Cascade control of superheated steam temperature with neuro-PID controller

Zhang, Jianhua; Zhang, Fenfang; Ren, Ming; Hou, Guolian; Fang, Fang

doi:10.1016/j.isatra.2012.06.008

Cited by 51 publications

(23 citation statements)

References 25 publications

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“…The single-segment method is appropriate for modelling of evaporator when the specific heat of the fluid in the heat exchanger does not change with temperature. A zone modelling technique can be used where the evaporator has three distinct phases: liquid, liquid-vapor and superheat zone (Quoilin et al, 2010;Tian et al, 2012;Wang et al, 2012;Zhang et al, 2012and Hou et al, 2011. The heat transfer equations in each zone are solved by applying this method.…”

Section: Evaporatormentioning

confidence: 99%

Dynamic model of supercritical Organic Rankine Cycle waste heat recovery system for internal combustion engine

Chowdhury

Nguyen

Thornhill

2017

Int.J Automot. Technol.

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ABSTRACTThe supercritical Organic Rankine Cycle (ORC) for the Waste Heat Recovery (WHR) from Internal Combustion (IC) engines has been a growing research area in recent years, driven by the aim to enhance the thermal efficiency of the ORC and engine. Simulation of a supercritical ORC-WHR system before a real-time application is important as high pressure in the system may lead to concerns about safety and availability of components. In the ORC-WHR system, the evaporator is the main contributor to thermal inertia of the system and is considered to be the critical component since the heat transfer of this device influences the efficiency of the system. Since the thermophysical properties of the fluid at supercritical pressures are dependent on temperature, it is necessary to consider the variations in properties of the working fluid. The well-known Finite Volume (FV) discretization method is generally used to take those property changes into account. However, a FV model of the evaporator in steady state condition cannot be used to predict the thermal inertia of the cycle when it is subjected to transient heat sources. In this paper, a dynamic FV model of the evaporator has been developed and integrated with other components in the ORC-WHR system. The stability and transient responses along with the performance of the ORC-WHR system for the transient heat source are investigated and are also included in this paper.

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Section: Evaporatormentioning

confidence: 99%

Dynamic model of supercritical Organic Rankine Cycle waste heat recovery system for internal combustion engine

Chowdhury

Nguyen

Thornhill

2017

Int.J Automot. Technol.

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“…Although entropy-based indexes provide general measures for non-Gaussian systems, it should be noted that an entropy metric has the shift invariance property, that is, for two random variables with the same shape of probability density functions (PDFs), they may have the same entropy values but 3 completely different mean values. This suggests that simply minimising entropy-based measures can be misleading in output tracking control.…”

Section: Model-free Controllers Have Been Well Developed Through Minimentioning

confidence: 99%

“…This can be achieved by employing minimum entropy criterion in controller design to make the shape of the tracking error PDF as narrow as possible [21][22][23]. For such a purpose, the following quadratic performance index is formulated based on the Renyi's entropy of the tracking error [3]. here the centre of the kernel function is set to zero.…”

Section: Entropy-based Tracking Error Performance Indexmentioning

confidence: 99%

“…Case study: superheated steam temperature control The proposed NN cascade control approach is applied to regulate the superheated steam temperature of a boiler in a 300 MW power plant using our previously developed process model [3]. In the simulation, the sampling period is taken to be The control results obtained in [3] are also presented to support the comparison, in which a NN-PID controller designed on EEC is served as the primary controller.…”

mentioning

confidence: 99%

“…In the simulation, the sampling period is taken to be The control results obtained in [3] are also presented to support the comparison, in which a NN-PID controller designed on EEC is served as the primary controller. The secondary controller in [3] is the same proportional controller as in this work.…”

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confidence: 99%

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Adaptive neural network cascade control system with entropy‐based design

Zhang

Zhou

Ren

et al. 2016

IET Control Theory & Applications

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This version is available at https://strathprints.strath.ac.uk/55772/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Abstract: A neural network (NN) based cascade control system is developed, in which the primary PID controller is constructed by NN. A new entropy-based measure, named the centred error entropy (CEE) index, which is a weighted combination of the error cross correntropy (ECC) criterion and the error entropy criterion (EEC), is proposed to tune the NN-PID controller. The purpose of introducing CEE in controller design is to ensure that the uncertainty in the tracking error is minimised and also the peak value of the error probability density function (PDF) being controlled towards zero. The NN-controller design based on this new performance function is developed and the convergent conditions are. During the control process, the CEE index is estimated by a Gaussian kernel function. Adaptive rules are developed to update the kernel size in order to achieve more accurate estimation of the CEE index. This NN cascade control approach is applied to superheated steam temperature control of a simulated power plant system, from which the effectiveness and strength of the proposed strategy are discussed by comparison with NN-PID controllers tuned with EEC and ECC criterions.

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The research of superheated steam temperature control based on generalized predictive control algorithm and adaptive forgetting factor

Jiang

Qian

Pan

et al. 2019

Adaptive Control & Signal

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SummaryThe superheated steam temperature system of the thermal power plant has the characteristics of large inertia, nonlinearity, and strong time variation, which make it difficult to be controlled. To address these problems, this paper proposes a generalized predictive control algorithm with an adaptive forgetting factor. First, based on a fuzzy algorithm and a recursive least squares algorithm, the controlled object's model can be quickly and accurately obtained with the adaptive forgetting factor in real time. It overcomes the nonlinear and time‐varying problems of the controlled object in the control progress. Meanwhile, it also solves the problem of data saturation and the weight assignment of the “new and old” data during online identification. Second, an adaptive generalized predictive controller algorithm has been developed with the controlled object. It solves the large inertia problem of the controlled object. Finally, through establishing simulation model of the superheated steam temperature system and simulating, the results show that the proposed method has better control performance, antidisturbance ability, adaptability, and robustness. Moreover, it has a certain reference significance for the design of a practical control system.

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Cascade control of superheated steam temperature with neuro-PID controller

Cited by 51 publications

References 25 publications

Dynamic model of supercritical Organic Rankine Cycle waste heat recovery system for internal combustion engine

Dynamic model of supercritical Organic Rankine Cycle waste heat recovery system for internal combustion engine

Adaptive neural network cascade control system with entropy‐based design

The research of superheated steam temperature control based on generalized predictive control algorithm and adaptive forgetting factor

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