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Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

机译:基于模糊预测的长时滞系统模糊神经网络滑模控制

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摘要

Delay time, which may degrade the control performance, is frequently encountered in various control processes. The fuzzy neural network sliding mode controller (FNNSMC), which incorporates the fuzzy neural network (FNN) with the sliding mode controller (SMC), is developed to control the long delay system with unknown model based on fuzzy prediction algorithm in the paper. According to the characteristics of the long delay systems, we simulate the manual operating process and predict the delayed error and its derivative based on the information of the input and output variables of the process, and then feedback these prediction values to the FNN and train the FNN with the regulation function by the idea of sliding mode control until the better control results are obtained. The FNNSMC has more robustness due to the abilities of the learning and reasoning and can eliminate the drawbacks of the general SMC, namely the chattering in the control signal and the needing knowledge of the bounds of the disturbances and uncertainties. Simulation examples demonstrate the advantages of the proposed control scheme.
机译:在各种控制过程中经常会遇到延迟时间,这可能会降低控制性能。本文基于模糊预测算法,开发了将模糊神经网络(FNN)与滑模控制器(SMC)相结合的模糊神经网络滑模控制器(FNNSMC),用于控制未知模型的长时滞系统。根据长时滞系统的特点,我们模拟了手动操作过程,并根据过程的输入和输出变量的信息预测了延迟误差及其导数,然后将这些预测值反馈给FNN并训练它们。 FNN具有调节功能,通过滑模控制的思想直到获得更好的控制效果为止。 FNNSMC由于学习和推理的能力而具有更强的鲁棒性,并且可以消除一般SMC的缺点,即控制信号中的抖动以及对干扰和不确定性范围的了解。仿真实例证明了所提出的控制方案的优点。

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