Nonlinear Robot Compliance Control Using Neural Networks

本研究的目的是建立一种新的方法， 利用神经网络探索非线性柔顺控制新方法 机器学习和机器人控制的可能性， 遥控操纵器 这里，顺应性被视为非线性的 从测量的力映射到校正的运动， 由多层神经网络表示，以及由 高斯网络 拟议研究的目标是 三倍 一个是开发一种新的方法来代表 “顺应性”处理高度非线性，如通过刚度 和阻尼矩阵。 第二个目标是发展一个 学习方法的产生和遵守的教学， 或力反馈策略。 神经网络方法允许 我们从教学数据中获得所需的顺应性， 人类操作员 它不需要明确的反馈定律， 详细的任务模型，如常规任务模型所需的任务模型， 分析方法 希望新办法还将 使我们能够将人类在柔顺运动控制方面的技能转移到 机器人和遥控机器人。 拟议的第三个目标 项目是开发一个实时的，神经网络控制器， 直接参与机器人控制系统的反馈回路。 必须开发有效的方法来分析和设计 非线性反馈系统，以实现平滑，稳定 应答 为了实现这些目标，将开展三个子项目 进行。 他们的目标是：1。 建立理论 非线性柔顺性的神经网络表示的基础。 2. 开发获取教学数据的技术， 数据，并训练神经网络学习 合规，3。开发一种真实的设计方法， 净反馈控制，以实现所需的动态 反应

The goal of this research is to establish a new method for nonlinear compliance control using neural nets to explore new possibilities in machine learning and control for robots and telemanipulators. Here, compliance is treated as nonlinear mapping from a measured force to a corrected motion and is represented by a multi-layer neural network, as well as by Gaussian networks. The objectives of the proposed research are three-fold. One is to develop a new method for representing "compliance" to deal with highly nonlinear, such as by stiffness and damping matrices. The second objective is to develop a learning methods for the generation and teaching of compliance, or force feedback strategies. The neural network approach allows us to teach a desired compliance from teaching data acquired from a human operator. It does not need explicit feedback laws and detailed task models such as those required for conventional analytic methods. It is hoped that the new approach will also allow us to transfer human skill in compliant motion control to robots and telemanipulators. The third objective of the proposed project is to develop a real-time, neural net controller that is involved directly in the feedback loop of robot control system. Efficient methods must be developed to analyze and design the nonlinear feedback system in order to accomplish smooth, stable responses. To achieve these goals, three subprojects will be conducted. Their objectives are to: 1. Establish a theoretical basis for the neural net representation of nonlinear compliance. 2. Develop techniques for acquiring teaching data, Processing the data, and training a neural network for learning a compliance, and 3. Develop a design method for real time, neural net feedback control in order to accomplish desirable dynamic response.