IEEE Trans Neural Netw Learn Syst. 2015 Aug;26(8):1789-802. doi: 10.1109/TNNLS.2015.2420661. Epub 2015 Apr 24.
This paper addresses the problem of adaptive neural output-feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states. A modified Bouc-Wen model is first employed to capture the output hysteresis phenomenon in the design procedure. For its fusion with the neural networks and the Nussbaum-type function, two key lemmas are established using some extended properties of this model. To avoid the bad system performance caused by the output nonlinearity, a barrier Lyapunov function technique is introduced to guarantee the prescribed constraint of the tracking error. In addition, a robust filtering method is designed to cancel the restriction that all the system states require to be measured. Based on the Lyapunov synthesis, a new neural adaptive controller is constructed to guarantee the prescribed convergence of the tracking error and the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system. Simulations are implemented to evaluate the performance of the proposed neural control algorithm in this paper.
本文针对一类具有迟滞输出机制和不可测量状态的特殊非线性系统的自适应神经网络输出反馈控制问题展开研究。在设计过程中，首先采用修正的 Bouc-Wen 模型来捕捉输出迟滞现象。为了将其与神经网络和 Nussbaum 型函数融合，利用该模型的一些扩展性质建立了两个关键引理。为了避免输出非线性导致的系统性能变差，引入了障碍李雅普诺夫函数技术来保证跟踪误差的规定约束。此外，设计了一种鲁棒滤波方法来消除对所有系统状态都需要测量的限制。基于李雅普诺夫综合，构建了一种新的神经网络自适应控制器，以保证跟踪误差的规定收敛和闭环系统中所有信号的半全局一致有界性。通过仿真评估了本文所提神经网络控制算法的性能。
