Abstract
The objective of this research is to provide a method for design of control systems when the plant involved is highly variable in both structure and parameter values. This typifies many manufacturing processes, which in general are highly sensitive to intrinsic properties of the infeed material, have poorly understood or controlled boundary conditions, and often have highly non-linear characteristics. In addition, the disturbances most frequent in a manufacturing process are best characterized as parameter changes rather than state variable changes. The choice of a parameter adaptive control paradigm is obvious since it has the property of optimizing system response characteristics in the face of wide plant parameter variations. Model Reference Adaptive Systems (MRAS) enjoy an ease of performance specification, and again seem applicable; however, there is a restriction within current theory that the plant be characterized in its structure well enough to have a reference model of identical order. This restriction implies a static plant structure and more a priori information than is often present. During this contract the basic design characteristics of certain MRAS have been studied and problems with reduced order models identified, such as noise and input magnitude sensitivity and instability caused by high frequency learning signals. This has lead to certain solutions and resulting guidelines for design. This is then followed by design studies for a position servomechanism (which serves as the foundation of many automated manufacturing processes). This study expanded the domain from MRAS to self-tuning controllers (STC) and found that both can deal with reduced order models if the initial parameter values are not too distant from the actual values. However, the STC appears to be the more robust. 12 refs., 56 figs.
Submitted Version (
Free)
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call