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- W2094962454 abstract "This paper presents a new multi-step predictive controller based on neural networks and researches the adaptability of the predictive controller for a pneumatic position servo system which has some typical characteristics of non-linearity and time-varying. A diagonal recurrent neural network (DRNN) is used to predict the system output of the multi-step ahead directly. According to the intrinsic defects of a back-propagation (BP) algorithm that cannot update network weights incrementally, a new hybrid learning algorithm combining the temporal differences (TD) method with the BP algorithm to train the DRNN is put forward. A three-layer feedforward BP neural network is used as a non-linear rolling optimal controller to realize the optimization of control input of the next step according to a single-value predictive control algorithm to simplify computation. Simulation and experimental results indicate that the proposed predictive controller is suitable for real-time control of a pneumatic position servo system because of its characteristics of a simple algorithm, fast calculation of the control input and good tracking effects." @default.
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- W2094962454 date "2006-03-01" @default.
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- W2094962454 title "Multi-step predictive control with TDBP method for pneumatic position servo system" @default.
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- W2094962454 doi "https://doi.org/10.1191/0142331206tm162oa" @default.
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