Research on Temperature Control of Fuel-Cell Cooling System Based on Variable Domain Fuzzy PID

Jia, Yuru; Zhang, Ruiliang; Liu, Xue; Zhang, Tao; Fan, Zhengwu

doi:10.3390/pr10030534

Cited by 16 publications

(6 citation statements)

References 10 publications

(14 reference statements)

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“…𝑇 𝑖 is the integration time constant and 𝑇 𝑑 is the differential time constant. Equation ( 1) is a continuous expression of the PID control algorithm, and the system output collected by the controller through the timing is a discrete value, so Equation ( 1) must be discretized [17]. Assuming that the sampling period is T and a total of k samples are taken, it can be obtained:…”

Section: Fuzzy Pid Controller Designmentioning

confidence: 99%

“…However, despite the many advantages of the variabledomain fuzzy PID controller, the variable-domain fuzzy PID controller requires a more complex computational process, especially in real-time control systems, where the selection of controller parameters has a great impact on the system performance, and inappropriate parameter settings may lead to a degradation of the system performance. In some cases, variable-domain fuzzy PID controllers may cause overregulation phenomena in the system, especially when the system dynamics change rapidly, which may lead to system instability [17].…”

Section: Variable-domain Fuzzy Pid Controller Designmentioning

confidence: 99%

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BA-Optimized Variable Domain Fuzzy PID Control Algorithm for Water and Fertilizer Ratio Control System in Cotton Field

Guo,

Zhu,

Zhao

et al. 2024

Processes

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Due to the time-varying, hysteresis and nonlinear characteristics of fertilizer concentration control in the water–fertilizer ratio control system, common control algorithms such as PID and fuzzy PID cannot obtain the expected control effect. In order to accurately control the cotton field water–fertilizer ratio regulation system drip irrigation process of the water–fertilizer ratio that will be controlled within a reasonable range, it is needed to design a bat-optimized variable-domain fuzzy PID water–fertilizer ratio control strategy, through the use of bat algorithm to find out the optimal expansion factor and the best domain of the current conditions, and then according to the changes in working conditions to automatically adjust the fuzzy control of the domain, through the control of the valve openings to change the fertilizer pump back to the amount of water. Realize the fast and precise control of fertilizer concentration in the water–fertilizer ratio control system. Comparative tests were conducted to verify the traditional PID, fuzzy PID, variable domain fuzzy PID and bat-optimized variable-domain fuzzy PID control algorithms. The results show that: if the water–fertilizer ratio is adjusted to 50:1 from the startup, the adjustment time required to reach the target water–fertilizer ratio under the bat-optimized variable-domain fuzzy PID control is 15.29 s, and the maximum overshooting amount is 16.28%, which is a smaller adjustment time and overshooting amount; if the water–fertilizer ratio is adjusted to 40:1 from 50:1, the advantages of bat-optimized variable-domain fuzzy PID are more obvious, with the best balance of response speed, overshooting amount and optimal control effect. In terms of response speed, overshooting amount and regulation time, the optimal balance is achieved, showing the optimal control effect. It is proved that the performance of the water–fertilizer ratio regulation system in cotton field under bat-optimized variable-domain fuzzy PID control designed in this paper can meet the actual production requirements, and these findings can help to develop precise irrigation technology for cotton cultivation under drip irrigation conditions.

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Section: Fuzzy Pid Controller Designmentioning

confidence: 99%

Section: Variable-domain Fuzzy Pid Controller Designmentioning

confidence: 99%

BA-Optimized Variable Domain Fuzzy PID Control Algorithm for Water and Fertilizer Ratio Control System in Cotton Field

Guo,

Zhu,

Zhao

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…The basic methods for building dynamic mathematical models are divided into mechanism modeling, process identification and parameter estimation. Wang et al used the system identification tool in MATLAB for parameter identification to obtain the model transfer function, constructed the greenhouse ambient temperature adaptive model and water‐cooled heat dissipation semi‐mechanistic model (Jia et al, 2022; Wang & Wang, 2020). Based on the identification of the temperature and pressure control system, continuous debugging was carried out using process models module, and the consistency of the temperature and pressure models reached 99.35% and 95.55% (Xu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy‐PID controller based on improved LFPSO for temperature and humidity control in a CA ripening system

