Digital command feedforward and PID temperature control for PET stretch blow molding machines

Tsai, Ching‐Chih; Tsai, Cheng‐Chih

doi:10.1109/ascc.2017.8287329

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Proportional-integral-derivative (PID) controllers have gained widespread adoption in various industrial control applications [4,5]. Nonetheless, the most dynamical model of a control system has less parameter uncertainty, which can potentially degrade system performance and even render the closed-loop control system unstable.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Temperature Control of a Stretch Blow Molding Machine Using Deep Reinforcement Learning

Hsieh

2023

Processes

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Stretch blow molding serves as the primary technique employed in the production of polyethylene terephthalate (PET) bottles. Typically, a stretch blow molding machine consists of various components, including a preform infeed system, transfer system, heating system, molding system, bottle discharge system, etc. Of particular significance is the temperature control within the heating system, which significantly influences the quality of PET bottles, especially when confronted with environmental temperature changes between morning and evening during certain seasons. The on-site operators of the stretch blow molding machine often need to adjust the infrared heating lamps in the heating system several times. The adjustment process heavily relies on the personnel’s experience, causing a production challenge for bottle manufacturers. Therefore, this paper takes the heating system of the stretch blow molding machine as the object and uses the deep reinforcement learning method to develop an intelligent approach for adjusting temperature control parameters. The proposed approach aims to address issues such as the interference of environmental temperature changes and the aging variation of infrared heating lamps. Experimental results demonstrate that the proposed approach achieves automatic adjustment of temperature control parameters during the heating process, effectively mitigating the influence of environmental temperature changes and ensuring stable control of preform surface temperature within ±2 ℃ of the target temperature.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent Temperature Control of a Stretch Blow Molding Machine Using Deep Reinforcement Learning

Hsieh

2023

Processes

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“…Feed-forward control is one of the basic types of control systems. The feed-forward control is used to eliminate and reduce the effect of disturbance for applications that need very fast and sensitive response (Irfan and Abdurraqeeb, 2016; Kosalay and Ozkaya, 2017; Pratilastiarso et al, 2017; Sveko and Kusi, 2015; Tsai and Tsai, 2017). Differential flatness approach (Liu et al, 2017; Poustini et al, 2015) is a mathematical method that is used to calculate the required voltage to control the piezoelectric actuator (Pahlevaninezhad et al, 2012).…”

Section: Differential Flatness Approachmentioning

confidence: 99%

Verification of the feed-forward control for a piezoelectric actuator using differential flatness approach

Alzoubi

Alboon

Alqudah

2018

Journal of Intelligent Material Systems and Structures

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In the last decade, the applications of nano- and micro-technology are widely used in many fields. In the modern mobile devices, such as digital cameras, there is an increased demand to achieve fast and precise positioning for some parts such as the recording sensor. Therefore, a smart material (piezoelectric) is used to achieve this requirement. This article discusses the feed-forward control for a piezoelectric actuator using differential flatness approach. The differential flatness approach is used to calculate the required voltage to control the piezoelectric actuator movement. The control voltage will be applied to the real actuator. The simulation and experimental results are compared for the actuator. The aim of this article is to verify the feed-forward control for second eigenfrequency using the differential flatness approach for the piezoelectric actuator.

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Adaptive Predictive PID Control Using Fuzzy Wavelet Neural Networks for Nonlinear Discrete-Time Time-Delay Systems

Tsai

Tai

Chang

et al. 2017

Int. J. Fuzzy Syst.

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Digital command feedforward and PID temperature control for PET stretch blow molding machines

Cited by 5 publications

References 4 publications

Intelligent Temperature Control of a Stretch Blow Molding Machine Using Deep Reinforcement Learning

Intelligent Temperature Control of a Stretch Blow Molding Machine Using Deep Reinforcement Learning

Verification of the feed-forward control for a piezoelectric actuator using differential flatness approach

Adaptive Predictive PID Control Using Fuzzy Wavelet Neural Networks for Nonlinear Discrete-Time Time-Delay Systems

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