.png){kind=icon}
.png){kind=icon}
Return
State constrained tracking control for nonlinear systems
.png){kind=icon}
OA
AI
DOI：10.1016/j.jfranklin.2015.05.003.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This work addresses the model reference tracking control problem. It aims to highlight the encountered difficulties and the proposed solutions to achieve the tracking objective. Based on a literature overview of linear and nonlinear reference tracking, the achievements and the limitations of the existing strategies are highlighted. This motivates the present work to propose clear control algorithms for perfect and approximate tracking controls of nonlinear systems described by Takagi-Sugeno models. First, perfect nonlinear tracking control is addressed and necessary structural conditions are stated. If these conditions do not hold, approximate tracking control is proposed and the choice of the reference model to be tracked as well as the choice of the criterion to be minimized are discussed with respect to the desired objectives. The case of constrained control input is also considered in order to anticipate and counteract the effect of the control saturation. (C) 2015 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keywords:
ITERATIVE LEARNING CONTROL
ADAPTIVE-CONTROL
FAULT-DETECTION
CONTROL DESIGN
FUZZY-MODEL
OBSERVERS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3.7
Papers:
6.4K
Citations:
1.5W