Modeling of the Space Shuttle Main Engine Using Feed-forward Neural Networks

This paper presents the modeling of the Space Shuttle Main Engine (SSME) using a feed-forward neural network. The input and output data for modeling are obtained from a non-linear performance simulation developed by Rockwell International. The SSME is modeled as a system with two inputs and four outputs. The back-propagation algorithm is used to train the neural network by minimizing the squares of the residuals. The inputs to the network are the delayed values of the selected inputs and outputs of the non-linear simulation. The results obtained from the neural network model are compared with the results obtained from the non-linear simulation. It is shown that a single neural network can be used to model the dynamics of the space shuttle main engine. This neural network model can be used for control design purposes as well as for model-based fault detection studies.

[1]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[2]  I. J. Leontaritis,et al.  Model selection and validation methods for non-linear systems , 1987 .

[3]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[5]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[6]  V. J. Wheelock,et al.  The reusable Space Shuttle Main Engine prepares for long life , 1982 .

[7]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .

[8]  T.-H. Guo,et al.  Space shuttle main engine model identification , 1990, IEEE Control Systems Magazine.

[9]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[10]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[11]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[12]  Ten-Huei Guo,et al.  A Simplified Dynamic Model of Space Shuttle Main Engine , 1991, 1991 American Control Conference.

[13]  Ten-Huei Guo,et al.  State Space Representation of the Open Loop Dynamics of the Space Shuttle Main Engine , 1991, 1991 American Control Conference.

[14]  J. Farison,et al.  On the Volterra-series functional identification of non-linear discrete-time systems , 1973 .

[15]  PAUL J. WERBOS,et al.  Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.

[16]  K. Narendra,et al.  An iterative method for the identification of nonlinear systems using a Hammerstein model , 1966 .

[17]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[19]  W. D. T. Davies,et al.  System identification for self-adaptive control , 1970 .

[20]  Eduardo D. Sontag,et al.  Feedback Stabilization Using Two-Hidden-Layer Nets , 1991, 1991 American Control Conference.

[21]  Sheng Chen,et al.  Orthogonal least squares methods and their application to non-linear system identification , 1989 .

[22]  Sheng Chen,et al.  Identification of MIMO non-linear systems using a forward-regression orthogonal estimator , 1989 .