163
Views
6
CrossRef citations to date
0
Altmetric
Articles

Graphical robust PID tuning for disturbance rejection satisfying multiple objectives

, , , &
Pages 1701-1711 | Received 30 Dec 2016, Accepted 21 Apr 2018, Published online: 04 Jun 2018
 

Abstract

In this article, a novel graphical tuning method is proposed to determine all feasible parameters of PID controller satisfying multiple objectives including disturbance rejection for a wide range of processes. The proposed method is based on a graphical method and the obtained parameters can satisfy a given analyzing reference to disturbance ratio (RDR) index, which is used in this paper to evaluate the disturbance rejection performance. In a similar way, all the parameters satisfying transient state specification of set-point tracking response and robustness specification are determined. As a result, the intersection region presents all available parameters that can provide multiple objectives. This paper proposes an alternative way to optimize the PID controller satisfying multiple objectives. Since no optimization algorithm is used, the proposed method is simpler than PID design methods using optimization method. Three examples are given to demonstrate the flexibility and effectiveness of the proposed method.

Additional information

Funding

This work is supported by the National Natural Science Foundation of China (61673004), National Natural Science Foundation of China (61273132) and the Fundamental Research Funds for the Central Universities (Grant ZY1619).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,086.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.