Space trajectories and errors in time, frequency, and tracking station location
Influence on trajectory determination of error in tracking station location and station time and frequency synchronization
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Influence on trajectory determination of error in tracking station location and station time and frequency synchronization
Commands are found to drive a linear system to optimally track a class of prescribed trajectories, each of which contains a point of discontinuity. The paper focuses on the guidance problem of command generation, to be implemented in a feedforward fashion; the necessary additional control or feedback regulation structure is not studied in detail, but assumed to be provided in applications as a conventional error-feedback regulator. Solutions are found for the infinite-time problem which are optimal with respect to a quadratic performance criterion; suboptimal controls which satisfy a continuity condition are also found. The controls have applications to certain problems in aircraft guidance where command trajectories are piecewise continuous. Several examples are worked out in detail, with comparisons with conventional, nonfeedforward solutions to the problem, and a brief discussion of a simpler, suboptimal solution.
Reduced-order models are considered in the context of parameter adaptive controllers for tracking workspace trajectories. A dual-arm manipulation task is used to illustrate the methodology and provide simulation results. A parameter adaptive controller is designed to track a payload trajectory using a four-parameter model instead of the full-order, nine-parameter model. Several simulations with different payload-to-arm mass ratios are used to illustrate the capabilities of the reduced-order model in tracking the desired trajectory.
The postflight trajectory for the Apollo/Saturn V SA-513 Skylab I flight is presented. An analysis is included of the orbital and powered flight trajectories of the launch vehicle, the orbital trajectory of the spent S-II stage, and the free flight impact trajectory of the expended S-IC stage. Launch vehicle trajectory dependent parameters are provided in earth-fixed launch site, launch vehicle navigation, and geographic polar coordinate systems. The time history of the trajectory parameters for the launch vehicle is presented from guidance reference release to the transfer to ATM control. Tables of significant launch vehicle parameters at engine cutoff, stage separation, and workshop orbit insertion are included. Figures of such parameters as altitude, surface and cross range, and the magnitude of total velocity and acceleration as a function of range time for the powered flight trajectory are given.
The authors use the concept of sliding surfaces for generating two types of tracking control laws for a free-flying robot engaged in zero-gravity assembly tasks. Suction control, developed elsewhere for controlling manipulators with stationary bases, is used here to track workspace trajectories for manipulators mounted on mobile platforms. Zone control is formulated for the purpose of stationkeeping a robot maneuvering unit during payload manipulation. Experimental results are described for tests performed on an air-bearing robot tracking payload trajectories along a glass surface.
Three strategies for adaptive control of cooperative dual-arm robots are described. In the position-position control strategy, the adaptive controllers ensure that the end-effector positions of both arms track desired trajectories in Cartesian space despite unknown time-varying interaction forces exerted through the load. In the position-hybrid control strategy, the adaptive controller of one arm controls end-effector motions in the free directions and applied forces in the constraint directions, while the adaptive controller of the other arm ensures that the end-effector tracks desired position trajectories. In the hybrid-hybrid control strategy, the adaptive controllers ensure that both end-effectors track reference position trajectories while simultaneously applying desired forces on the load. In all three control strategies, the cross-coupling effects between the arms are treated as disturbances which are rejected by the adaptive controllers while following desired commands in a common frame of reference. The adaptive controllers do not require the complex mathematical model of the arm dynamics or any knowledge of the arm dynamic parameters or the load parameters such as mass and stiffness. The controllers have simple structures and are computationally fast for on-line implementation with high sampling rates.
The described and improved multi-arm invention of this application presents three strategies for adaptive control of cooperative multi-arm robots which coordinate control over a common load. In the position-position control strategy, the adaptive controllers ensure that the end-effector positions of both arms track desired trajectories in Cartesian space despite unknown time-varying interaction forces exerted through a load. In the position-hybrid control strategy, the adaptive controller of one arm controls end-effector motions in the free directions and applied forces in the constraint directions; while the adaptive controller of the other arm ensures that the end-effector tracks desired position trajectories. In the hybrid-hybrid control strategy, the adaptive controllers ensure that both end-effectors track reference position trajectories while simultaneously applying desired forces on the load. In all three control strategies, the cross-coupling effects between the arms are treated as disturbances which are compensated for by the adaptive controllers while following desired commands in a common frame of reference. The adaptive controllers do not require the complex mathematical model of the arm dynamics or any knowledge of the arm dynamic parameters or the load parameters such as mass and stiffness. Circuits in the adaptive feedback and feedforward controllers are varied by novel adaptation laws.
