Adaptive neural control for a class of pure-feedback nonlinear time-delay systems with asymmetric saturation actuators
Adaptive neural control for a class of pure-feedback nonlinear time-delay systems with asymmetric saturation actuators
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DOI：
10.1016/j.neucom.2015.09.020
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发表时间：
2016-01
期刊：
影响因子：
6
通讯作者：
Zhaoxu Yu;Shugang Li;Zhaosheng Yu
中科院分区：
文献类型：
--
作者：
Zhaoxu Yu;Shugang Li;Zhaosheng Yu
研究了一类具有未知非对称饱和执行器的不确定纯反馈非线性时滞系统的自适应跟踪控制问题。所考虑的问题是具有挑战性的，由于存在未知的分布时变时滞和非对称饱和执行器。特别地，分别利用积分中值定理和基于高斯误差函数的连续可微模型处理了分布时变时滞和未知非对称饱和非线性的困难.然后，基于中值定理、Razumikhin泛函方法、变量分离技术和神经网络（NN）参数化的新组合，通过动态面控制（DSC）技术，提出了一种仅需更新一个参数的自适应神经网络控制器.此外，DSC技术可以克服传统的反推设计中的“爆炸的复杂性”的问题。闭环系统中的所有信号保持半全局一致最终有界（SGUUB），跟踪误差收敛到原点的一个小邻域。最后，通过仿真验证了该设计的有效性.
This paper addresses the problem of adaptive tracking control for a class of uncertain pure-feedback nonlinear time-delay systems with unknown asymmetric saturation actuators. The considered problem is challenging due to the existence of unknown distributed time-varying delays and asymmetric saturation actuator. In particular, the difficulties from distributed time-varying delays and unknown asymmetric saturation nonlinearity are processed by using the mean value theorem for integrals and a Gaussian error function-based continuous differentiable model, respectively. Then, based on a novel combination of mean value theorem, Razumikhin functional method, variable separation technique and Neural Network (NN) parameterization, an adaptive neural controller which involves only one parameter to be updated is presented for such systems via Dynamic Surface Control (DSC) technique. Moreover, the DSC technique can overcome the problem of ‘explosion of complexity’ in the traditional backstepping design. All signals in the closed-loop system remain semi-globally uniformly ultimately bounded (SGUUB), and the tacking error converges to a small neighborhood of the origin. Finally, simulation results are given to verify the effectiveness of the proposed design.