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Nonlinear Spacecraft Attitude Control via Cascade-Forward Neural Networks

Tamer Mekky Habib(1*)

(1) Spacecraft Division acting head, National Authority for remote sensing and space science, Egypt
(*) Corresponding author


DOI: https://doi.org/10.15866/ireaco.v13i3.19149

Abstract


The problem of spacecraft attitude control has been addressed in the literature via several linear and nonlinear algorithms. In the current study, cascade-forward neural networks have been used to mimic the behaviour of the High Performance Nonlinear Discrete Controller (HPNDC). Controller stability is proven via Lyapunov second method. The developed control algorithm has the ability to work during all of the spacecraft operational modes and alleviates many problems associated with other nonlinear algorithms existing in the literature. Performance of the algorithm has been tested against various nonlinear attitude control algorithms including HPNDC, Sliding Mode (SM) Controller, Nonlinear Dynamic Inversion (NDI)controller, and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) controller. The proposed neural networks control algorithm achieved a good performance compared to the aforementioned control algorithms. The behaviour of the HPNDC control algorithm has been used for training of the Neural Networks due to its numerous advantages when operating in linear and nonlinear operational modes. To prevent data overfitting, 10% of the training data set are used for testing. The training ratio is selected to be 90%. Gravity gradient torques acting on EGYPTSAT-1 spacecraft are considered the main acting disturbance. The developed control algorithm can nullify the initial high angular velocities and attitude angles of the spacecraft within two orbits.
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Keywords


Nonlinear; Control; Attitude; Lyapunov; Neural Networks; Cascade-Forward

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References


Wertz, J. R., Spacecraft Attitude Determination and Control. (D. Reidel Publishing Company, 1997).

Habib, T., New algorithms of nonlinear spacecraft attitude control via attitude, angular velocity, and orbit estimation based on the earth’s magnetic field, PhD Thesis, Cairo University, 2009.

Habib, T., Spacecraft Nonlinear Attitude Dynamics Control with Adaptive Neuro-Fuzzy Inference System, (2019) International Review of Automatic Control (IREACO), 12 (5), pp. 242-250.
https://doi.org/10.15866/ireaco.v12i5.18056

Hameed, A., Al-Dujaili, A., Humaidi, A., Hussein, H., Design of Terminal Sliding Position Control for Electronic Throttle Valve System: a Performance Comparative Study, (2019) International Review of Automatic Control (IREACO), 12 (5), pp. 251-260.
https://doi.org/10.15866/ireaco.v12i5.16556

Apolloni, B., Battini., F., and Lucisano, C., A Co-Operating Neural Approach for Spacecrafts Attitude Control, Neurocomputing, Vol. 16:279-307, 1997.
https://doi.org/10.1016/s0925-2312(97)00035-0

Zhao, L., Jia, Y., Neural Network-Based Distributed Adaptive Attitude Synchronization Control of Spacecraft Formation under Modified Fast Terminal Sliding Mode, Neurocomputing, Vol. 171: 230-241, 2016.
https://doi.org/10.1016/j.neucom.2015.06.063

Leeghim, H., Kim, D., Adaptive Neural Control of Spacecraft using Control Moment Gyros, Advances in Space Research, Vol. 55:1382-1393, 2015.
https://doi.org/10.1016/j.asr.2014.06.038

Zou, A., Krishna, K., Adaptive Attitude Control of Spacecraft without Velocity Measurements using Chebyshev Neural Network, Acta Astronautica, Vol. 66:769-779, 2010.
https://doi.org/10.1016/j.actaastro.2009.08.020

Krishna, K., Rickard, S., and Bartholomew, S., Adaptive Neuro-Control for Spacecraft Attitude Control, Neurocomputing, Vol. 9:131-148, 1995.
https://doi.org/10.1016/0925-2312(94)00062-w

Cheng, C., Shu, S., Adaptive Neural Control of Spacecraft using Control Moment Gyros, Aerospace Science and Technology, Vol. 14:241-249, 2010.

MacKunis, W., et al, Adaptive Neural Network-Based Satellite Attitude Control in the Presence of CMG Uncertainty, Aerospace Science and Technology, Vol. 54:218-228, 2016.
https://doi.org/10.1016/j.ast.2016.04.022

Wright, W., Agaian, S., Stochastic Tuning of a Spacecraft Controller Using Neural Networks, Eng. Applic. Artif. lntell, Vol. 8 No. 6:651-656, 1995.

