高阶非线性时滞系统的神经网络动态面控制

针对一类具有未知时变时滞的不确定高阶非线性系统，基于增加幂次积分方法，提出了一种非光滑状态反馈自适应神经网络动态面控制设计方案。通过构造适当的Lyapunov Krasovskii泛函处理了未知时变时滞不确定项；通过利用神经网络权值范数的适当形式幂次函数，将神经网络用于对在单步递推中所构造的未知函数进行建模；采用动态面技术，解决了“微分爆炸”问题。所提控制方案能够保证闭环控制系统的状态量和跟踪误差半全局一致终结有界。最后，仿真算例结果表明了该方案的有效性。

Neural network dynamic surface control for high order nonlinear systems with time delay

Based on the adding a power integrator method, a non-smooth state feedback adaptive neural network dynamic surface control scheme is proposed for a class of uncertain high-order nonlinear systems with unknown time varying delays. The appropriate Lyapunov Krasovskii functionals are chosen to deal with the unknown time varying delay uncertainties. The proper power function of the norm of the neural network’s power value is adopted to model the constructed unknown function in every step. And the problem of “explosion of complexity” can be solved by adopting the dynamic surface control. The designed controller can guarantee that all the states of the closed loop control systems and the tracking error are semi-global uniformly ultimately bounded. The results of a simulation example demonstrate the effectiveness of this scheme.