Optimized PID controller for an industrial biological fermentation process

Khan, Omar; Madhuranthakam, Chandra Mouli R.; Douglas, Peter; Lau, Heron; Sun, Jacob; Farrell, Patrick J.

doi:10.1016/j.jprocont.2018.09.007

Cited by 29 publications

(27 citation statements)

References 15 publications

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“…I is usually used to remove the static error, which can improve the stability of the system. The differential link can reflect the change of the deviation and speed up the response speed of the system, but the system will lose stability if the differential coefficient is too large [21]. The control process of traditional PID controller is shown in Fig 4. The mathematical model of the PID controller is shown in Eq (1).…”

Section: Proportional Integral and Derivative Bearing Controllermentioning

confidence: 99%

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

Sun

2020

PLoS ONE

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The hybrid electromagnetic and elastic foil gas bearing is explored based on the radial basis function (RBF) neural network in this study so as to improve its stabilization in work. The related principles and structure of hybrid electromagnetic and elastic foil gas bearings is introduced firstly. Then, the proportional, integral, and derivative (PID) bearing controller is introduced and improved into two controllers: IPD and CPID. The controllers and hybrid bearing system are controlled based on the RBF neural network based on deep learning. The characteristics of the hybrid bearing system are explored at the end of this study, and the control simulation research is developed based on the Simulink simulation platform. The effects of the PID, IPD, and CIPD controllers based on the RBF neural network are compared, and they are also compared based on the traditional particle swarm optimization (PSO). The results show that the thickness, spread angle, and rotation speed of the elastic foil have great impacts on the bearing system. The proposed CIPD bearing control method based on RBF neural network has the shortest response time and the best control effect. The controller parameter tuning optimization starts to converge after one generation, which is the fastest iteration. It proves that RBF neural network control based on deep learning has high feasibility in hybrid bearing system. Therefore, the results provide an important reference for the application of deep learning in rotating machinery.

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Section: Proportional Integral and Derivative Bearing Controllermentioning

confidence: 99%

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

Sun

2020

PLoS ONE

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“…As is known to all, PID is one of the earliest control strategies. Since its simple structure, good robustness, and high reliability, PID controller plays an important role in the closed industrial system [1], [2]. The PID controller is designed based on the error of the system, which uses proportion, integral, and differential to calculate the control quantity in order to achieve excellent performance.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm

et al. 2021

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“…PID controller improves the control performance using error information of a closed-loop system and includes the proportional, integral and differential terms of the errors. Up to now, PID control has been one of the most popular technologies in the industries (Qi and Meng, 2012; Yu et al, 2014; Khan et al, 2018). However, many practical industrial processes have complex mechanisms, high nonlinearities, and time-varying uncertainties, which bring about many difficulties to apply PID control with satisfied control performance.…”

Section: Introductionmentioning

confidence: 99%

Model-free adaptive PID control for nonlinear discrete-time systems

Zhang

Chi

2020

Transactions of the Institute of Measurement and Control

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This work explores a model-free adaptive PID (MFA-PID) control for nonlinear discrete-time systems with rigorous mathematical analysis under a data-driven framework. An improved compact form dynamic linearization (iCFDL) is proposed to transfer the original nonlinear system into an affined linear data model including a nonlinear residual term. Both a time-difference estimator and a gradient parameter estimator are designed to estimate the nonlinear residual uncertainties and the unknown parameters in the iCFDL model. Subsequently, a novel improved CFDL based MFA-PID (iCFDL-MFA-PID) control is proposed by incorporating these two estimators. The results are extended by the use of improved partial format dynamic linearization (iPFDL) and full format dynamic linearization (iFFDL). The theoretical results are shown using contraction mapping principle-based mathematical analysis, as well as simulations.

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Optimized PID controller for an industrial biological fermentation process

Cited by 29 publications

References 15 publications

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm

Model-free adaptive PID control for nonlinear discrete-time systems

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