Aerospace and Electronic Systems Magazine July 2018 - 68

Integrated Attitude-Orbit Dynamics and Control of Spacecraft Systems

ACKNOWLEDGMENT

TUNING OF STATE ESTIMATOR
Noisy sensor observations, approximations in the system dynamics equations, and imperfect external factors model result in some
uncertainties on the estimated values for a spacecraft's state. Having an appropriate state estimation system is essential to model
an accurate IOADC system. The performance of state estimators
depends on the description of the noises in the plant dynamics and
in the sensor observations. The parameters of the noise densities
are treated as "tuning parameters".

The authors gratefully acknowledge the University Kebangsaan
Malaysia (UKM) for the partial financial support for the project
under research grant GUP-2015-052.

REFERENCES
[1]
[2]

MATHEMATICAL AND COMPUTATIONAL LOAD
Complex dynamics model of IOADC causes a high computational
load on the ground and onboard the spacecraft. However, computing equipment is becoming more powerful and less expensive.

[4]

[5]

LIMITED HARDWARE REDUNDANCY
Implementation of hardware redundancy in low-cost spacecraft
becomes more difficult. Implementation of a single actuator for
both attitude and orbit correction is more complex than different
multiple actuators. Consequently, the control system must be modeled for a single actuator system.

[6]
[7]
[8]

[9]

CONCLUSION
Disturbing torques in a space environment depend on the spacecraft's orbital position and velocity, and the dependence of the
space environment disturbing forces on the spacecraft's attitude
generate some nonlinear terms in the spacecraft dynamics model. In the IOADC architecture, all of these nonlinear dynamics
terms are considered. It was concluded that IOADC algorithms
are characterized by high computational load and high accuracy
with respect to SOADC techniques. Moreover, mathematical
models and advantages and disadvantages of attitude determination algorithms such as the TRIAD method and q-method
and attitude estimation techniques such as Kalman filter have
been provided. The TRIAD and q-method algorithms need two
nonparallel unit vector measurement pairs to determine the attitude of the spacecraft. The TRIAD method is a deterministic
and nonoptimal attitude determination algorithm, while the qmethod is an optimal solution for attitude determination. Kalman filter was able to optimally estimate the spacecraft attitude
using only one set of observations. The Kalman filter algorithms apply both sensor measurements and system dynamics
to estimate the spacecraft attitude. Additionally, the discussion
also included the single spacecraft's orbital and attitude motions model, and some popular spacecraft control algorithms
have been compared in this article. Then, several main challenges that should be considered for the IOADC architecture
have also been mentioned. Finally, this article concluded future
spacecraft OADC subsystems will employ IOADC to improve
accuracy and performance.
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[3]

[10]

[11]
[12]

[13]

[14]

[15]

[16]
[17]

[18]

IEEE A&E SYSTEMS MAGAZINE

Kaplan, M. H. Modern Spacecraft Dynamics and Control, Vol. 1. New
York: John Wiley and Sons, Inc., 1976, p. 427.
Lin, A. K., and Lee, R. Attitude control for small spacecraft with sensors error. In Proceedings of the AIAA SPACE 2015 Conference and
Exposition, 2015, p. 4423.
Markley, F. L., and Crassidis, J. L. Fundamentals of Spacecraft Attitude Determination and Control, Vol. 33. Springer, 2014.
Forbes, J. R. Fundamentals of spacecraft attitude determination
and control [bookshelf]. IEEE Control Systems, Vol. 35, (2015),
56-58.
Sidi, M. J. Spacecraft Dynamics and Control: A Practical Engineering Approach, Vol. 7. Cambridge University Press, 1997.
Wertz, J. R. Spacecraft Attitude Determination and Control, Vol. 73.
Springer Science & Business Media, 2012.
King-Hele, D. A Tapestry of Orbits. Cambridge University Press,
2005.
Pyragas, K. Delayed feedback control of chaos. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and
Engineering Sciences, Vol. 364, (2006), 2309-2334.
Yamashita, T., Ogura, N., Kurii, T., and Hashimoto, T. Improved satellite attitude control using a disturbance compensator. Acta Astronautica, Vol. 55, (2004), 15-25.
Faragher, R. Understanding the basis of the Kalman filter via a simple
and intuitive derivation. IEEE Signal Processing Magazine, Vol. 29,
(2012), 128-132.
Morris, A., and Sterling, M. Model tuning using the extended Kalman
filter. Electronics Letters, Vol. 15, (1979), 201-202.
Soken, H. E., and Sakai, S.-i. Adaptive tuning of the unscented Kalman filter for satellite attitude estimation. Journal of Aerospace Engineering, Vol. 28, (2014), 04014088.
Crassidis, J. L., Markley, F. L., and Cheng, Y. Survey of nonlinear attitude estimation methods. Journal of Guidance, Control, and Dynamics, Vol. 30, (2007), 12-28.
Boskovic, D. M., and Krstic, M. Global attitude/position regulation
for underwater vehicles. International Journal of Systems Science,
Vol. 30, (1999), 939-946.
Stevens, B. L., Lewis, F. L., and Johnson, E. N. Aircraft Control and
Simulation: Dynamics, Controls Design, and Autonomous Systems.
John Wiley & Sons, 2015.
Montenbruck, O., and Gill, E. Satellite Orbits: Models, Methods and
Applications. Springer Science & Business Media, 2012.
Kim, Y.-R., Park, E., Choi, E.-J., Park, S.-Y., Park, C., and Lim, H.C. Precise orbit determination using the batch filter based on particle
filtering with genetic resampling approach. Advances in Space Research, Vol. 54, (2014), 998-1007.
Sun, X., Chen, P., Macabiau, C., and Han, C. Autonomous orbit determination via Kalman filtering of gravity gradients. IEEE Transactions
on Aerospace and Electronic Systems, 2016.

JULY 2018

Table of Contents for the Digital Edition of Aerospace and Electronic Systems Magazine July 2018