Nonlinear Robot Compliance Control Using Neural Networks

这项研究的目的是建立一种新的基于神经网络的非线性顺应控制方法，以探索机器人和遥操作机器人在机器学习和控制方面的新可能性。这里，柔度被视为从测量力到校正运动的非线性映射，并由多层神经网络和高斯网络表示。拟议研究的目标有三个方面。一种是发展一种新的方法来表示“柔度”，以处理高度非线性，如通过刚度和阻尼阵。第二个目标是开发一种学习方法，用于生成和教授遵从性或强制反馈策略。神经网络方法允许我们从从人类操作员那里获得的教学数据中教授所需的遵从性。它不需要明确的反馈规律和详细的任务模型，如传统分析方法所需的那些。人们希望，这种新方法还将使我们能够将人类在顺应运动控制方面的技能转移到机器人和远程机械手身上。该项目的第三个目标是开发一个实时的神经网络控制器，该控制器直接参与机器人控制系统的反馈回路。为了获得平稳、稳定的响应，必须发展有效的方法来分析和设计非线性反馈系统。为实现这些目标，将开展三个分项目。其目的是：1.建立非线性柔度的神经网络表示的理论基础。2.开发教学数据的获取、处理和训练神经网络以学习顺应性的技术；3.开发实时神经网络反馈控制的设计方法，以实现理想的动态响应。

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.