High Order Sliding Mode Neurocontrol for Uncertain Nonlinear SISO Systems: Theory and Applications
High Order Sliding Mode Neurocontrol for Uncertain Nonlinear SISO Systems: Theory and Applications
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不确定非线性 SISO 系统的高阶滑模神经控制：理论与应用
DOI：
10.1007/978-3-540-79016-7_9
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
T. Poznyak
中科院分区：
文献类型：
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作者：
I. Chairez;A. Poznyak;T. Poznyak
动态系统中的不确定性在实际应用中很常见，在任何控制实现中都会引起很大的麻烦，并且是跟踪或调节问题的不稳定或性能不佳的根源。在过去的三十年里，人们在不确定非线性动态系统的控制设计方面进行了大量的研究工作。在这种情况下，有多种方法可以设计和构建控件。其中，更有效的是人工神经网络（ANN）和滑模（SM）技术以及所有可能的变体（积分滑模、高阶滑模等）。这种组合似乎非常有前途[21]、[28]，因为它提供了一种新的工具，用于识别、状态估计和控制受外部扰动影响的许多类不确定系统。本章讨论了这一想法的实现，并提出了一种基于微分神经网络观测和高阶滑模技术的自适应控制设计。下面这种方法被称为高阶滑模神经控制（HOSMNC）。
Uncertainties in dynamic systems are common in real applications, provoking substantial troubles in any control realization and being a source of instability or poor performance for tracking or regulation problems. Considerable research efforts had been undertaken on control designing for uncertain nonlinear dynamic systems over the last thirty years. There are several approaches to design and construct a control in this situation. Among them, the more effective are the Artificial Neural Networks (ANN) and the Sliding Mode (SM) technique with all possible variants within (Integral Sliding Mode, Higher Order Sliding Mode, etc.). Such combination seems to be very promising [21], [28] because it provides a new instrument for identification, state estimation and control of many classes of uncertain systems affected by external perturbations. This chapter deals with the realization of this idea and suggests an adaptive control designing based on both Differential Neural Network Observation and High Order Sliding Mode Technique. Below this approach is referred to as High Order Sliding Mode Neural Control (HOSMNC).