基于决策树与模糊神经网络的双机协调控制设计

在两个巨型操作装备夹持一个工件水平操作时,需要对其协调控制,以防止对工件的损伤。本文将协调控制过程整体设计为决策和执行两层,首先用适应于过程辨识的模糊神经网络为决策树提供对象模型,然后设计了分类结果为模糊隶属度的针对时间有序的决策树,为双机提供上层协调策略,并为单机控制器提供了输入,最后由模糊神经网络控制器根据决策树的分类决策执行单机控制,从而完成整个双机的协调控制过程。通过双操作机仿真及双液压缸实验与被动柔顺及PID控制进行了对比,均证明了该方法更优越。

Coordination control design based on decision tree and fuzzy neural network

Two heavy manipulators need to be controlled coordinately when they are grasping one workpiece to avoid the damage of the workpiece. A whole coordinate control method is designed, which includes a decision-making layer and an execution layer. First, an objective model for the decision tree is proposed by means of constructing a new fuzzy neural network, which especially adapts to process identification. Then a new kind of decision tree with membership function is designed to provide up-layer coordinate policy and fuzzy input of each single-controller in the low-layer. Finally, the fuzzy neural network controller executes the policy and thus to complete the whole adaptive coordinate control of the two heavy manipulators. The proposed coordinate control method is verified by simulation of the manipulators and experiments of hydraulic cylinders in comparison with passive compliance and PID control.