A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system

Attaran, Seyed Mohammad; Yusof, Rubiyah; Selamat, Hazlina

doi:10.1016/j.applthermaleng.2016.01.025

Cited by 62 publications

(21 citation statements)

References 16 publications

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“…The features of this research are included as (1) the decoupling method is focused on overcoming the issue of coupling relative humidity by eliminating the temperature disturbance entirely and not focused on reducing the relative humidity recovery time [9]- [11]; (2) the method of decoupling is partitioned into two parts: using the fuzzy logic algorithm to build the desired decoupling relationship, and the decoupling model is inserted into an existing pair of independent PID controllers for temperature and relative humidity. The prior works on decoupling based on modifying the entire system as a whole or changing the overall control algorithm [12]- [15]; and (3) the data were collected from a large-scale operating meat drying room and a large number of previous works on decoupling used simulation or results from small test set up to characterize performance.…”

Section: Introductionmentioning

confidence: 99%

Decoupling control and energy efficient system for meat drying processing based on fuzzy-PID approaches

Zhang

Yang

et al. 2015

2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation

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The paper presents a fuzzy PID decoupling control method for a drying room system in meat manufacturing. This decoupling algorithm aims to solve the issue of the coupling between temperature and relative humidity in meat drying rooms. The current traditional PID control algorithm is not performed better control accuracy to the systems of temperature and relative humidity. Therefore, the proposed decoupling control with the fuzzy PID algorithm is developed to solve the issue of the coupling. The result of simulated decoupling data for the meat drying system is presented better control accuracy based on the fuzzy PID decoupling algorithm. The coupling effects of temperature and relative humidity are also discussed in details with the system stability and energy consumption.Keywords-relative humidity; drying process; decoupling control; energy efficient 978-1-4673-7338-8/15/$31.00 c 2015 IEEE

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Section: Introductionmentioning

confidence: 99%

Decoupling control and energy efficient system for meat drying processing based on fuzzy-PID approaches

Zhang

Yang

et al. 2015

2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation

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“…Firstly, it is of simple principle and structure and is easy to implement. Moreover, it can match the requirements of different industrial applications and is of higher robust performance [8][9][10][28][29][30][31].…”

Section: Control Objective and Thermodynamic Modelmentioning

confidence: 99%

“…Attaran et al put forward an energy efficiency optimization method for heating, ventilating, and air conditioning (HVAC) system by using a radial basis function neural network (RBFNN) combined with the epsilon constraint (EC). The method adopted the advanced algorithm of RBFNN for the HVAC system to estimate the residual errors, increased the control signal, and reduced the error results [8]. Trafczynski et al proposed PID-controlled heat exchangers to analyze the influence of fouling on the dynamic behavior and proved that, at changed thermal resistance of fouling, the values of tuning parameters should be changed [9].…”

Section: Introductionmentioning

confidence: 99%

A Self-Tuning Proportional-Integral-Derivative-Based Temperature Control Method for Draw-Texturing-Yarn Machine

Rong

Chen

2017

Mathematical Problems in Engineering

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Owing to the fast time-varying characteristics, the temperature control for draw-texturing-yarn (DTY) machine has higher technical difficulties and results in challenges for system energy optimization. To address the matter, a self-tuning proportionalintegral-derivative-(ST-PID-) based temperature control method is proposed. Referring to the technical procedures of DTY machine, a thermodynamic model is set up. Then, a ST-PID minimum phase control system is constructed by the pole-point placement method. Subsequently, an artificial neural network based forgetting factor searching (ANN-FFS) algorithm is developed to optimize the system parameter identification. The numerical cases show that the proposed ANN-FFS algorithm can improve the parameter identification process, and the average identifying efficiency ( > 15) can increase by more than 50%; compared with the fuzzy PID controller, the proposed ST-PID method can increase the control accuracy nearly 3 times for the static temperature ascending. The experimental results prove that the proposed ST-PID method has better abilities of characteristics tracing and antiinterference and can restrain the temperature fluctuation caused by objective switching and the factual control accuracy reaches 3 times that of fuzzy PID method.

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“…Judging from the progress of theoretical research at present, although there are many types of research on advanced HVAC system control technology [14,15], the control modes of building HVAC systems presently have a great limitation both in control methods and controlled parameters. From the control method point of view, PID control [16][17][18] is a kind of negative feedback control system, which is widely used in the control of HVAC systems of public buildings by using the proportional integral and differential method to calculate the control amount according to the system deviation. For controlled objects with inherent nonlinearity and hysteresis characteristics [19] such as the HVAC system, it is difficult to obtain an ideal PID control effect due to the uncertainty and time-varying nature of external environmental disturbances.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System

Zhao

2020

Energies

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The energy consumption of air-conditioning systems is a major part of energy consumption in buildings. Optimal control strategies have been increasingly developed in building heating, ventilation, and air-conditioning (HVAC) systems. In this paper, a load forecast fuzzy (LFF) control strategy was proposed. The predictive load based on the SVM method was used as the input parameter of the fuzzy controller to perform feedforward fuzzy control on the HVAC system. This control method was considered as an effective way to reduce energy consumption while ensuring indoor comfort, which can solve the problem of hysteresis and inaccuracy in building HVAC systems by controlling the HVAC system in advance. The case study was conducted on a ground source heat pump system in Tianjin University to validate the proposed control strategy. In addition, the advantages of the LFF control strategy were verified by comparing with two feedback control strategies, which are the supply water temperature (SWT) control strategy and the room temperature fuzzy (RTF) control strategy. Results show that the proposed LFF control strategy is capable not only to ensure the minimum indoor temperature fluctuations but also decrease the total energy consumption.

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A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system

Cited by 62 publications

References 16 publications

Decoupling control and energy efficient system for meat drying processing based on fuzzy-PID approaches

Decoupling control and energy efficient system for meat drying processing based on fuzzy-PID approaches

A Self-Tuning Proportional-Integral-Derivative-Based Temperature Control Method for Draw-Texturing-Yarn Machine

A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System

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