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中文摘要：
在这篇论文，一条新模糊神经的适应控制途径为一个类被开发单个输入、单个产量(SISO ) 有无节制的状态的非线性的系统。使用模糊神经网络接近未知非线性的功能，一个模糊神经的适应观察员为状态被介绍评价以及系统鉴定。在走设计的背的框架下面，模糊神经的适应输出反馈控制递归地被构造。建议模糊适应控制途径为所有信号保证全球围住的海角性质，这被证明，驾驶到起源的小 neighbordhood 的追踪的错误。模拟例子被包括说明建议途径的有效性。
英文摘要：
In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.
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Robust adaptive tracking control for nonlinear systems based on bounds of fuzzy approximation parame
Adaptive fuzzy observer backstepping control for a class of uncertain nonlinear systems with unknown
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