Aerospace and Electronic Systems - July 2019 - 18

Feature Article:

DOI. No. 10.1109/MAES.2019.2916535

Design of a TTC Antenna Using Simulation and
Multiobjective Evolutionary Algorithms
, Madrid,
Javier Moreno, Ivan Gonzalez, Daniel Rodrıguez, University of Alcala
Spain

INTRODUCTION
In this paper, we present a case study about how to apply
simulation-optimization, i.e., the application of simulation together with a multiobjective algorithm can help us
to optimize the design parameters of an antenna with very
stringent constrains. The objective is to obtain a compact
dual-band helical antenna for Telemetry, Tracking, and
Control (TTC) of satellites. A TTC subsystem provides
the communication between a satellite and a ground station, as illustrated in Figure 1. The Telemetry system monitors the satellite retrieving its health and status of other
subsystems and sending data to the station. The Tracking
subsystem manages the satellite position in its orbit while
the Control subsystem allows us to command the satellite,
reconfiguring it if necessary. Within a TTC system, the
antenna is undoubtedly the most critical part. The antenna
must guarantee a proper operation within the established
parameters and due to its constraints, its design can be
extremely complex.
In our case study, the antenna needed to be able to
operate in the S-Band at 1.81 and 2.55-GHz frequencies:
Minimizing the cross-polarization level.
Maximizing the gain for the Right-Hand Circular
Polarization (RHCP).
In our case study, the parameters of the antenna that
fulfills the radiation patterns needed for the communication are obtained using a simulation tool called MONURBS [1] together with two well-known multiobjective
algorithms: Non-dominated Sorting Genetic Algorithm
Authors' current address: J. Moreno, I. Gonzalez, and
D. Rodrıguez, Department of Computer Science,
University of Alcala, Madrid 28805, Spain, E-mail:
(javier.morenom@edu.uah.es).
Manuscript received August 30, 2018, revised
December 13, 2018, and ready for publication May 10,
2019.
Review handled by M. D. R-Moreno.
0885-8985/19/$26.00 ß 2019 IEEE
18

(NSGA-II) [2] and the Strength Pareto Evolutionary
Algorithm-2 (SPEA-2) [3].
In this paper, a comparison with previous designs and
the antenna prototype is presented, showing that simulation-optimization can obtain multiple valid solutions and
accelerate the design process.

PREVIOUS EXPERIENCE AND MOTIVATION
In a previous work, Gonzalez et al. [4] presented the
design of a compact dual-band helical antenna for TTC
applications in satellites. In [5], we proposed the use of
NSGA-II to reduce the cost of time and optimize the
design of the helical antenna. Here, we extend our proposal by applying the SPEA-2 algorithm to obtain multiple valid solutions and to expedite the process in future
designs. The initial work was immersed in a ESA project
20995/NL/ST/na, "S-Band Toroidal Antenna," where the
main contractor was RYMSA.1
Although the geometric model is quite simple, it needs
to be parametrized according to rigorous requirements
where there are several objectives that the optimization
process has to deal with. In the previous work [4], the optimization process was carried out applying the Gradient
Descent (GD) algorithm with a simulation tool called
MONURBS to analyze and obtain the radiation pattern of
the antenna. This GD method was used with a cost function that depended on the antenna requirements. However,
it resulted in a very complex problem with a large number
of maximums and minimums where the application of the
GD method was difficult and not appropriate (it was more
like a random sampler in the search space). A huge number of simulations were needed to obtain a valid solution
that satisfied all the requirements simultaneously. It was,
therefore, an extremely CPU intensive task that needed a
very large time span (several months). As a consequence,
we started tackling this problem as a case study applying
multiobjective optimization techniques.
1

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Aerospace and Electronic Systems - July 2019

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