A Self-Tuning Proportional-Integral-Derivative-Based Temperature Control Method for Draw-Texturing-Yarn Machine
Rong Song and
Shuting Chen
Mathematical Problems in Engineering, 2017, vol. 2017, 1-17
Abstract:
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 proportional-integral-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 ( ) 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 anti-interference and can restrain the temperature fluctuation caused by objective switching and the factual control accuracy reaches 3 times that of fuzzy PID method.
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1864321
DOI: 10.1155/2017/1864321
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