摘要
针对具有输入饱和和输出受限的纯反馈非线性系统,设计了神经网络自适应控制器。首先利用隐函数定理和中值定理将非仿射形式的纯反馈非线性系统转换成有显式输入的非线性系统,基于李雅普诺夫第二方法以及反推法并采用障碍型李雅普诺夫函数进行控制器的设计,最后通过稳定性分析证明了闭环控制系统是半全局一致最终有界的,利用仿真例子验证了控制方案的有效性。
A neural network adaptive controller is designed for pure feedback nonlinear systems with input saturation and output constraints. Firstly, using the implicit function theorem and the mean value theorem, the pure feedback nonlinear system in non-affine form is transformed into a nonlinear system with explicit input. Based on Lyapunov’s second method and backstepping method, the barrier Lyapunov function is used to design the controller. Finally, the stability analysis proves that the closed-loop control system is bounded by the semi-global consistent termination. The effectiveness of the control scheme is verified by the simulation example.
作者
张春蕾
王立东
高闯
陈雪波
ZHANG Chun-lei;WANG Li-dong;GAO Chuang;CHEN Xue-bo(School of Elecronic and Information Engineering,University of Science and Technology Liaoning,Anshan 114051,Liaoning)
出处
《控制工程》
CSCD
北大核心
2021年第3期531-539,共9页
Control Engineering of China
基金
国家自然科学基金项目(71571091,71771112)。
关键词
纯反馈系统
输入饱和
输出受限
神经网络
Pure feedback system
input saturation
output constraints
neural network
