A neuro-adaptive approach to near-optimal controller design for a class of constrained systems

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Rad Moghadam S.; Farrokhi M.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: TABRIZ JOURNAL OF ELECTRICAL ENGINEERING Year:2023 | Volume:53 | Issue:2 Page(s): 115-126

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

A neuro-adaptive approach to near-optimal controller design for a class of constrained systems

Author(s)

Rad Moghadam S. | Farrokhi M. | Issue Writer Certificate

Keywords

Near-optimal controlQ4

Constrained controlQ4

control barrier functionQ4

adaptive modelQ4

recurrent neural networkQ4

Abstract

This paper proposes a controller capable of satisfying the output constraints while providing a near-optimal performance. The method is designed for square nonlinear systems with stable internal dynamics. To this end, using a primary performance index and Taylor approximation, the problem is approximated by a constrained programming, whose constraints are written with the aid of control barrier function (CBF) to ensure the output restrictions. As a result, a constrained near-optimal performance without encountering difficulties in Hamilton-Jacobi-Bellman (HJB) equations is obtained. In order to overcome the model uncertainties, which appear in the optimization problem, an adaptive structure is formulated. The online solution of the constrained optimization is obtained using a Projection recurrent neural network (PRNN). As a result, a closed-form solution is provided that can be simply implemented without requiring additional solvers or toolboxes. Stability of the proposed method and constraints satisfaction are addressed thoroughly. Finally, effectiveness of the proposed method in realizing the aforementioned aims are illustrated through simulations on trajectory control of a surface vessel system and a comparative study on the constrained stabilization of a pendulum. The first simulation example shows the effectiveness of the method in constrained tracking, while the second example confirms the proper performance of the controller in stabilization and regulation applications.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops