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• W2056772771 abstract "In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance-oriented control laws for a class of single-input–single-output (SISO) nth-order non- linear systems. Both repeatable (or state dependent) unknown non-linearities and non-repeatable unknown non-linearities such as external disturbances are considered. In addition, unknown non-linearities can exist in the control input channel as well. All unknown but repeatable non-linear functions are approximated by outputs of multi-layer neural networks to achieve a better model compensation for an improved performance. All NN weights are tuned on-line with no prior training needed. In order to avoid the possible divergence of the on-line tuning of neural network, discontinuous projection method with fictitious bounds is used in the NN weight adjusting laws to make sure that all NN weights are tuned within a prescribed range. By doing so, even in the presence of approximation error and non-repeatable non-linearities such as disturbances, a controlled learning is achieved and the possible destabilizing effect of on-line tuning of NN weights is avoided. Certain robust control terms are constructed to attenuate various model uncertainties effectively for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy in general. In addition, if the unknown repeatable model uncertainties are in the functional range of the neural networks and the ideal weights fall within the prescribed range, asymptotic output tracking is also achieved to retain the perfect learning capability of neural networks in the ideal situation. The proposed neural network adaptive control (NNARC) strategy is then applied to the precision motion control of a linear motor drive system to help to realize the high-performance potential of such a drive technology. NN is employed to compensate for the effects of the lumped unknown non-linearities due to the position dependent friction and electro-magnetic ripple forces. Comparative experiments verify the high-performance nature of the proposed NNARC. With an encoder resolution of 1 µm, for a low-speed back-and-forth movement, the position tracking error is kept within ±2 µm during the most execution time while the maximum tracking error during the entire run is kept within ±5.6 µm. Copyright © 2001 John Wiley & Sons, Ltd." @default.
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• W2056772771 date "2001-01-01" @default.
• W2056772771 modified "2023-09-22" @default.
• W2056772771 title "Neural network adaptive robust control with application to precision motion control of linear motors" @default.
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• W2056772771 doi "https://doi.org/10.1002/acs.658" @default.
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