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

这项研究考察了神经网络在涉及分布式机械柔性的系统的运动控制中的使用，这是大型空间结构系统和高速、高精度、轻量化操纵设备的特征。在控制这类系统中遇到的主要问题是与动态非线性和自由度之间的耦合有关，以及由于机械柔性而导致的系统的大动态阶数。目前的控制技术，即模型参考自适应控制和自校正自适应控制都是基于线性系统理论的。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，它们特别适用于动态阶数较大的系统。其他有用的特征包括他们的学习能力和容错能力。在本项目中，将对要控制的机械系统进行足够详细的建模，以提供其动力学的合理表示。该系统的神经控制器将被脱机训练到可接受的水平。然后，它将被安装在实际的机械系统上，其特性可能与它所在的系统的特性不完全匹配。随着对系统的在线了解越来越多，控制性能有望不断提高。比较了神经控制器、模型参考控制器和自校正控制器的优缺点。

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.