带有死区模型的严格反馈非线性系统的自适应神经网络控制

The Adaptive Neural Network Control for Strict-Feedback Nonlinear Systems with Unknown Dead-zone Model

基于后推设计方法,Nussbaum函数的性质及积分型李亚普诺夫函数,提出了一种自适应神经网络控制器的设计方案。通过引入示性函数,提出一种简化死区模型,取消了死区模型的倾斜度相等的条件。此外,该方法取消了函数控制增益符号已知和死区模型参数上界、下界已知的条件。理论分析证明了闭环系统是半全局一致终结有界。

Based on the back-stepping design method, the property of Nussbaum function and integral-type Lyapunov function, a design scheme of adaptive neural network controller is proposed in this article. By introducing characteristic function for the dead-zone model in the systems, a simplified dead-zone model is developed, removing the condition of the equal slope with defined region. In addition, this approach does not require a priori knowledge of the sign of the control gain and the upper bound and lower bound of dead zone model parameter to be known a priori. By theoretical analysis, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded.