高度不确定对象的智能控制算法研究与仿真

Highly Uncertain Object's Intelligent Control Algorithms and Simulation

针对现有不确定对象的智能控制中权限分配过于复杂,以及动态性方面的不足,从实际的角度提出了一种新的算法,基于约束神经网络的不确定对象的智能控制算法。通过分析不确定对象的智能控制中,控制角色的的对应关系,构造了一个可用于对控制模型的不确定性进行准确估计的约束神经网络,添加可以对控制参数进行约束的对应关系。完成不确定对象的智能控制,后期的实验结果表明,本文的方法能够较好的对不确定对象实现精度较高的智能控制,取得了较好的效果。

Existing uncertain object's intelligent control authority distribution is too complex,and the deficiency of dynamic, from a practical point of view,a new algorithm was proposed based on constraint neural network uncertain object of intelligent control algorithm.Through the analysis of the uncertain object of intelligent control,the control role of the corresponding relation of the structure and a can be used to control the model uncertainty accurately estimated constraint neural network,add to control parameters are constraint corresponding relation.Complete uncertain object's intelligent control,later the experiment results show that the method can better to uncertain object to realize intelligent control and high accuracy,and good effects have been achieved.