Open AccessPID-Controller Tuning Optimization with Genetic Algorithms in Servo Systems
Author(s) -
Arturo Yosimar Jaen-Cuéllar,
René de Jesús Romero-Troncoso,
Luis Morales-Velazquez,
Roque Alfredo Osornio-Ríos
Publication year - 2013
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/56697
Subject(s) - pid controller , phase margin , computer science , control theory (sociology) , genetic algorithm , matlab , control engineering , controller (irrigation) , control (management) , artificial intelligence , temperature control , engineering , machine learning , operational amplifier , bandwidth (computing) , amplifier , computer network , agronomy , biology , operating system
Performance improvement is the main goal of the study of PID control and much research has been conducted for this purpose. The PID filter is implemented in almost all industrial processes because of its well-known beneficial features. In general, the whole system's performance strongly depends on the controller's efficiency and hence the tuning process plays a key role in the system's behaviour. In this work, the servo systems will be analysed, specifically the positioning control systems. Among the existent tuning methods, the Gain-Phase Margin method based on Frequency Response analysis is the most adequate for controller tuning in positioning control systems. Nevertheless, this method can be improved by integrating an optimization technique. The novelty of this work is the development of a new methodology for PID control tuning by coupling the Gain-Phase Margin method with the Genetic Algorithms in which the micro-population concept and adaptive mutation probability are applied. Simulations using a positioning system model in MATLAB and experimental tests in two CNC machines and an industrial robot are carried out in order to show the effectiveness of the proposal. The obtained results are compared with both the classical Gain-Phase Margin tuning and with a recent PID controller optimization using Genetic Algorithms based on real codification. The three methodologies are implemented using software