Adaptive Backstepping Extremum Seeking Control of a Class of Nonlinear Systems

Traditional methods for extremum seeking control (ESC) disregard possible prior knowledge of the system model. In practice, however, these models are usually known, but they contain uncertain parameters. Assuming that partial knowledge about the system model is available, ESC problem has been solved...

Full description

Saved in:
Bibliographic Details
Published in:Iranian journal of science and technology. Transactions of mechanical engineering 2019-07, Vol.43 (Suppl 1), p.415-423
Main Authors: Yaghoubi, Shakiba, Dehghani, Maryam, Khayatian, Mohammad
Format: Article
Language:English
Subjects:
Engineering
Mechanical Engineering
Research Paper
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Traditional methods for extremum seeking control (ESC) disregard possible prior knowledge of the system model. In practice, however, these models are usually known, but they contain uncertain parameters. Assuming that partial knowledge about the system model is available, ESC problem has been solved recently for two different cases where systems outputs are considered to be measurable in one and immeasurable in the other. Available results in the case with immeasurable outputs are restricted to a certain class of systems in which the cost function only includes the states of the system that are directly affected by the control input. The contribution of this paper is to solve the ECS problem for a wider class of nonlinear system with parametric uncertainty whose outputs are not measurable. To this purpose, we use adaptive backstepping control technique: extremizing the cost function is achieved by driving the states to their desired values using input controls directly or indirectly. To illustrate the efficiency of the proposed framework, we provide simulation results.
ISSN:2228-6187
2364-1835
DOI:10.1007/s40997-018-0167-8