The described and improved multi-arm invention of this application presents three strategies for adaptive control of cooperative multi-arm robots which coordinate control over a common load. In the position-position control strategy, the adaptive controllers ensure that the end-effector positions of both arms track desired trajectories in Cartesian space despite unknown time-varying interaction forces exerted through a load. In the position-hybrid control strategy, the adaptive controller of one arm controls end-effector motions in the free directions and applied forces in the constraint directions; while the adaptive controller of the other arm ensures that the end-effector tracks desired position trajectories. In the hybrid-hybrid control strategy, the adaptive controllers ensure that both end-effectors track reference position trajectories while simultaneously applying desired forces on the load. In all three control strategies, the cross-coupling effects between the arms are treated as disturbances which are compensated for by the adaptive controllers while following desired commands in a common frame of reference. The adaptive controllers do not require the complex mathematical model of the arm dynamics or any knowledge of the arm dynamic parameters or the load parameters such as mass and stiffness. Circuits in the adaptive feedback and feedforward controllers are varied by novel adaptation laws.
Reduced-order models are considered in the context of parameter adaptive controllers for tracking workspace trajectories. A dual-arm manipulation task is used to illustrate the methodology and provide simulation results. A parameter adaptive controller is designed to track the desired position trajectory of a payload using a four-parameter model instead of a full-order, nine-parameter model. Several simulations with different payload-to-arm mass ratios are used to illustrate the capabilities of the reduced-order model in tracking the desired trajectory.
Chemical data from flight 8 of NASA's Subsonic Assessment (SASS) Ozone and Nitrogen Oxide Experiment (SONEX) exhibited signatures consistent with aircraft emissions, stratospheric air, and surface-based pollution. These signatures are examined in detail, focussing on the broad aircraft emission signatures that are several hundred kilometers in length. A mesoscale meteorological model provides high resolution wind data that are used to calculate backward trajectories arriving at locations along the flight track. These trajectories are compared to aircraft locations in the North Atlantic Flight Corridor over a 27-33 hour period. Time series of flight level NO and the number of trajectory/aircraft encounters within the NAFC show excellent agreement. Trajectories arriving within the stratospheric and surface-based pollution regions are found to experience very few aircraft encounters. Conversely, there are many trajectory/aircraft encounters within the two chemical signatures corresponding to aircraft emissions. Even many detailed fluctuations of NO within the two aircraft signature regions correspond to similar fluctuations in aircraft encountered during the previous 27-33 hours. Results indicate that high resolution meteorological modeling, when coupled with detailed aircraft location data, is useful for understanding chemical signatures from aircraft emissions at scales of several hundred kilometers.
An error analysis has been made of a Shuttle postflight entry trajectory reconstruction process to obtain trajectory state estimation errors and to assess the impact of these errors on Shuttle aerodynamic force coefficient extraction. In this analysis, the entry trajectory is assumed to be reconstructed via numerical integration of onboard accelerometer and gyro measurements and constrained to satisfy ground-based radio tracking. The trajectory state estimation errors are calculated using a Kalman-Schmidt sequential filter assuming various measurement error models and combinations of ground-based tracking. The resultant trajectory estimation errors are analyzed in a simplified perturbation process to establish the accuracy to which postflight aerodynamic force coefficients can be determined. Results are presented which show that the principal error sources affecting the trajectory reconstruction and thus the force coefficient extraction, assuming perfect atmospheric density knowledge, are the accelerometer and gyro resolution, acceleration-sensitive gyro drifts, and the alignment uncertainties associated with integration on the Shuttle.
