Predictor-Based Neural Dynamic Surface Control for Uncertain Nonlinear Systems in Strict-Feedback Form
Predictor-Based Neural Dynamic Surface Control for Uncertain Nonlinear Systems in Strict-Feedback Form
复制标题
严格反馈形式的不确定非线性系统的基于预测器的神经动态表面控制
DOI：
10.1109/tnnls.2016.2577342
复制
发表时间：
2017-09-01
影响因子：
10.4
通讯作者：
Wang, Jun
中科院分区：
文献类型：
--
作者：
Peng, Zhouhua;Wang, Dan;Wang, Jun
针对一类严格反馈形式的不确定非线性系统，提出了一种基于预测器的神经动态面控制（PNDSC）设计方法。与现有的NDSC方法相比，跟踪误差通常用于更新神经网络的权重，提出了一个预测器的每个子系统，预测误差被用来更新神经适应律。所提出的方案，使顺利和快速识别系统动态，而不会引起高频振荡，这是不可避免的使用经典的NDSC方法。进一步将结果推广到具有观测器反馈的PNDSC，并分析了其对量测噪声的鲁棒性。数值和实验结果证明了所提出的PNDSC架构的有效性。
This paper presents a predictor-based neural dynamic surface control (PNDSC) design method for a class of uncertain nonlinear systems in a strict-feedback form. In contrast to existing NDSC approaches where the tracking errors are commonly used to update neural network weights, a predictor is proposed for every subsystem, and the prediction errors are employed to update the neural adaptation laws. The proposed scheme enables smooth and fast identification of system dynamics without incurring high-frequency oscillations, which are unavoidable using classical NDSC methods. Furthermore, the result is extended to the PNDSC with observer feedback, and its robustness against measurement noise is analyzed. Numerical and experimental results are given to demonstrate the efficacy of the proposed PNDSC architecture.