A fuzzy PID controller with nonlinear compensation term for mold level of continuous casting process

Feng, Yuanyue; Wu, Min; Chen, Xin; Chen, Luefeng; Du, Sheng

doi:10.1016/j.ins.2020.06.024

Cited by 33 publications

(18 citation statements)

References 37 publications

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“…The FOPID controller is given as which is commonly known as PI w D z controller, where the real numbers w and z, respectively, are designated as orders of integration and differentiation. Clearly, FOPID controllers have two additional variables when compared to classical PID controller [6,8,15,21,23]. Due to this, FOPID controllers can be designed such that these perform better control and are less sensitive to parameter variations [20].…”

Section: Fractional-order Proportional-integral-derivative (Fopid) Comentioning

confidence: 99%

Design of FOPID controller for higher order continuous interval system using improved approximation ensuring stability

2021

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This contribution deals with the design of a fractional-order proportional-integral-derivative (FOPID) controller through reduce-order modeling for continuous interval systems. First, a higher order interval plant (HOIP) is considered. The reduced-order interval plant (ROIP) for considered HOIP is derived by multipoint Padé approximation integrated with Routh table. Then, FOPID controller is designed for ROIP to satisfy the phase margin and gain cross over frequency. Thus obtained FOPID controller is implemented on HOIP also to validate the performance of designed FOPID on HOIP. A single-input-single-output (SISO) test system is taken up to elaborate the entire process of controller design. The outcomes affirm the validity of the designed FOPID controller. The designed FOPID controller produced stable results retaining the phase margin and gain cross-over frequency when implemented on HOIP. The results further proved that FOPID controller is working efficiently for ROIP and HOIP.

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Section: Fractional-order Proportional-integral-derivative (Fopid) Comentioning

confidence: 99%

Design of FOPID controller for higher order continuous interval system using improved approximation ensuring stability

2021

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“…Many control solutions have been proposed, but their complete understanding and compensation is still complex [26,28]. Unclogging and sudden changes of casting speed are sudden disturbances with a damaging effect that can be compensated for with a control approach of well sudden disturbance rejection [2,29]. Finally, slow disturbances, including valve clogging, valve erosion and changes of tundish level, also need specific treatment to compensate [2].…”

Section: Process Descriptionmentioning

confidence: 99%

Neuro-Fuzzy System for Compensating Slow Disturbances in Adaptive Mold Level Control

González-Yero

Leyva

Mendoza

et al. 2020

Metals

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Good slow disturbances attenuation in a mold level control with stopper rod is very important for avoiding several product defects and keeping down casting interruptions. The aim of this work is to improve the accuracy of the diagnosis and compensation of an adaptive mold level control method for slow disturbances related to changes of stopper rod. The advantages offered by the architecture, called Adaptive-Network-based Fuzzy Inference System, were used for training a previous model. This allowed learning based on the process data from a steel cast case study, representing all intensity levels of valve erosion and clogging. The developed model has high accuracy in its functional relationship between two compact input variables and the compensation coefficient of the valve gain variations. The future implementation of this proposal will consider a combined training of the model, which would be very convenient for maintaining good accuracy in the Fuzzy Inference System using new data from the process.

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“…(6) A fuzzy PID controller with nonlinear compensation term is designed, in which the parameters of membership functions are optimized by PSO algorithm. It shows that F-PID can achieve satisfactory control effect in areas like suppressing random disturbances comparing with classical PID [36]. 7A Takagi-Sugeno fuzzy PID control is proposed for variable pitch system and the PI parameters of the current loop are set by using the internal model principle.…”

Section: A Related Workmentioning

confidence: 99%

Fuzzy Controllers With Neural Network Predictor for Second-Order Linear Systems With Time Delay

Liu

Tao

et al. 2020

IEEE Access

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Artificial intelligence methods are widely applied in advanced control practices such as fuzzy calculation-based intelligent control method, neural network-based predictive control method, etc. Control performances are expected to be improved such as: faster control speed, smaller steady-state error, and less repeated manual tuning workloads in harmful environments for engineers. Main works are as follows: Firstly, change ratio-based fuzzy adjusted PID (FA-PID) method is improved. The adjusted parameters of FA-PID are the multiplication result of the change ratio at the current control cycle and the control parameters at the time of previous adjustment cycle. Secondly, prediction of back propagation neural networks-based fuzzy-PID (BPNN-F-PID) and prediction of back propagation neural networks-based FA-PID (BPNN-FA-PID) are improved, in which the adjusted control parameters are calculated according to the predicted output of control system. Thirdly, comparative simulations of all the above methods are implemented, a series of better effects are found. Effects are as follows: The improved controllers have better performance such as faster control speed, better ability of anti-interference, restrained overshoot and smaller steady-state error.

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A fuzzy PID controller with nonlinear compensation term for mold level of continuous casting process

Cited by 33 publications

References 37 publications

Design of FOPID controller for higher order continuous interval system using improved approximation ensuring stability

Design of FOPID controller for higher order continuous interval system using improved approximation ensuring stability

Neuro-Fuzzy System for Compensating Slow Disturbances in Adaptive Mold Level Control

Fuzzy Controllers With Neural Network Predictor for Second-Order Linear Systems With Time Delay

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