Research Initiation: Neural Network Robot Controller

主要研究者（PI）将研究人工 机器人控制中的神经网络 各种非线性自适应 控制问题将模拟一个单连杆机械手应用 力到各种物体。 柔性和刚性机械手将 与移动的和固定的对象交互以执行各种任务 非线性自适应控制器将 结合人工神经网络，学习非线性 机械手的动力学 计算机模拟将用于 基于神经网络的机器人控制器性能评估 网络. 神经网络逼近多变量的能力 表示机器人操纵器动力学的非线性函数将是 在前馈控制器架构中利用。 研究将 专注于神经网络的学习算法， 和灵活的单链路系统。 对磨损或拾取物体引起的动态变化的适应 也将被审查，因为将替代架构， controller. 这个项目的目标是创建一个神经网络 控制器，适用于任何控制问题。

The principal investigator (PI) will study the use of artificial neural networks in robotic control. A variety of nonlinear adaptive control problems will be simulated for a one-link manipulator applying force to various objects. Flexible and rigid manipulators will interact with mobile and fixed objects to conduct various tasks specified by the PI The nonlinear adaptive controller will incorporate an artificial neural network that learns the nonlinear dynamics of the manipulator. Computer simulations will be used to assess the performance of robotic controllers based on neural networks. The ability of neural networks to approximate multivariate nonlinear functions representing robot manipulator dynamics will be exploited in a feedforward controller architecture. Research will focus on learning algorithms for neural networks used with both rigid and flexible one-link systems. Adaptation to varying dynamics caused by wear or by picking up objects will also be examined, as will alternative architectures for controller. the goal of this project is to create a neural network controller that is applicable to any control problem.