Temperature Control of a Regasification System for LNG-fuelled Marine Engines Using Nonlinear Control Techniques

So, Gun-Baek; Yi, Hyun-Sik; Son, Yung-Deug; Jin, Gang-Gyoo

doi:10.1007/s12555-018-0179-6

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Recently, research works on nonlinear PID control using nonlinear characteristics to modify the traditional linear PID controller have been actively conducted [9][10][11][12][13][14]. NPID controllers can be various forms depending on the application strategy of nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

“…Han proposed a nonlinear PID controller with nonlinear gain functions combining e(t), ∫e(t)dt and ( ) to achieve a better tracking and better noise rejection [12]. So et al presented two nonlinear PID controllers with a nonlinear function described as a Takagi-Sugeno fuzzy model for a liquefied natural gas (LNG) regasification system [13]. Jin and Son introduced an NPID controller in which a nonlinear gain is coupled in series with the integral action to scale the error and three tuning rules for the FOPTD model based on dimensional analysis and a GA [14].…”

Section: Introductionmentioning

confidence: 99%

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A Self-Tuning NPID Control Method for FOPTD Processes

Ha¹,

Mg²,

Mg³

et al. 2020

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Owing to the time-varying characteristics and nonlinearities of industrial processes, control has higher difficulties and results in challenges for advanced technology. In this paper, a self-tuning controller that includes a nonlinear proportional-integral-derivative (NPID) control function as well as a self-tuning function is proposed for ﬁrst-order plus time delay (FOPTD) process control. The NPID control function is implemented using the nonlinear PID controller whose optimum parameters are adapted by a neural network (NN). The self-tuning function is able to identify the process dynamics using a short period of process behavior and tune NPID parameters based on the identified parameters. The advantage of the proposed method is validated with a set of simulation works on three processes and the comparison results are presented.

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