Neuro-Controllers for Motion Control Systems with MechanicalDistributed Flexibility (SGER)

这项研究探讨了神经网络在 涉及分布式机械的系统的运动控制 柔性是大型空间结构的一个特点 高速、高精度、重量轻 操纵装置。 遇到的主要问题 控制这样的系统与动态 自由度之间的非线性和耦合，以及 由于系统的大动态阶数， 机械柔性 目前的控制技术，即， 模型参考自适应控制与自校正自适应 控制是基于线性系统理论。 一个优点 是它们能够直接识别 非线性动力学，并将其用于确定 控制动作。 第二个优点是， 特别适用于大动态阶数的系统。 其他有用的特征包括他们的学习能力 和容错性。 在这个项目中，机械 要控制的系统将被充分建模 详细说明，以提供其合理的代表性 动力学 这个系统的神经控制器 离线训练到可接受的水平。 然后它将 安装在实际的机械系统上， 特性可能与系统的特性不完全匹配 它所接受的训练。 控制性能 期望通过学习越来越多的东西来不断提高 关于在线系统。 神经外科的相对优势- 控制器、模型参考控制器和自校正 控制器将进行比较。

This research examines the use of neural networks in the motion control of systems involving distributed mechanical flexibility, a characteristic of large space structures systems and of high speed, high precision, light weight manipulation devices. The major problems encountered in controlling such systems are associated with dynamic nonlinearities and coupling between degrees of freedom, and with the large dynamic order of the system due to mechanical flexibility. Current control techniques, i.e., model reference adaptive control and self tuning adaptive control are based on linear systems theory. An advantage of neural networks is their ability to directly identify nonlinear dynamics and use it in the determination of the control action. A second advantage is that they are particularly suitable for systems of large dynamic order. Other useful characteristics include their learning ability and fault tolerance. In this project, the mechanical system to be controlled will be modeled in sufficient detail to provide a reasonable representation of its dynamics. The neuro-controller for the system will be trained off line to an acceptable level. It will then be installed on the actual mechanical system whose characteristics may not perfectly match those of the system on which it was trained. The control performance is expected to continuously improve by learning more and more about the system on line. The relative merits of neuro- controllers, model reference controllers, and self tuning controllers will be compared.