A neural-network method for the nonlinear servomechanism problem

Authors:
Yun-Chung Chu;Jie Huang
Affiliations:
Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst.;-
Venue:
IEEE Transactions on Neural Networks
Year:
1999

Citing 0
Cited 4

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Recurrent neural networks for nonlinear output regulation

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Neural network enhanced output regulation in nonlinear systems

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Tracking problems of a spherical inverted pendulum via neural network enhanced design

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Abstract

The solution of the nonlinear servomechanism problem relies on the solvability of a set of mixed nonlinear partial differential and algebraic equations known as the regulator equations. Due to the nonlinear nature, it is difficult to obtain the exact solution of the regulator equations. This paper proposes to solve the regulator equations based on a class of recurrent neural network, which has the features of a cellular neural network. This research not only represents a novel application of the neural networks to numerical mathematics, but also leads to an effective approach to approximately solving the nonlinear servomechanism problem. The resulting design method is illustrated by application to the well-known ball and beam system