Aerospace and Electronic Systems Magazine July 2018 - 66

Integrated Attitude-Orbit Dynamics and Control of Spacecraft Systems
gimes. Manufacturing and launching procedure of the spacecraft
are highly costly [53]. Since the early years of the space program,
several types of research have been performed to assess and improve the reliability and availability of the spacecraft attitude and
orbit control systems [54]. The automated control systems are
highly demanding, and consequently, safety has always been important in the final design stage. Control system safety is twofold.
First, a control system has to be safe by not generating hazardous
events. Second, a control system performs a safety function by
monitoring a physical process, detecting failures, and potentially
hazardous deviations in order to undertake reactive actions aiming
at putting the system in a safe state or to warn an operator [55]. In
addition, high-performance- and high-efficiency-optimized control systems are interested in serving the sophisticated spacecraft
mission in different regimes. The optimized controllers are used
to eliminate the overshoot, achieve minimal rise time and settling
time, and minimize steady-state error [30], [56], [57].
Besides, to tackle these issues in the field of control design,
new solutions such as dynamic inversion, predictive control, linear
parameter-varying control, H-infinity and H2 (mixed) approaches
are being proposed [58]. Automatic control field proposes several
methods of which each has a main kind of application. To keep determined variables at a constant value within some errors, the regulation technique is proper. There are some other techniques that focus
on appropriate transient characteristics. For instance, a maneuver
that moves from an initial system state vector to a new system state
vector with minimum time consumption. Moreover, there are techniques that aim at good trajectory tracking. Scharf et al. [19] provide a review of spacecraft formation flying guidance. This study
includes both path planning and optimal, open-loop control design.
Transfer functions propose intuitive visual methods to evaluate the essential properties of the control system such as stability,
gain and phase margins, rejection of undesired frequencies, and
many others [59]. However, this technique focused on single-input
single-output (SISO) plants. At the beginning of the space era, the
state-space approach was a new control technique that was developed by the advent of powerful computers. This control methodology centered on the control of multiple-input multiple-output
(MIMO) plants [60]. In this technique, vectors and matrices were
systems representation terms that compromised well with computer capabilities. Still, the state-space technique is a time-domain
framework, and in this domain, there are no intuitive graphical
tools. Later, other techniques and algorithms such as the Kalman
filter, the Linear Quadratic Regulator (LQR), and optimization objectives were developed [61]. The state-space feedback algorithms
required access to real-time measurement of states. However, usual
outputs of the plant can be observable. Observer techniques were
developed in order to solve this challenge.
Table 1 shows a comparison of some popular spacecraft control
algorithms. Consequently, optimality and stability are the main advantages of MIMO algorithms. However, MIMO architectures need
to use the entire state of the spacecraft dynamics and highest information of the system. Robustness and high performance are the primary advantages of the SISO algorithms. Simplicity and robustness
are the main advantages of PID controllers. However, since tuning
of the control parameters of the PID controllers is by trial and error,
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the control gains are, therefore, not optimized. LQR controllers are
suitable for high stability and optimal control of spacecraft.

OPTIMIZATION ALGORITHMS BASED ON CONTROLLER
TECHNIQUES
At the end of the last century, optimization algorithm techniques
have been created to be applied in many applications in order to
find the best solution for many problems. These optimization algorithm techniques are classified according to the underlying principle of biological- and physical-based algorithm. The first category
is biology-based algorithm such as genetic algorithm (GA), harmony search algorithm, particle swarm optimization (PSO), bacterial foraging optimization, cuckoo search algorithm, bee colony
algorithm, ant colony optimization, firefly algorithm, backtracking
search optimization algorithm (BSA), lightening search algorithm,
and many others. The second category is physics-based algorithm
such as simulated annealing, gravitational search algorithm, chaotic
optimization algorithm, etc. [81]. Table 2 shows some of the previous works on optimization algorithms-based controller techniques.
All the optimization techniques and their variants do not provide
superior solutions to some specific problems. Furthermore, even
though some of the optimization techniques are efficient, it still needs
improvement so as to further enhance their performance [94]. Moreover, the manner in which the convergence of an algorithm can be
sped up is still a very challenging question. Therefore, new natureinspired optimization techniques must be continuously searched to advance the field of computational intelligence or heuristic optimization.

ISSUES AND CHALLENGES
IOADC has a promising future for more accurate OADC subsystem option of spacecraft for the next generation [39], [95]. However, there are some issues that need to be considered before applying
IOADC. The issues and challenges with the design of IOADC are
discussed in the following subsections.

COUPLED DYNAMICS AND NONLINEARITIES
Naturally, the spacecraft motion equations are nonlinear, and there
is coupling between attitude and orbit dynamics. To increase the
accuracy of the control architecture, these nonlinear terms must be
taken into account for spacecraft control mathematical model and
simulation. Consequently, the IOADC model with a nonlinear state
estimator method, such as the EKF, is recommended.

MEASUREMENTS NOISE, EXTERNAL PERTURBATIONS,
AND UNCERTAINTIES OF MODEL
The noise of sensor measurements and uncertainties of model
pose complications. Besides, disturbance sources of the space
environment give rise to further issues and challenges in spacecraft OADC subsystem. Spacecraft vehicle can be simply driven
to an unstable state in cases where control schemes are not properly designed.

IEEE A&E SYSTEMS MAGAZINE

JULY 2018

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