The results of controlling A PUMA 560 Robotic Manipulator and the NASA shuttle Remote Manipulator System (RMS) using a Command Generator Tracker (CGT) based Model Reference Adaptive Controller (DMRAC) are presented. Initially, the DMRAC algorithm was run in simulation using a detailed dynamic model of the PUMA 560. The algorithm was tuned on the simulation and then used to control the manipulator using minimum jerk trajectories as the desired reference inputs. The ability to track a trajectory in the presence of load changes was also investigated in the simulation. Satisfactory performance was achieved in both simulation and on the actual robot. The obtained responses showed that the algorithm was robust in the presence of sudden load changes. Because these results indicate that the DMRAC algorithm can indeed be successfully applied to the control of robotic manipulators, additional testing was performed to validate the applicability of DMRAC to simulated dynamics of the shuttle RMS.
Payload and trajectory data for Nike Apache firings tracked by radar - January-June 1965
Pioneer project support - Doppler tracking data analysis, trajectory dispersions and initial tracking station acquisition, and gas leak effect on orbit
Surveyor 1 and 2 trajectories determined from tracking data
The trajectory of the Ulysses spacecraft caused its geocentric declination to exceed 60 deg South for over two months during the Fall of 1994, permitting continuous tracking from a single site. During this time, spacecraft operations constraints allowed only Doppler tracking data to be collected, and imposed a high radial acceleration uncertainty on the orbit determination process. The unusual aspects of this situation have motivated a re-examination of the Hamilton-Melbourne results, which have been used before to estimate the information content of Doppler tracking for trajectories closer to the ecliptic. The addition of an acceleration term to this equation is found to significantly increase the declination uncertainty for symmetric passes. In addition, a simple means is described to transform the symmetric results when the tracking pass is non-symmetric. The analytical results are then compared against numerical studies of this tracking geometry and found to be in good agreement for the angular uncertainties. The results of this analysis are applicable to the Near Earth Asteroid Rendezvous (NEAR) mission and to any other missions with high declination trajectories, as well as to missions using short tracking passes and/or one-way Doppler data.
The BioSentinel mission was recently launched aboard the SLS launch vehicle (LV) as part of the Artemis- 1 campaign. The BioSentinel navigation team successfully tracked and guided the spacecraft through a lunar gravity assist to its destination Earth-trailing heliocentric orbit. This 6U CubeSat carries live yeast cells to analyze the effects of radiation at large distances from Earth, becoming the first biological payload in Deep Space. Prelaunch activities included mission design updates, orbit determination rehearsals and the development of a tracking schedule in coordination with the Artemis-1 payload office and the Deep Space Network (DSN). An important influence on the trajectories of Artemis I secondaries was the uncertainty associated with deployment from the Interim Cryogenic Propulsion System (ICPS), the upper stage of the SLS LV. The ICPS was rotating at a rate of 1 rpm; there was also an uncertainty in the spin axis attitude, which translated into an unknown clock angle of deployment. The variability in this angle and magnitude of deployment implied the existence of a non-negligible risk of a lunar impact, which was evaluated for various potential launch dates. We present the results of Monte Carlo analyses and compute the pertinent maneuvers to avoid it. In addition, we present a comparison with the actual deployment once the mission launched by reconstructing our trajectory with tracking data. On November 16th 2022 BioSentinel successfully deployed from ICPS and the navigation team started to receive 2-way Doppler and Sequential Ranging data from the DSN. We processed early data to try to obtain a first ephemeris using Initial Orbit Determination (IOD) methods such as the least squares. Soon after deployment, the spacecraft was tumbling and entered safe mode, creating a period where the tracking data were sparse. The mission team recovered the spacecraft and after four tracking passes, we solved for a first ephemeris that was sent to the DSN for better tracking of the spacecraft. After propagating this first ephemeris solution, we determined that we avoided impact with a margin of a few hundred km from the lunar surface. More tracking data over the next few days (from DSN as well as ESA antennas) allowed for a more refined orbit solution predicting a periselene altitude of 406 km and a lunar eclipse lasting 36.5 minutes. Therefore, BioSentinel operators aborted any correction maneuvers. This periselene altitude also gave us the necessary energy to achieve a heliocentric orbit. The next challenge was due to the necessary adjustments in our orbit determination method due to the large energy boost resulting from the lunar flyby. After a series of tracking passes we were able to get a nominal solution that resulted into a stable trajectory. This paper discusses in detail the navigation performance using the X-band IRIS transponder, as well as the challenges and lessons learned prior to and during this deep space, CubeSat mission.
Payload and trajectory data for Nike Apache firings tracked by radar - July-December 1965