Research Initiation: Neural Network Robot Controller

研究启动：神经网络机器人控制器

• 批准号: 8810478
• 负责人: Neil Cotter
• 金额: $ 6万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-08-01 至 1991-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8810478&HistoricalAwards=false
• 关键词: Research; Initiation; Neural; Network; Robot

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.

首席研究员（PI）将研究人工神经网络在机器人控制中的应用。针对单连杆机械臂对各种物体施加力的问题，将模拟各种非线性自适应控制问题。柔性和刚性机械臂将与移动和固定物体相互作用，以执行PI指定的各种任务。非线性自适应控制器将包含学习机械臂非线性动力学的人工神经网络。计算机模拟将用于评估基于神经网络的机器人控制器的性能。神经网络近似表示机器人操纵臂动力学的多元非线性函数的能力将在前馈控制器体系结构中得到利用。研究将集中于神经网络的学习算法，用于刚性和柔性单链系统。对磨损或拾取物体引起的动态变化的适应也将被检查，控制器的替代架构也将被检查。这个项目的目标是创建一个适用于任何控制问题的神经网络控制器。