Calvo, D., et al, Fuzzy Attitude Control for a Nanosatellite in Low Earth Orbit, Expert Systems with Applications, Vol. 58: 102-118, 2016.
https://doi.org/10.1016/j.eswa.2016.04.004

Hu, Q., Xiao, B., Intelligent Proportional-Derivative Control for Flexible Spacecraft Attitude Stabilization with Unknown Input Saturation, Aerospace Science and Technology, Vol. 23:63-74, 2012.
https://doi.org/10.1016/j.ast.2011.06.003

Seo, I., Leeghim, H., and Bang, H., Nonlinear Momentum Transfer Control of a Gyrostat with a Discrete Damper Using Neural Networks, Acta Astronautica, Vol. 62:357-373, 2008.
https://doi.org/10.1016/j.actaastro.2008.01.014

Kassem, A., Efficient Neural Network Modeling for Flight and Space Dynamics Simulation, International Journal of Aerospace Engineering, Vol. 2011:1-7, 2011.
https://doi.org/10.1155/2011/247294

Jang, J., Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm, Proc. of the Ninth National Conf. on Artificial Intelligence (AAAI-91), 762-767, 1991.

Jang, J., ANFIS: Adaptive-Network-based Fuzzy Inference Systems, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No.3: 665-685, 1993.
https://doi.org/10.1109/21.256541

Kim, S., Park, S., and Park, C., Spacecraft Attitude Control Using Neuro-Fuzzy Approximation of the Optimal Controllers, Advances in Space Research, Vol. 57:137-152, 2016.
https://doi.org/10.1016/j.asr.2015.09.016

Gayvoronskiy, S., Ezangina, T., Pushkarev, M., Khozhaev, I., Parametrical Synthesis of Linear Controllers in Aperiodical Systems on Basis of Decomposition Approach, (2019) International Review of Automatic Control (IREACO), 12 (4), pp. 192-199.
https://doi.org/10.15866/ireaco.v12i4.16401

Nayeri, M., Alasty, A., and Daneshjou, K., Neural Optimal Control of Flexible Spacecraft Slew Maneuver, Acta Astronautica, Vol. 55:817-827, 2004.
https://doi.org/10.1016/j.actaastro.2004.04.002

Greenblatt, A., Agaian, S., Introducing Quaternion Multi-Valued Neural Networks with Numerical Examples, Information Sciences, Vol. 423:326-342, 2018.
https://doi.org/10.1016/j.ins.2017.09.057

Metallaoui, S., Ahmida, Z., Boukelkoul, L., Improved Mode-Dependent State-Feedback Stabilization of Discrete-Time Networked Control Systems with Markovian Communication Delays, (2019) International Review of Automatic Control (IREACO), 12 (4), pp. 163-173.
https://doi.org/10.15866/ireaco.v12i4.16342

Khodja, M., Larbes, C., Ramzan, N., Ibrahim, A., Implementation of Heuristical PID Tuning for Nonlinear System Control, (2019) International Review of Automatic Control (IREACO), 12 (2), pp. 108-114.
https://doi.org/10.15866/ireaco.v12i2.16791

Khorchef, N., Mokhtari, A., Boudjemai, A., Multi-Scenarios Attitude Control of a Satellite with Flexible Solar Panels, (2018) International Review of Automatic Control (IREACO), 11 (6), pp. 326-335.
https://doi.org/10.15866/ireaco.v11i6.15266

Bukhtoyarov, V., Milov, A., Tynchenko, V., Petrovskiy, E., Tynchenko, S., Intelligently Informed Control Over the Process Variables of Oil and Gas Equipment Maintenance, (2019) International Review of Automatic Control (IREACO), 12 (2), pp. 59-66.
https://doi.org/10.15866/ireaco.v12i2.16790

Sunarno, E., Assidiq, R., Nugraha, S., Sudiharto, I., Qudsi, O., Eviningsih, R., Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage, (2019) International Review of Automatic Control (IREACO), 12 (3), pp. 145-153.
https://doi.org/10.15866/ireaco.v12i3.16455

