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

本研究探讨了神经网络在分布式机械灵活性系统的运动控制中的应用，这是大空间结构系统和高速、高精度、轻质操纵装置的特点。控制此类系统所遇到的主要问题与动态非线性和自由度之间的耦合以及由于机械柔性而导致的系统的大动态阶数有关。目前的控制技术，即模型参考自适应控制和自整定自适应控制都是基于线性系统理论。神经网络的一个优点是能够直接识别非线性动力学并将其用于控制动作的确定。第二个优点是，它们特别适用于大动态秩序的系统。其他有用的特性包括它们的学习能力和容错能力。在这个项目中，要控制的机械系统将被足够详细地建模，以提供其动力学的合理表示。系统的神经控制器将被离线训练到一个可接受的水平。然后将其安装在实际的机械系统上，该系统的特性可能与训练它的系统不完全匹配。通过对系统的在线学习，期望系统的控制性能不断提高。比较了神经控制器、模型参考控制器和自整定控制器的相对优点。

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.