4.7 Article

New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder-Mead algorithm

期刊

APPLIED SOFT COMPUTING
卷 62, 期 -, 页码 216-229

出版社

ELSEVIER
DOI: 10.1016/j.asoc.2017.10.007

关键词

Automatic voltage regulator; PID controller; Optimization; Nelder-Mead algorithm; Ant Colony Optimizationa

资金

  1. Vanier Canada Graduate Scholarship
  2. Michael Smith Foreign Study Supplements Program from the Natural Sciences and Engineering Research Council of Canada
  3. Ministerio de Economia y Competitividad (Spain) [DPI2015-71443-R]
  4. Bourse Mobilite Etudiante from Ministere de l'Education du Quebec
  5. CEMF Claudette MacKay-Lassonde Graduate Engineering Ambassador Award
  6. SWAAC Bourseau merite pour etudiantes de cycles superieurs

向作者/读者索取更多资源

In this paper, an optimal gain tuning method for PID controllers is proposed using a novel combination of a simplified Ant Colony Optimization algorithm and Nelder-Mead method (ACO-NM) including a new procedure to constrain NM. To address Proportional-Integral-Derivative (PID) controller tuning for the Automatic Voltage Regulator (AVR) system, this paper presents a meta-analysis of the literature on PID parameter sets solving the AVR problem. The investigation confirms that the proposed ACO-NM obtains better or equivalent PID solutions and exhibits higher computational efficiency than previously published methods. The proposed ACO-NM application is extended to realistic conditions by considering robustness to AVR process parameters, control signal saturation and noisy measurements as well as tuning a two-degree-of-freedom PID controller (2DOF-PID). For this type of PID, a new objective function is also proposed to manage control signal constraints. Finally, real time control experiments confirm the performance of the proposed 2DOF-PIDs in quasi-real conditions. Furthermore, the efficiency of the algorithm is confirmed by comparing its results to other optimization algorithms and NM combinations using benchmark functions. (C) 2017 Elsevier B.V. All rights reserved.

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