具有输出限制的纯反馈系统的神经网络控制

Neural Network Control for Pure-feedback Systems with Output Constraints

为了研究一类具有输出限制的不确定非线性纯反馈系统的自适应神经网络追踪控制问题,利用神经网络的非线性逼近能力与自适应控制的反推法给出该系统的自适应控制器;利用障碍Lyapunov函数与隐函数存在定理进行控制器的设计。结果表明,该控制方法保证了闭环系统所有信号的半全局一致最终有界性。

To investigate the adaptive neural network tracting control problem of a class of uncertain nonlinear pure-feed- back systems with output constraints, an adaptive controller of the systems was provided by using the ability of Neural Network approximation and the adaptive backstepping techniques. The controller was designed by the barrier Lyapunov function and the implicit function theorem. The results show that the developed control scheme guarantees semiglobally uniform ultimate boundedness of all the signals in the closed-loop systems.