Abstract
Recently, there have been many attempts to use neural networks as a feedback controller. However, most of the reported cases seek to control Single-Input Single-Output (SISO) systems using some sort of adaptive strategy. In this paper, we demonstrate that neural networks can be used for the control of complex multivariable, rather than simply SISO, systems. A modified direct control scheme using a neural network architecture is used with backpropagation as the adaptive algorithm. The proposed algorithm is designed for Multi-Input Multi-Output (MIMO) systems, and is similar to that proposed by Saerens and Soquet [1] and Goldenthal and Farrell [2] for (SISO) systems, and differs only in the form of the gradient approximation. As an example of the application of this approach, we investigate the control of the dynamics of a submarine vehicle with four inputs and four outputs, in which the differential stern, bow and rudder control surfaces are dynamically coordinated to cause the submarine to follow commanded changes in roll, yaw rate, depth rate and pitch attitude. Results obtained using this scheme are compared with those obtained using optimal linear quadratic control.
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References
Saerens M, Soquet A. Neural controller based on back-propagation algorithm. IEE Proc F 1991; 138(1): 55–62
Goldenthal W, Farrell J. Application of neural network to automatic control. Proc AIAA Conference on Guidance Navigation and Control 1990; 1108–1112
Hunt KJ, Sbarbaro D, Zbikowski R, Gawthrop PJ. Neural networks for control systems —a survey. Automatica 1992; 28(6): 1083–1112
David AW, Donald AS. Handbook of Intelligent Control. Van Nostrand Reinhold, New York, 1992
Liguni Y, Sakai H, Tokumaru H. A nonlinear regulator design in the presence of system uncertainties using multilayered neural network. IEEE Trans Neural Networks 1991; 2(4): 410–417
Swiniarski RW. Novel network control of a space manipulator. Proc American Control Conference 1991; 3023–3024
Psaltis D, Sideris A, Yamamura A. A multilayered neural network controller. IEEE Control Syst Mag 1988; 17–21
Narendra KS, Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Trans Neural Networks 1990; 1(1): 4–27
Nguyen D, Widrow B. Neural networks for self-learning control systems. Int J Control 1991; 54(6): 1439–1451
Wu QH, Hogg BW, Irwin GW. A neural network regulator for turbogenerators. IEEE Trans Neural Networks 1992; 3(1): 95–100
Venugopal KP, Sudhakar R, Pandya AS. On-line learning control of autonomous underwater vehicles using feedforward neural networks. IEEE J Oceanic Eng 1992; 10(4): 308–319
Rumelhart D, Hinton G, Williams R. Learning internal representation by error propagation. Distributed Parallel Processing. MIT Press, Cambridge, MA, 1986
Minsky M, Papert S. Perceptrons. MIT Press, Cambridge, MA, 1969
Foulard C, Gentil S, Sandraz JP. Commande et Regulation par Calculateur Numerique. Eyrolles, Paris, 1984
Martin RJ. Multivariable Control System Design for a Submarine Using Active Roll Control. Engineering Thesis, MIT, 1985
Mette JA. Multivariable Control of a Submarine Using the LQG/LTR Method. Engineering Thesis, MIT, 1985
Werbos PJ. Neurocontrol and supervised learning: an overview and evaluation. Handbook of Intelligent Control. Van Nostrand Reinhold, New York, 1992
Maxwell N. Modem Control Engineering, Pergamon Press, New York, 1972
Mort N, Derradji DA, Tiano A, Ranzi A. Application of neural networks to marine vehicle control. Tenth Ship Control Systems Symposium, Ottawa, October 1993
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Derradji, D.A., Mort, N. Neural network controller for a multivariable model of submarine dynamics. Neural Comput & Applic 7, 295–308 (1998). https://doi.org/10.1007/BF01428121
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DOI: https://doi.org/10.1007/BF01428121