Design of Digital PID Control Systems Based on Sensitivity Analysis and Genetic Algorithms

Perng, Jau-Woei; Hsieh, Shan-Chang

doi:10.1007/s12555-018-0570-3

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…Step PM3: For each value of θ, solve (19) and (20) to determine the two PID controller gains selected in Step S3 above.…”

Section: Phase Margin Bound Regionmentioning

confidence: 99%

“…Applied sensor noise can be seen within the plot on top in Fig. 19. In the bottom plot, two different tracking error results for two different simulations, with and without sensor noise applied, were plotted for comparison.…”

Section: B Robustness Simulationsmentioning

confidence: 99%

“…While fewer in number, there have also been several papers in the literature that treat digital PID controller design. One recent example uses a genetic algorithm for optimizing digital PID gains for well known time domain criteria [19]. Just like similar approaches for continuous time PID controller design, a low order plant model with time delay is treated [19].…”

Section: Introductionmentioning

confidence: 99%

“…One recent example uses a genetic algorithm for optimizing digital PID gains for well known time domain criteria [19]. Just like similar approaches for continuous time PID controller design, a low order plant model with time delay is treated [19]. Most of the recent digital PID controller design papers are on application to a specific problem like CAN based dc motor control [20] and use an optimization method like particle swarms [20] or like a voltage source inverter application [21].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Digital PID Controller Design in Parameter Space and its Application to Automated Path Following

2021

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This paper presents a multi-objective digital PID controller design method using the parameter space approach of robust control. Absolute stability is treated first by finding the digital PID controller gain parameter space corresponding to closed loop poles being inside the unit circle. Additionally, phase margin, gain margin and a mixed sensitivity bound are treated as frequency domain constraints. Determination of digital PID controller parameter space regions satisfying these constraints is presented. All of these regions are superimposed to obtain a multi-objective digital PID controller gain parameter space solution region. The path following controller design of an automated driving vehicle is used as an example to illustrate the method. This multi-objective parameter space design approach can be extended to other digital controller forms also.

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“…Step PM3: For each value of θ, solve (19) and (20) to determine the two PID controller gains selected in Step S3 above.…”

Section: Phase Margin Bound Regionmentioning

confidence: 99%

Section: B Robustness Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Digital PID Controller Design in Parameter Space and its Application to Automated Path Following

2021

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“…Let's just say that control systems design is one of the engineering areas that have benefited with the dawn of EA. In particular, the use of such techniques for PID controllers design can be tracked down to more than twenty years ago and remain an up-to-date research subject [8][9][10][11]. PID based control loops are ubiquitous and can be found in several industrial plants and automotive systems, just to name a few.…”

Section: Controller Design With Eamentioning

confidence: 99%

Digital Controller Design of an Industrial Sewing Station using Evolutionary Algorithms

Moretti

Coelho

2019

Carpathian Journal of Electronic and Computer Engineering

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Development of increasingly efficient production methods is a competiveness driving factor for any company. Today, many of these improvements include the integration of technology-based solutions into processes traditionally operated by humans. In this context, the present work aims to report the controller performance of a prototype developed for semi-automatic sewing stations. This project was fostered by “Factory Play”, a Portuguese company that produces inflatable structures, under the technical supervision of the Polytechnic Institute of Bragança. At the present time, the sewing station travel speed is regulated by an embedded PID controller that has been previously tuned using classical methods. However, even if the overall performance is currently acceptable, additional experiments were made regarding the use of evolutionary based algorithms to attain a better dynamic response and flexibility. This article present the results obtained using those methods where it is possible to confirm that the use of evolutionary algorithm will simplify the design process while consistently leading to a suitable solution.

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An Air Pressure Control Method for Fast and Precise Inspection on Triple Action Pressure Sensors

Kim

Chung

Hong

2020

Int. J. Control Autom. Syst.

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Design of Digital PID Control Systems Based on Sensitivity Analysis and Genetic Algorithms

Cited by 11 publications

References 16 publications

Multi-Objective Digital PID Controller Design in Parameter Space and its Application to Automated Path Following

Multi-Objective Digital PID Controller Design in Parameter Space and its Application to Automated Path Following

Digital Controller Design of an Industrial Sewing Station using Evolutionary Algorithms

An Air Pressure Control Method for Fast and Precise Inspection on Triple Action Pressure Sensors

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