Adaptive Hinfinity tracking control for a class of uncertain nonlinear systems using radial-basis-function neural networks
Adaptive Hinfinity tracking control for a class of uncertain nonlinear systems using radial-basis-function neural networks
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DOI：
10.1016/j.neucom.2006.10.020
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发表时间：
2007
期刊：
影响因子：
6
通讯作者：
Yansheng Yang;Xiao-Feng Wang
中科院分区：
文献类型：
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作者：
Yansheng Yang;Xiao-Feng Wang
针对一类具有不确定系统和增益函数的非线性系统，提出了一种新的自适应H∞跟踪控制器设计方法，该系统是非结构的（或不可重复的）和状态依赖的未知非线性函数。采用连续函数分离技术和径向基函数（RBF）神经网络逼近不确定系统函数。针对具有H∞性能的自适应神经网络跟踪控制器的综合问题，提出了一种系统的设计方法。所得到的闭环系统被证明是半全局一致最终有界的，并且外部干扰对跟踪误差的影响可以被衰减到任何指定的水平。此外，在一些现有的自适应控制方案遇到的反馈线性化技术可能出现的控制器奇异性问题，可以消除和自适应机制，只有一个学习参数化可以实现。通过合理选择设计参数，保证了闭环系统的控制性能。仿真结果表明了该控制方案的有效性。
In this paper, we propose a novel adaptive H∞tracking controller design for a class of nonlinear systems with uncertain system and gain function, which are unstructured (or non-repeatable) and state-dependent unknown nonlinear functions. Both the separation technique of continuous function and radial-basis-function (RBF) neural network are incorporated to approximate the uncertain system function. Systematic design procedure is developed for the synthesis of adaptive neural network tracking control with H∞performance. The resulting closed-loop system is proven to be semi-globally uniformly ultimately bounded and the effect of the external disturbances on the tracking error can be attenuated to any prescribed level. In addition, the possible controller singularity problem in some of the existing adaptive control schemes met with feedback linearization techniques can be removed and the adaptive mechanism with only one learning parameterizations can be achieved. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. Finally, simulation results show the effectiveness of the control scheme.