The colored-noise observer algorithm has the disadvantage of having to compute an extra error covariance matrix of lower order.