Date of Completion
5-8-2017
Embargo Period
5-1-2017
Advisors
Chengyu Cao, Jiong Tang, Ying Li
Field of Study
Engineering
Degree
Master of Engineering
Open Access
Campus Access
Abstract
Nonlinearity is one of major challenges in modern engineering system that we need to deal with in the control strategy design. In contrast to a linear system, the superposition principle normally cannot be simply applied to a nonlinear system. The feature of nonlinear system varies significantly from one to another, which brings more difficulty to conclude a uniform controller design methodology than the linear system. In addition, most of systems contain the feature of constraints for both input and/or output implicitly or explicitly, which makes it more complicated for the controller to maintain within required margins. The nonlinearity and constraints may not necessarily appear simultaneously in a system, but the resulted challenges in control system design cannot be ignored or avoided.
The basic design of L1 adaptive control aims to solve the nonlinear system with guaranteed response. In the absence of constrained output, however, the traditional L1 adaptive control design structure needs to be revised and adjusted by incorporating other control design strategy such as model predictive control so that the basic scope of L1 adaptive control can be extended to provide effective solutions to such problems.
The objective of this thesis is to solve the controller design problem for the system in combination of nonlinearity, output constraints based on the fundamental L1 adaptive control structure with adequate modifications. The system dynamic feature of the process that has been investigated in this thesis differs from case to case though, a uniform design method for the L1 adaptive control has been generated to solve those problems as a whole.
Recommended Citation
wang, chuan, "L1 Adaptive Control Application to Constrained Nonlinear Systems" (2017). Master's Theses. 1058.
https://digitalcommons.lib.uconn.edu/gs_theses/1058
Major Advisor
Chengyu Cao