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Intelligent dynamic sliding-mode neural control using recurrent perturbation fuzzy neural networks

机译：基于递归扰动模糊神经网络的智能动态滑模神经控制

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In this paper, a recurrent perturbation fuzzy neural network (RPFNN) is used to online approximate an unknown nonlinear term in the system dynamics. A sine-cosine perturbed membership function is used to handle rule uncertainties when it is hard to exactly determine the grade of the value of fuzzy sets. Unlike type-2 fuzzy sets use an extra type reduction operation to find the output, the proposed RPFNN does not require heavy computational loading. Meanwhile, this paper proposes an intelligent dynamic sliding-mode neural control (IDSNC) system which is composed of a neural controller and an exponential compensator. The neural controller is designed as the main controller via dynamic sliding-mode approach, and the exponential compensator is designed to obtain a faster reaching time and a good robustness. The parameter adaptation laws of the IDSNC system are derived based on the Lyapunov function, so that the system stability can be guaranteed. Finally, the IDSNC system is applied to an inverted pendulum problem and a chaotic synchronization problem. The simulation results demonstrate that the IDSNC system can achieve favorable control performance and is robust against parameter variations and external disturbances. (C) 2015 Elsevier B.V. All rights reserved.
机译：本文使用递归摄动模糊神经网络（RPFNN）在线近似估计系统动力学中的未知非线性项。当难以准确确定模糊集的值的等级时，使用正弦余弦摄动隶属函数来处理规则不确定性。与类型2模糊集使用额外的类型归约运算来查找输出不同，所提出的RPFNN不需要繁重的计算负荷。同时，提出了一种由神经控制器和指数补偿器组成的智能动态滑模神经控制（IDSNC）系统。通过动态滑模方法将神经控制器设计为主控制器，并设计指数补偿器以获得更快的到达时间和良好的鲁棒性。基于李雅普诺夫函数推导了IDSNC系统的参数自适应律，从而保证了系统的稳定性。最后，将IDSNC系统应用于倒立摆问题和混沌同步问题。仿真结果表明，IDSNC系统可以实现良好的控制性能，并且对参数变化和外部干扰具有鲁棒性。 （C）2015 Elsevier B.V.保留所有权利。

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