Adaptive Neural Control for a Class of Uncertain Nonlinear Systems in Pure-Feedback Form With Hysteresis Input
Adaptive Neural Control for a Class of Uncertain Nonlinear Systems in Pure-Feedback Form With Hysteresis Input
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DOI：
10.1109/tsmcb.2008.2006368
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发表时间：
2009-04
期刊：
影响因子：
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通讯作者：
B. Ren;S. Ge;C. Su;Tong-heng Lee
中科院分区：
文献类型：
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作者：
B. Ren;S. Ge;C. Su;Tong-heng Lee
本文研究了一类具有广义Prandtl-Ishlinskii滞后输入的未知纯反馈非线性系统的自适应神经控制问题。为了处理面对迟滞非光滑特性的非仿射问题，首先将中值定理应用于纯反馈对象中的函数，然后应用于迟滞输入函数。利用神经网络的函数逼近能力对未知不确定性进行补偿。用Nussbaum函数处理未知的虚拟控制方向。利用Lyapunov综合，证明了闭环控制系统是半全局一致最终有界的，跟踪误差收敛到零的小邻域。仿真结果说明了所提方法的性能。
In this paper, adaptive neural control is investigated for a class of unknown nonlinear systems in pure-feedback form with the generalized Prandtl-Ishlinskii hysteresis input. To deal with the nonaffine problem in face of the nonsmooth characteristics of hysteresis, the mean-value theorem is applied successively, first to the functions in the pure-feedback plant, and then to the hysteresis input function. Unknown uncertainties are compensated for using the function approximation capability of neural networks. The unknown virtual control directions are dealt with by Nussbaum functions. By utilizing Lyapunov synthesis, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach.