Design of an Optimized GMV Controller Based on Data-Driven Approach

DOI

10.2991/jrnal.k.210922.006How to use a DOI?

Keywords

PID controller; GMV; Nelder–Mead method; data-driven approach

Abstract

This paper presents a data-driven scheme that can obtain the optimized Generalized Minimum Variance (GMV) control parameters by applying the Nelder–Mead (NM) method based on Proportional-Integral Derivative (PID) controller for linear system. An adjustable λ is included in the GMV-PID controller. In the existing GMV-PID controller, the PID parameters are calculated by simply changing λ manually. Therefore, it is hard to get desirable control performance. The NM method is introduced to improve the control performance. The application of NM method can optimize the calculation of λ. However, the objective function in NM method needs the output calculation. In other words, the model information is inevitably to obtain output. To achieve model-free design scheme, the estimation of closed-loop response method is introduced in database-driven approach. Using obtained closed-loop data can predict the output and then substitute it into the objective function without any model information of the process. The effectiveness is verified by experiment.

Copyright

© 2021 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Liying Shi
AU  - Zhe Guan
AU  - Toru Yamamoto
PY  - 2021
DA  - 2021/10/09
TI  - Design of an Optimized GMV Controller Based on Data-Driven Approach
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 180
EP  - 185
VL  - 8
IS  - 3
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.210922.006
DO  - 10.2991/jrnal.k.210922.006
ID  - Shi2021
ER  -