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Robust attitude tracking control scheme for a multi-body spacecraft using a radial basis function network and terminal sliding mode

Published online by Cambridge University Press:  04 September 2014

CHANGQING YUAN
Affiliation:
Aviation University of Air Force, Changchun 130022, P. R. China Email: [email protected]; [email protected]; [email protected]
YANHUA ZHONG
Affiliation:
Department Of Electronics and Information Technology, Jiangmen Polytechnic, Jiangmen, 529000, P. R. China Email: [email protected]
JINGRUI ZHANG
Affiliation:
Beijing Institute of Technology, School of Aerospace Science Engineering, Beijing 100081, P. R. China Email: [email protected]
HONGBUO LI
Affiliation:
Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, P. R. China Email: [email protected]
GUOJUN YANG
Affiliation:
Aviation University of Air Force, Changchun 130022, P. R. China Email: [email protected]; [email protected]; [email protected]
YING SHEN
Affiliation:
Aviation University of Air Force, Changchun 130022, P. R. China Email: [email protected]; [email protected]; [email protected]
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Abstract

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We present a novel robust control scheme that deals with multi-body spacecraft attitude tracking problems. The control scheme consists of a radial basis function network (RBFN) and a robust controller. By using the finite time convergence property of the terminal sliding mode (TSM), we derive a new online learning algorithm for updating all the parameters of the RBFN that ensures the RBFN has fast approximation for the parameter uncertainties and external disturbances. We design a robust controller to compensate RBFN approximation errors and realise the anticipative stability and performance properties. We can also achieve closed-loop system stability using Lyapunov stability theory.

No detailed knowledge of the non-linear dynamics of the spacecraft is required at any point in the entire design process, and the proposed robust scheme is simple and effective and can be applied to more complex systems. Simulation results demonstrate the good tracking characteristics of the proposed control scheme in the presence of inertial uncertainties and external disturbances.

