反推
控制理论(社会学)
非线性系统
Padé逼近
有界函数
自适应控制
李雅普诺夫函数
计算机科学
国家(计算机科学)
功能(生物学)
人工神经网络
数学
控制(管理)
应用数学
算法
量子力学
进化生物学
生物
机器学习
物理
数学分析
人工智能
作者
Nan Wang,Zhumu Fu,Fazhan Tao,Shuzhong Song,Tong Wang
摘要
Summary This article investigated the adaptive backstepping tracking control for a class of pure‐feedback systems with input delay and full‐state constraints. With the help of mean value theorem, the system is transformed into strict‐feedback one. By introducing the Pade approximation method, the effect of input delay was compensated. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. Furthermore, in order to reduce the computational burden by introducing backstepping design technique, dynamic surface control technique was employed. In addition, the number of the adaptive parameters that should be updated online was also reduced. By utilizing the barrier Lyapunov function, the closed‐loop nonlinear system is guaranteed to be semi‐globally ultimately uniformly bounded. Finally, a numerical simulation example is given to show the effectiveness of the proposed control strategy.
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