Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties
Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties
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DOI：
10.1016/j.engappai.2013.08.008
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
F. Rossomando;C. Soria;R. Carelli
中科院分区：
文献类型：
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作者：
F. Rossomando;C. Soria;R. Carelli
研究了一类多输入多输出非线性系统的自适应神经滑模控制问题。该控制策略采用逆非线性控制器和自适应神经网络滑模控制相结合的在线学习算法。当系统的整体结构(运动学和动力学)发生变化时，采用滑模控制的自适应神经网络作为常规逆控制器的补偿器，以改善控制性能。利用李亚普诺夫稳定性理论设计了控制器。实例研究的实验结果表明，该方法对具有意外大不确定性的动态系统控制是有效的。
This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.