Wireless Control and Automation of Hot Air Temperature in Oven for Sterilization using Fuzzy PID Controller and Adaptive Smith Predictor

Pamela, D.; Premi, M. S. Godwin

doi:10.1007/s11277-016-3358-x

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…According to (14) and (15), if 0 and 0 are selected adequately, the expected closed-loop pole points will be acquired. Selftuning controller is of better characteristics tracing ability to adapt different control objectives, especially for temperature control.…”

Section: Self-tuning Pid Temperature Control Methods For Dtymentioning

confidence: 99%

“…Zhang et al presented a multiinput multioutput (MIMO) self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multimodule instrument, in which the internal temperature of the instrument converged to a target without the need of a system model, thus making the control robust [13]. Pamela and Godwin Premi presented a hot air temperature controller in oven for sterilization using fuzzy PID controller and adaptive smith predictor, which could maintain the optimum temperature in hot air ovens from a farther distance [14]. Zhang developed an improved PID controller based on predictive functional control (PFC) to test the chamber pressure in an industrial coke furnace.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Self-tuning Pid Temperature Control Methods For Dtymentioning

confidence: 99%

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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“…At present, most manufacturers in our country realize temperature control with PID regulator.PID control algorithm has large time lag and inertia, so it is not suitable for the heat exchanger temperature control requirements of the thermal performance test bench [5][6][7] .With the development of intelligent control algorithm, fuzzy control and Smith estimation are gradually applied to the production process. Fuzzy control does not need to establish a mathematical model.The overshoot and steady-state error of the system response are small;Strong ability to restrain external interference and noise;With high stability, PID parameters can be optimized and adjusted according to different conditions .Smith predictive controller can effectively solve the problem of large time delay and inertia in temperature control.Therefore, this experiment designed a control algorithm combining traditional PID, fuzzy control and Smith's prediction [8][9][10] to improve the response speed, adjustment accuracy and anti-interference performance of the cold water outlet temperature control of the thermal performance test bench.…”

Section: Introductionmentioning

confidence: 99%

Temperature control technology based on Smith fuzzy PID

Gu,

Zhu,

Guan

2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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In order to improve the response speed, adjustment accuracy and anti-interference performance of the outlet temperature of the heat exchanger of the thermal performance test bench. An algorithm combining fuzzy control, Smith prediction and traditional PID control is proposed, and use Matlab/Simulink to simulate the temperature characteristics and antiinterference characteristics of the system. The experimental results show that compared with the traditional PID and Fuzzy PID algorithms, the response speed of Smith fuzzy PID is 57S and 50S faster 8 than that of traditional PID and Smith PID, respectively, and the overshoot is 18% and 16% less than that of traditional PID and Fuzzy PID respectively.Combined with Smith prediction algorithm, the time lag problem is effectively solved. Therefore, the Smith fuzzy PID control method has a faster response to the outlet temperature control of the heat exchanger, a smaller overshoot, and a faster response to interference signals.

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“…In this paper, we adopt micro bimodal sensors that can simultaneously detect spatial airflow and temperature to support a fast, robust, self-adaptive thermal and energy management. However, at the same time, it is not suitable for multi-input multi-output (MIMO) control problems [10][11][12]. The MIMO temperature control problem is complex because of the strong coupling that exists in the controlled object.…”

Section: Introductionmentioning

confidence: 99%

Thermal and Energy Management Based on Bimodal Airflow-Temperature Sensing and Reinforcement Learning

Zhang

Zhu

2018

Energies

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Multi-physical field sensing and machine learning have drawn great attention in various fields such as sensor networks, robotics, energy devices, smart buildings, intelligent system and so on. In this paper, we present a novel efficient method for thermal and energy management based on bimodal airflow-temperature sensing and reinforcement learning, which expedites an exploration process by self-learning and adjusts action policy only through actuators interacting with the environment, being free of the controlled object model and priori experiences. In general, training of reinforcement learning requires a large amount of data iterations, which takes a long time and is not suitable for real-time control. Here, we propose an approach to speed up the learning process by indicating the action adjustment direction. We adopt tailor-designed bimodal sensors to simultaneously detect airflow and temperature field, which provides comprehensive information for reinforcement learning. The proposed thermal and energy management incorporates bimodal parametric sensing with an improved actor-critic algorithm to realize self-learning control. Experiments of thermal and energy management in a multi-module integrated system validate the effectiveness of the proposed methodology, which demonstrate high efficiency, fast response, and good robustness in various control scenarios. The proposed methodology can be widely applied to thermal and energy management of diverse integrated systems.

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Wireless Control and Automation of Hot Air Temperature in Oven for Sterilization using Fuzzy PID Controller and Adaptive Smith Predictor

Cited by 8 publications

References 6 publications

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

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

Temperature control technology based on Smith fuzzy PID

Thermal and Energy Management Based on Bimodal Airflow-Temperature Sensing and Reinforcement Learning

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