Mahmoud Magdi S, Maaruf Muhammad, El-Ferik Sami
Systems Engineering Department, KFUPM, P.O. Box 5067, Dhahran 31261, Saudi Arabia.
ISA Trans. 2021 Jun;112:1-11. doi: 10.1016/j.isatra.2020.11.026. Epub 2020 Dec 1.
This paper proposes an adaptive neural network based output feedback backstepping fast terminal sliding mode control (NN-BFTSMC) for continuous polymerization reactor with external disturbances and parametric uncertainties. Firstly, neural networks (NN) are employed to approximate the uncertain nonlinear functions. Next, the average molecular weight and the reactor temperature tracking controllers are designed based on the finite-time NN-BFTSMC. In addition, the NN-BFSMC effectively estimates and compensates the upper bounds of the external disturbances. Moreover, the chattering effect is eliminated without losing the robustness accuracy. The finite-time stability of the closed-loop system is proved by Lyapunov theory. At last, numerical simulations and comparative studies are introduced to illustrate the superior performance of the proposed method.
本文针对具有外部干扰和参数不确定性的连续聚合反应器，提出了一种基于自适应神经网络的输出反馈反步快速终端滑模控制（NN-BFTSMC）方法。首先，利用神经网络（NN）逼近不确定非线性函数。其次，基于有限时间NN-BFTSMC设计了平均分子量和反应器温度跟踪控制器。此外，NN-BFSMC有效估计并补偿了外部干扰的上界。而且，消除了抖振效应，同时不损失鲁棒精度。利用李雅普诺夫理论证明了闭环系统的有限时间稳定性。最后，通过数值仿真和对比研究说明了所提方法的优越性能。
