Backstepping Design of Adaptive Neural Fault-Tolerant Control for MIMO Nonlinear Systems
Backstepping Design of Adaptive Neural Fault-Tolerant Control for MIMO Nonlinear Systems
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DOI：
10.1109/tnnls.2016.2599009
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发表时间：
2017-11
影响因子：
10.4
通讯作者：
Hui Gao;Yongduan Song;C. Wen
中科院分区：
文献类型：
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作者：
Hui Gao;Yongduan Song;C. Wen
针对一类多输入多输出非线性系统，提出了一种基于神经网络的自适应控制器。结果表明，对于任意外部输入，所设计的自适应神经网络控制器的闭环系统的所有信号在L [0，\infty]}中是全局一致有界的.在我们的控制设计中，神经网络的建模误差的上界和外部干扰的增益是未知的上界，这是更合理的建立自适应神经网络控制的稳定性。基于滤波器的修正项被用于未知参数的更新规律中，以改善瞬态性能。最后，容错控制的发展，以适应执行器故障。一个说明性的例子应用自适应控制器控制刚性机器人手臂显示所提出的控制器的验证。
In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in $L_{[0,\infty ]}$ . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.