具有未建模动态和输入饱和约束的纯反馈非线性系统自适应神经网络控制

Adaptive neural control of pure-feedback nonlinear systems withunmodeled dynamics and input saturation

目的:研究一类具有未建模动态和输入饱和约束的纯反馈非线性系统镇定问题。方法:在控制器设计过程中,引入动态信号处理未建模动态,用径向基函数神经网络逼近未知非线性函数,然后通过Backstepping方法导出自适应神经网络控制律。结果:通过稳定性分析,可得闭环系统所有信号半全局一致终结有界,并由仿真结果可知,系统状态、自适应参数以及未建模动态分别在t≈2 s,t≈4 s,t≈2.2 s时到达原点的小邻域内。结论:本文提出的控制方案证明闭环系统所有信号半全局一致终结有界。

Aims:This paper investigates a stabilization problem for a class of pure-feedback nonlinear systems with unmodeled dynamics and input saturation.Methods:In the controller design procedure,a dynamic signal was introduced to handle the unmodeled dynamics;and radial basis function(RBF)neural networks were used to approximate the unknown nonlinearities.Then an adaptive neural controller was exported via the Backstepping method.Results:By stability analysis,it proved that all the signals in the resulting closed loop systems were semi-globally uniformly ultimately bounded(SGUUB).The simulation results showed that all the states,adaptive parameters and unmodeled dynamics reached the small neighborhood of the origin at t≈2 s,t≈4 s and t≈2.2 s respectively.Conclusions:The proposed control method proves that all the signals in the resulting closed loop systems are SGUUB.