Artificial-neural-network-based nonlinear adaptive control and simulation

Wei Dong; Ma Ruiping; Zhang Minglian; Shi Xiaorong

Volume 30 Issue 03

Mar. 2004

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Journal of Beijing University of Aeronautics and Astronautics > 2004 > 30(03): 217-221.

Zhang Ketao, Fang Yuefa, Fang Haironget al. Design and analysis of a rover mechanism based on the metamorphic principle[J]. Journal of Beijing University of Aeronautics and Astronautics, 2007, 33(07): 838-841. (in Chinese)

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Wei Dong, Ma Ruiping, Zhang Minglian, et al. Artificial-neural-network-based nonlinear adaptive control and simulation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2004, 30(03): 217-221. (in Chinese)

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Artificial-neural-network-based nonlinear adaptive control and simulation

School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

Received Date: 04 Nov 2002
Publish Date: 31 Mar 2004

Abstract

Abstract

The increasing complexity of the nonlinear systems highlights the need for advanced modeling and control techniques for effective control. An artificial-neural-network-based nonlinear adaptive control system was studied, which identified the dynamics of the plant with improved NARMA(nonlinear auto-regressive moving average) model at first. Then, the control algorithm was deduced with respect to the model directly. An adaptive system was designed to control a longitudinal missile autopilot which achieves tracking of external reference commands in angle of attack.Simulation results illustrate the ability of this technique to compensate for unmodeled dynamics and uncertain plant nonlinearities.
- neural networks,
- nonlinear,
- system identification,
- adaptive control

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References

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Artificial-neural-network-based nonlinear adaptive control and simulation