El Kari, B., Ayad, H., El Kari, A., Mjahed, M., Pozna, C., Design and FPGA Implementation of a New Intelligent Behaviors Fusion for Mobile Robot Using Fuzzy Logic, (2019) International Review of Automatic Control (IREACO), 12 (1), pp. 1-10.
https://doi.org/10.15866/ireaco.v12i1.14802

Khouane, B., Han, C., Zhu, Y., Yadegari, H., Observer-Based Terminal Sliding Mode Control for Attitude Stabilization of Flexible Spacecraft with Fuel Slosh, (2017) International Review of Automatic Control (IREACO), 10 (4), pp. 316-324.
https://doi.org/10.15866/ireaco.v10i4.11533

El Hamidi, K., Mjahed, M., El Kari, A., Ayad, H., Neural and Fuzzy Based Nonlinear Flight Control for an Unmanned Quadcopter, (2018) International Review of Automatic Control (IREACO), 11 (3), pp. 98-106.
https://doi.org/10.15866/ireaco.v11i3.14055

Tumbuan, T., Nurprasetio, I., Indrawanto, I., Abidin, Z., Stable PID Control Strategy to Remove Limit Cycle Due to Stribeck Friction on DC Servo Motor, (2018) International Review of Automatic Control (IREACO), 11 (4), pp. 208-216.
https://doi.org/10.15866/ireaco.v11i4.14883

Siti, I., Mjahed, M., Ayad, H., El Kari, A., New Designing Approaches for Quadcopter PID Controllers Using Reference Model and Genetic Algorithm Techniques, (2017) International Review of Automatic Control (IREACO), 10 (3), pp. 240-248.
https://doi.org/10.15866/ireaco.v10i3.12115

Al-Sinbol, G., Perhinschi, M., Development of an Artificial Immune System for Power Plant Abnormal Condition Detection, Identification, and Evaluation, (2017) International Review of Automatic Control (IREACO), 10 (3), pp. 218-228.
https://doi.org/10.15866/ireaco.v10i3.11739

Hua, C., Guan, C., Neural Network Observer-Based Networked Control for a Class of Nonlinear Systems, Neurocomputing, Vol. 133: 103-110, 2014.
https://doi.org/10.1016/j.neucom.2013.11.026

Kiumarsi, B., Lewis, F., and Levine, D., Optimal Control of Nonlinear Discrete Time-Varying Systems using a New Neural Network Approximation Structure, Neurocomputing, Vol. 156: 157-165, 2015.
https://doi.org/10.1016/j.neucom.2014.12.067

Mu, C., Wang, D., Neural-network-based Adaptive Guaranteed Cost Control of Nonlinear Dynamical Systems with Matched Uncertainties, Neurocomputing, Vol. 245: 46-54, 2017.
https://doi.org/10.1016/j.neucom.2017.03.047

Mu, C., Wang, D., Data-Based Adaptive Neural Network Optimal Output Feedback Control for Nonlinear Systems with Actuator Saturation, Neurocomputing, Vol. 247: 192-201, 2017.
https://doi.org/10.1016/j.neucom.2017.03.053

Sidi, M. J., Spacecraft Dynamics and Control, a Practical Engineering Approach. (Cambridge University Press, 1997).

Bak, T., Spacecraft Attitude Determination- a Magnetometer Approach, PhD Thesis, Aalborg Uiversity, 1999.

Habib, T., A New Optimal Fusion Algorithm for Spacecraft Attitude Determination and Estimation Algorithms, The Egyptian Journal of Remote Sensing and Space Sciences, Vol. 21:305-309, 2018.
https://doi.org/10.1016/j.ejrs.2017.08.007

Hagan, M.., Dcmuth, H., and Beale, M., Neural Network Design. (PWS Publishing Company, 1996).
https://directory.eoportal.org/web/eoportal/satellite-missions/e/egyptsat-1.

Sedelnikov, A., Potienko, K., Analysis of Reduction of Controllability of Spacecraft During Conducting of Active Control Over Microaccelerations, (2017) International Review of Aerospace Engineering (IREASE), 10 (3), pp. 160-166.
https://doi.org/10.15866/irease.v10i3.12342

Kabirov, V., Semenov, V., Shinyakov, Y., A Digital Control System for the Power Conditioning Unit of Spacecraft, (2019) International Review of Aerospace Engineering (IREASE), 12 (1), pp. 26-34.
https://doi.org/10.15866/irease.v12i1.15573


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