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Journal of Computational Intelligence and Secure Systems of Artificial Intelligence
Volume 01, Issue 01, May 2025
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Implementation of Fuzzy for Satellite Attitude Control by Two State
Actuator to Reduce Limit Cycle
Sridhar
Department of Electrical and Computer Science Engineering, Lingayas Engineering Institute, Delhi
Corresponding Author: sridharece@gmail.com
To Cite this Article
Sridhar, “Implementation of Fuzzy for Satellite Attitude Control by Two State actuator to reduce Limit Cycle”,
Journal of Computational Intelligence and Secure Systems of Artificial Intelligence, Vol. 01, Issue 01, May 2025,
pp:01-04,
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Abstract: This paper presents an algorithm used to control the attitude of satellites in their orbit with the goal of
reducing fuel consumption and increasing operational lifespan. A fuzzifying controller was therefore deployed to save
fuel while dealing with the uncertainties and nonlinearities of the satellite control system based on its effective
performance and simplicity. The suggested control algorithm displays an extremely high level of reliability in the face
of adverse unintended disturbances consistent with the satellite’s constraints. Low-frequency limit cycles result from
the natural chatter of the on-off controllers. This results in the increase of system error and the greater fuel
consumption for the satellite. To minimize the system error, the algorithm of Particle Swarm Optimization (PSO) was
used. Simulation results for the satellite show that the fuzzy on-off controller is greatly enhanced once this algorithm
is applied.
Keywords: PSO, Limit cycle, Satellite attitude control, Fuzzy on-off control
This is an open access article under the creative commons license https://creativecommons.org/licenses/by-nc-nd/4.0/
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I. Introduction
The control systems of satellite attitude ability is to save fuel is very desirable. The control of satellite attitude
is based on two-level on-off controllers that are typically utilized in conjunction with the thrust reaction actuator.
These controllers are time independent and respond quickly. With or without propulsion power, they quickly adjust
the satellite's attitude. The valves in on-off control systems are dependable enough to remain open for a few
milliseconds. The actuations effect the discrete angular velocity of the satellite when it is altered then the valves are
fully opened for a limited period of time. Consequently, zero residual angular quickness cannot be achieved. A dead
band is placed between the on-off control and the controller is turned off in this dead band area to stop thruster
engagement. As a result, the controlled system either increases wetness or decreases velocity to attain the equilibrium
position. This diminishes the thruster force and produces low frequency limit cycles. Fuzzy logic and other nonlinear
control algorithms are advised due to the satellite's intrinsically unpredictable and nonlinear behavior. The accuracy
of the microsatellite model has no bearing on this approach. To stabilize a small spacecraft in low earth orbit, Stein
used three fuzzy controllers with several inputs and one output [2-4].
By selecting the optimal magnetic moment, polarity, and switching periods, he demonstrated that fuzzy
controllers may eliminate control constraints [1]. A satellite control system can improve satellite performance and
save fuel. In reference [2], on-off attitude control using on-off and sliding mode was examined. Sliding mode
controllers have the drawback of producing a large control signal because of system uncertainty [1].
II. Satellite State Space Model with Three Degrees of Freedom
This issue was resolved by using a fuzzy controller [3]. A fuzzy controller is a suitable option for managing
nonlinear systems. A key consideration in the design of fuzzy controllers is minimizing the amount of time needed
for the system to reach the steady state. Membership functions are optimally adjusted to do this [4]. Different initial
settings are required by the controller studied in reference [5] in order to enhance system performance and reduce
response time.
However, ordinary linear controllers are not appropriate for these applications. An on-off controller is a
suitable choice in this situation [6,7]. After comparing several controllers, Reference [4] determined that the fuzzy on-
Implementation of Fuzzy for Satellite Attitude Control by Two State actuator to reduce Limit Cycle
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off controller is the most effective one in terms of efficiency. A multi-level relay is a fuzzy controller. It defuses using
the average least squares approach. In this research, control signals from defuzzification were converted using
specialized hardware. In reference [6], the fuzzy on-off controller's minimum control time using a relay was
demonstrated [3-5].