Type
Paper
Copyright
Copyright © Cambridge University Press 2014 

References

Akpan, V. A. and Hassapis, G. D. (2011) Nonlinear model identification and adaptive model predictive control using neural networks. ISA Transactions 50 (2)177194.CrossRefGoogle ScholarPubMed
Bang, H., Lee, J.-S. and Eun, Y.-J. (2004) Nonlinear Attitude Control for a Rigid Spacecraft by feedback linearization. KSME International Journal 18 (2)203210.CrossRefGoogle Scholar
Efrati, T. (1997) Tracking Control of Mechanical Systems Using Artificial Neural Networks, Ph.D. Thesis, USC.Google Scholar
Feng, Y., Yu, X. and Man, Z. (2002) Non-singular terminal sliding mode control of rigid manipulators. Automatica 38 (12)21592167.CrossRefGoogle Scholar
Funahashi, K. (1998) On the Approximate Realization of Continuous Mappings by Neural Networks. Neural Networks 2 (3)183192.CrossRefGoogle Scholar
George, A. (2011) Multivariate sigmoidal neural network approximation. Neural Networks 24 (4)378386.Google Scholar
Girosi, F. and Poggio, T. (1990) Networks and the best approximation property. Biological Cybernetics 63 (3)169176CrossRefGoogle Scholar
Hirschorn, R. M. (1979) Invertibility of multivariable nonlinear control systems. IEEE Transactions on Automatic Control 24 (8)855865.CrossRefGoogle Scholar
Hornik, K., Stinchcombe, M and White, H. (1989) Multilayer Feedforword Networks are Universal Approximators. Neural Networks 2 359366.CrossRefGoogle Scholar
Li, Y., Sundararajan, N. and Saratchandran, P. (2001) Neuro-controller design for nonlinear fighter aircraft maneuver using fully tuned RBF networks. Automatica 37 (8)12931301.CrossRefGoogle Scholar
Liang, H., Sun, Z. and Wang, J. (2011) Robust decentralized coordinated attitude control of spacecraft formation. Acta Astronautica 69 (5–6)280288.CrossRefGoogle Scholar
Lin, F. J. and Wai, R. J. (1998) Ultrasonic motor servo drive with on-line trained neural network model-following controller. IEEE Proceedings, Electric Power Applications 145 (2)105110.CrossRefGoogle Scholar
Matthew, R. L. (1999) Spacecraft Attitude Tracking Control, M.Sc. Thesis, The Virginia Polyechnic Institute and State University.Google Scholar
Nardi, F. (2000) Neural network based adaptive algorithms for nonlinear control, Ph.D. Thesis, The School of Aerospace Engineering, Georgia Institute of Technology.Google Scholar
Nayeri, M. R. D., Alasty, A. and Daneshjou, K. (2004) Neural optimal control of flexible spacecraft slew maneuver. Acta Astronautica 55 (10)817827.CrossRefGoogle Scholar
Park, J. and Sandberg, I. W. (1991) Universal approximation using radial basis function networks. Neural Computation 3 (2)246257.CrossRefGoogle ScholarPubMed
Passino, K. M. (2004) Biomimicry for Optimization, Control, and Automation, Springer-Verlag.Google Scholar
Pazelli, T. F. P. A. T., Terra, M. H. and Siqueira, A. A. G. (2011) Experimental investigation on adaptive robust controller designs applied to a free-floating space manipulator. Control Engineering Practice 19 (4)395408.CrossRefGoogle Scholar
Poggio, T. and Girosi, F. (1990) Networks for approximation and learning. Proceedings of the IEEE 78 (9)14811497.CrossRefGoogle Scholar
Sheen, J. J. and Bishop, R. H. (1994a) Spacecraft Nonlinear Control. Journal of the Astronautical Sciences 42 (3)361377.Google Scholar
Sheen, J. J. and Bishop, R. H. (1994b) Adaptive Nonlinear Control of Spacecraft. Journal of the Astronautical Sciences 42 (4)451472.Google Scholar
Sibai, F. N., Hosani, H. I., Naqbi, R. M., Dhanhani, S. and Shehhi, S. (2011) Iris recognition using artificial neural networks. Expert Systems with Applications 38 (5)59405946.CrossRefGoogle Scholar
Slotine, J. S. and Li, W. (1991) Applied Nonlinear Control, Prentice Hall.Google Scholar
Stonier, D. J. and Stonier, R. J. (2004) Obstacle avoidance and finite-time tracking of mobile targets. In: 2nd international conference on autonomous robots and agents, Palmerston North, New Zealand 58–63.Google Scholar
Vadali, S. R. (1986) Variable-structure control of spacecraft large-angle maneuvers. Journal of Guidance, Control, and Dynamics 9 (2)235239.CrossRefGoogle Scholar
Wu, C. S. and Chen, B. S. (1999) Unified Design for H2, H, and Mixed control of spacecraft. Journal of Guidance, Control, and Dynamics 22 (6)854896.CrossRefGoogle Scholar
Yang, C. D. and Sun, Y. P. (2002) Mixed H2/H state-feedback design for microsatellite attitude control. Control Engineering Practice 10 (9)951970.CrossRefGoogle Scholar
Yu, X. and Man, Z. (2002) Variable structure systems with terminal sliding modes. Springer-Verlag Lecture Notes in Control and Information Sciences 274 109128.CrossRefGoogle Scholar
Yu, X., Wu, Y. and Man, Z. (1999) On global stabilization of nonlinear dynamical systems. Springer-Verlag Lecture Notes in Control and Information Sciences 247 109122.CrossRefGoogle Scholar
Yu, S., Yu, X. and Zhihong, M. (2000) Robust global terminal sliding mode control of SISO nonlinear uncertain systems. Proceedings of the 39th IEEE conference on decision and control.Google Scholar
Zhang, S. J. and Cao, X. C. (2004) Coordinated attitude control for a tracking and data relay satellite with mobile antennas. Aircraft Engineering and Aerospace Technology 76 (4)414419.CrossRefGoogle Scholar