论文标题
合作控制设计的遗传模糊方法与Q学习之间的比较
Comparison Between Genetic Fuzzy Methodology and Q-learning for Collaborative Control Design
论文作者
论文摘要
本文介绍了两种机器学习方法，即遗传模糊方法和Q学习的比较。这些方法用于为一组协作机器人建模控制器，这些机器人需要一起工作以将对象带到目标位置。机器人是固定的，并通过弹性电缆连接到对象上。在此问题中考虑的一个主要约束是机器人无法彼此通信。这意味着在任何瞬间，每个机器人都没有其他机器人的运动或控制信息，并且只能基于对象的运动状态拉或释放其电缆。这个分散的控制问题提供了一个很好的例子，可以测试这两种机器学习方法的功能和限制。首先使用一组训练方案对系统进行训练，然后应用于广泛的测试集，以检查每种方法所实现的概括。
A comparison between two machine learning approaches viz., Genetic Fuzzy Methodology and Q-learning, is presented in this paper. The approaches are used to model controllers for a set of collaborative robots that need to work together to bring an object to a target position. The robots are fixed and are attached to the object through elastic cables. A major constraint considered in this problem is that the robots cannot communicate with each other. This means that at any instant, each robot has no motion or control information of the other robots and it can only pull or release its cable based only on the motion states of the object. This decentralized control problem provides a good example to test the capabilities and restrictions of these two machine learning approaches. The system is first trained using a set of training scenarios and then applied to an extensive test set to check the generalization achieved by each method.