Zhou,

Wu,

Zheng

et al. 2024

J Food Process Engineering

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Temperature and humidity as the key factors affecting the storage and ripening of fruits and vegetables directly determine the quality of fruits and vegetables. In this paper, a temperature and humidity model was constructed based on an integrated device of controlled atmosphere storage and ripening. Aiming at the shortcomings of the temperature and humidity model such as large inertia, nonlinearity, and model uncertainty, a fuzzy‐PID controller based on the improved Lévy flight particle swarm algorithm (LFPSO) is proposed. Initially, a mathematical model of temperature and humidity is developed through mechanism research and parameter estimation. Subsequently, a temperature and humidity fuzzy‐PID control strategy is proposed for regulating temperature and humidity. An improved LFPSO is then introduced to optimize the key parameters of the fuzzy‐PID controller, such as the quantization factor and the scale factor. The superiority of the improved LFPSO algorithm is verified by comparing the test functions. Finally, the improved LFPSO‐fuzzy‐PID controller, fuzzy‐PID controller, and Smith‐PID controller are applied to the simulation model for comparison using the MATLAB Simulink simulation platform. The results show that the improved LFPSO‐fuzzy‐PID control algorithm has a good control effect in the temperature and humidity simulation system of controlled atmosphere (CA) ripening container, which provides a reference for solving the optimization problem of actual engineering design.Practical applicationsIn order to reduce the postharvest cold chain losses of fruits and vegetables, this paper proposes a containerized style device that combines fruits and vegetables storage and ripening for simplifying the losses caused by transshipment at cold chain nodes. Since temperature and humidity play a key role in fruits and vegetables storage and ripening, which directly affect the product quality, this paper establishes a complete mathematical model of CA ripening temperature and humidity system by combining mechanism modeling and parameter identification. In order to keep the temperature and humidity within the ideal range, Smith‐PID controller, fuzzy‐PID controller, and fuzzy‐PID control method based on improved Lévy flight particle swarm optimization are proposed to regulate the air conditioner and humidifier. The performance of three different types of controllers was tested on the MATLAB. The simulation results demonstrate that the improved LFPSO‐fuzzy‐PID controller is superior and more effective than Smith‐PID and fuzzy‐PID controllers.

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“…It does not need to spend a lot of effort and time to update control rules of traditional fuzzy controllers, or update control tables of fuzzy controller with modifiable factors when it adapts to a new modifiable factor. In addition, the quantization factor of fuzzification is fixed in the development process of traditional fuzzy controller and fuzzy controller with modifiable factors [40], [41], so the control parameter resolution is not enough which leads to insufficient control precision and less robustness, so the neural network technology is adopted to optimize the compromise factor of proposed controller.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Neural Network Controller Using Compromise Features for Timeliness Problem

Wang¹,

Dong

Huangfu

et al. 2023

IEEE Access

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W HEN the control rules of traditional fuzzy controller are determined, it comes to be time-consuming and laborious to adjust for different usage conditions. Therefore, the timeliness cannot be guaranteed to solve the timeliness problem, and a fuzzy controller with modifiable factors is designed. While the entire control table is affected by the modifiable factors selection table, all previous control parameters need to be reset. In light of above problems, this paper firstly proposes new fuzzy controller design methods, which retain strengths of traditional controller and controller with modifiable factors. It effectively overcomes the shortcomings of the two controllers mentioned above, and only need to adjust the compromise factor for different working conditions of proposed controller, which is more convenient and efficient. Secondly, the proposed fuzzy controller also adopts a four-layer neural network to optimize the control rules of compromise to improve control precision and system robustness. Finally, the excellent characteristics of proposed controller are verified through simulation research, and the simulation result proves the proposed fuzzy controller has the advantages of higher control precision and smaller transition.

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Research on Temperature Control of Fuel-Cell Cooling System Based on Variable Domain Fuzzy PID

Cited by 16 publications

References 10 publications

BA-Optimized Variable Domain Fuzzy PID Control Algorithm for Water and Fertilizer Ratio Control System in Cotton Field

BA-Optimized Variable Domain Fuzzy PID Control Algorithm for Water and Fertilizer Ratio Control System in Cotton Field

Fuzzy‐PID controller based on improved LFPSO for temperature and humidity control in a CA ripening system

A Fuzzy Neural Network Controller Using Compromise Features for Timeliness Problem

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