Fig 1: Satellite reference and body coordinates
III. Algorithm Particle Swarm Optimization
A fixed number of particles with a random start value comprised in the particle swarm optimization approach.
The values of velocity and attitude of the particles are indicated. A location vector and a velocity vector, in turn,
represent the above quantities. Exploring the problem’s n-dimensional space and looking for new possibilities, these
particles move across the space using the optimality value as the assessment parameter. The number of the useful
parameters of the optimization function is equal to the dimension of the problem space. Memory areas are separated
for recording the particle with the best conditions and the previous best location of the particle [4].
Particles make future motion decisions based on these memories. Every particle moves in the n-dimensional
issue space during the repetitions. The public optimal point has finally been identified. Depending on the best local
and public solutions, particles alter their location and velocity.
Fig 2: General structure of particle swarm algorithm
Implementation of Fuzzy for Satellite Attitude Control by Two State actuator to reduce Limit Cycle
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IV. Simulation
This section analyzed the response of this system to initial conditions, or the zero input response. The roll
angle oscillates for the rolling fuzzy on-off controller in Figures 3 and 4 after 18 seconds, with amplitude 0.04 radians
(3.2 degrees) and a frequency of 0.014 hertz. The phase plane trajectory shows that the time response decays towards
the origin where rate and location are both zero as rate feedback attenuates the system.
Fig 6: Roll angle operation of fuzzy on-off controller with dead band (nonlinear model)
Fig 7: Roll angle operation of fuzzy on-off controller (T-S model)
V. Conclusion
A simulation of the fuzzy on-off controller algorithm was demonstrated. This controller was installed on a
three-degree of freedom satellite model nonlinear system. From the simulation results, the current fuzzy on-off control
makes the system refractory, resistant and stable with good disturbance rejection.
The fuzzy system was optimized and the limit cycle's oscillation amplitude was decreased using the particle
swarm approach, which was derived from the absolute error integral. As a result, satellite longevity decreases and fuel
consumption rises. The method requires fewer parameters for tuning and has a high rate of convergence. The controller
used the optimization approach to complete the tracing without steady-state error based on the results. The amplitude
of the output oscillations was significantly lower than that of the other controllers. This approach was also used to
lower the power consumption of the thrusters and the temporal damping system.
Implementation of Fuzzy for Satellite Attitude Control by Two State actuator to reduce Limit Cycle
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References
[1] Srikanth Iyengar and Shang Lee Chei. “XZ-YZ-Source Technology Inverter SMPS”. Springer Proceedings Ind. Appl. 2013; 32(5): 210–218.
[2] Hugh Man, Diesel, Shang Chie and Naresh Kumar. “Computational Intelligence in Pulse width modulation of Z-source inverters”. IEEE
Trans. Control Power Systems. 2015; 22(8): 12–22.
[3] J Liu, J Hu, and L Xu. “Dynamic modelling and analysis of Z-source converter—Derivation of ac small signal model and design-oriented
analysis”. IEEE Trans. Power Electron. 2007; 22(5): 1786–1796.
[4] Chie Lee and Yadav Kumar. “An Matrix Converter using Array System in Power Electronics in Communication Systems”. Springer
Conference in Hindustan University, Chennai, VOL. 2, NO. 3, March 2009
[5] Saritha, Srikanth, Subhakar and Sunitha, “A Process control system using Fuzzy in Industrial Applications using Thyristors in power
electronics for PMSG”,”. Elsevier 2011. China, 7 – 9, January 2012.
[6] Niharika, Lakshman Reddy and Shanchie, “A Novel of MIMO concepts in wireless relay networks in Space Time and Space Frequency in
achieve diversity”, ” IEEE Conference Proceedings on Innovative Research in Communication Systems (IRCS), International Conference.
vol. 2, pp. 67 – 75, January. 2010
[7] John Diesel, Shang Chee and Cooper Lee, “Implementation of Fuzzy using Artificial Intelligence in Standalone Grid system for On and OFF
modes Using Renewable energy sources using PMMC Technology’, ”Springer Proceedings on Green Energy on World environmental Day”,
IEEE conference proceedings held at Madras University, on the 20tt Century. pp.10-19, 2020