A simplified Genetic Algorithm for online tuning of PID controller in LabView

Nazir, Abdul Jaleel Abdul; Gautham,; Surajan, Remya; Binu, L.S

doi:10.1109/nabic.2009.5393665

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…It is flexible to switch between different virtual instruments according to work and test requirements [2,3] . Meanwhile, adding, reducing or changing the size and function of the virtual instrument system can be easily done by resetting the software [4] .…”

Section: Introductionmentioning

confidence: 99%

Design of self-tuning PID controller with fuzzy variable parameters based on LabView

Liu

2015

2015 IEEE International Conference on Information and Automation

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In industrial production PID control technology is still in a dominant position. But the PID controllers without welltuned parameters will seriously affect, resulting in a large waste of resources as well as lack of efficiency and safety of production. Instead of manual tuning, modern complex industrial process demands a common computing program to get the tuning task done, so the self-tuning PID technology make a huge progress. Still the limitation of correspondence is hard to overcome.In this paper, a design of PID auto-tuning procedure based on LabView is introduced to solve this problem, which is compatible of the majority of communication with VISA interface. Selecting fuzzy tuning as the self-tuning method, it carries out real-time PID controller parameter tuning according to fuzzy rules. This article firstly accomplishes the design and simulation of process control with LabView toolkit, and then the practical temperature control of Zirconia Oxygen analyzer. The outcome verifies the capability and operability of the design.

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Section: Introductionmentioning

confidence: 99%

Design of self-tuning PID controller with fuzzy variable parameters based on LabView

Liu

2015

2015 IEEE International Conference on Information and Automation

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“…Simulation tests were performed over a third order proposed plant model. A Real-coded Genetic Algorithm (RGA) approach using floating point variables representation presented in [34] was used to optimize the PID parameters of a temperature system model, obtained by software identification tools; the system response was simulated compared to ZN and CC methods. Recently in [35] the RGA technique was applied for system identification and control tuning for a model reference adaptive control for a hybrid tank system.…”

Section: Introductionmentioning

confidence: 99%

PID-Controller Tuning Optimization with Genetic Algorithms in Servo Systems

Jaen-Cuéllar

Romero-Troncoso

Morales-Velazquez

et al. 2013

International Journal of Advanced Robotic Systems

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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 wellknown 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 GainPhase 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.

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Position control of brushless DC motor using harmony search algorithm optimization technique

Kumar

Chaursiya²

2017

2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA)

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A simplified Genetic Algorithm for online tuning of PID controller in LabView

Cited by 5 publications

References 10 publications

Design of self-tuning PID controller with fuzzy variable parameters based on LabView

Design of self-tuning PID controller with fuzzy variable parameters based on LabView

PID-Controller Tuning Optimization with Genetic Algorithms in Servo Systems

Position control of brushless DC motor using harmony search algorithm optimization technique

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