Bourns College of Engineering

UCR

Electrical Engineering

Defense Announcement


03.20.07 - Self-Organizing On-line Approximation Based Control
Yuanyuan Zhao

Ph.D. Defense

Tuesday, March 20, 2007
Bourns Hall – A277
10:00AM

TitleSelf-Organizing On-line Approximation Based Control

Abstract:  Nonlinear dynamics and model uncertainty exist in all physical plants and must be addressed directly by the control system for systems where a high level of performance is specified. The objective of this research is to develop adaptive approximation based control algorithms to achieve stability and accurate reference input tracking for nonlinear systems with modeling uncertainty.

Typically, adaptive approximation based control uses approximators with a predefined set of basis functions. The designer must therefore over design the approximator structure to guarantee that desired approximation accuracy is achievable for the unknown nonlinearities. Self-organizing on-line approximation based control defines and adapts both the approximator structure and its parameters during on-line operation. This offers the potential for increased performance with fewer computational resources. Recently trajectory exploration dependent methods have defined self-organizing approximators where new locally supported basis elements were incorporated when existing basis elements were insufficiently excited. However, these existing results were open loop in the sense that augmentation of the approximation structure was not dependent on the performance of the closed-loop control system.

In this presentation, an entirely new performance-dependent self-organizing function approximation approach will be presented. The designer specifies a positive performance criterion. In the self-organizing approach, the structure of the function approximator is defined by the control system during operation, with new approximator resources allocated as necessary to achieve the performance specification. For high dimensional systems, the decrease in the approximator resources allocated via performance-dependent self-organization, relative to prior allocation over the entire operation region, may be significant enough to enable more applications.

 
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