不确定非线性系统变结构控制器设计

传统的控制方法大多需要知道被控对象的数学模型 ,而许多复杂的实际系统 ,其准确的数学模型往往不易得到 ,或者难以求解。采用自适应滑模变结构控制方法 ,对形如 x(n) =f( x,t) g( x,t) u d( t)的非线性系统进行跟踪控制 ,其中 f( x,t)和 g( x,t)为未知非线性函数 ,且其边界未知 ,d( t)为未知有界扰动 ,为减少对被控制对象信息的依赖性 ,没有利用专家知道 ,也没有利用被控对象的历史运行数据 ,通过利用基函数类神经网络动态逼近函数 f( x,t)和 g( x,t) ,对自适应调整控制系统输入 ,得出基于 RBF网络的滑模变结构自适应控制方案 ,经过理论分析 ,证明了控制方案的稳定性。仿真结果表明了该方案的可行性的实用性

Designing Sliding Mode Controller for Nonliear System Based-on Neural N etworks

Traditional controllers normally need the mathematical model of the plant. But it is very hard to get the precise mathematical model of the plant or to get its result.In order to minimize the dependence on the plant information,Adaptive Sliding Mode Control (ASMC) is applied to track and control a family nonlinear system.The scheme can function without any plant model,without expert knowledge,and without the previous plant data,which uses the radial based function neural networks to adjust the system input,so as to ensure the stability and adaptation.Its global stability is proved through theoretical analysis.The simulation of the controller shows its strong robustness and feasibility.