Simple Recurrent Neural Network‐Based Adaptive Predictive Control for Nonlinear Systems

doi:10.1111/j.1934-6093.2002.tb00350.x

Simple Recurrent Neural Network‐Based Adaptive Predictive Control for Nonlinear Systems

2008-10-22

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DOI：10.1111/j.1934-6093.2002.tb00350.x

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ABSTRACTMaking use of the neural network universal approximation ability, a nonlinear predictive control scheme is studied in this paper. On the basis of a uniform structure of simple recurrent neural networks, a one‐step neural predictive controller (OSNPC) is designed. The whole closed‐loop system's asymptotic stability and passivity are discussed, and stable conditions for the learning rate are determined based on the Lyapunov stability theory for the whole neural system. The effectiveness of OSNPC is verified via exhaustive simulations.

Simple Recurrent Neural Network‐Based Adaptive Predictive Control for Nonlinear Systems

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