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The use of neural networks in control systems can be seen as a natural step in the evolution of control methodology to meet new challenges. Many attempts have been made to apply the neural networks to deal with non-linearities and uncertainties of the control systems. Research in neural network applications to control can be classified according to the major methods depending on structures of the control system, such as NN-based NON-linear System Identification, NN-based Supervised Control, NN-based Direct Control, NN-based Indirect Control, NN-based Adaptive Control, NN-based Self-learning Control, NN-based Fuzzy Control, and NN Variable Structure Control. All these control methods cannot, however, effectively guarantee system stability, i.e. none of these neural network controls, except for NN-based Variable Structure Control, is based on system stability. This also limits the application and development of the neural networks in control theory. The paper shows the effort to solve this difficulty and give a way for the design method of the stability based neural networks controller using Lyapunov second stability theorem. This kind of controller can not only guarantee system stability, but also fully compensate for the influence of system uncertainties and non-linearities.Simulation results also show the effectiveness of the controller.
Czasopismo
Rocznik
Tom
Strony
119--133
Opis fizyczny
Bibliogr. 54 poz.,Rys., wykr.,
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Bibliografia
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bwmeta1.element.baztech-article-BAT2-0001-1641
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