A hybrid of fuzzy and fuzzy self-tuning PID controller for servo electro-hydraulic system

Sinthipsomboon, Kwanchai; Hunsacharoonroj, Issaree; Khedari, Joseph; Po-Ngaen, Watcharin; Pratumsuwan, Pornjit

doi:10.1109/iciea.2011.5975583

Cited by 42 publications

(20 citation statements)

References 5 publications

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“…There is a large volume of published studies describing the implementation of STFPID controllers such in steam temperature regulation [14,27,28], position control of shape memory alloy actuators [26], industrial hydraulic actuator [25,29], gasoline refinery catalytic reformer [30], and plastic injection molding process [31]. The STFPID controllers are more attractive for industrial use since can overcome the limitation of PID controllers such as parameters change and disturbed by unknown facts [29,32]. The STFPID controller has the advantageous over PID controller by significantly reduce rise time and percentage overshoot [14,29].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real Time Steam Temperature Regulation using Self-Tuning Fuzzy PID Controller on Hydrodiffusion Essential Oil Extraction System

Yusoff

Muhammad

Hezri

et al. 2013

International Journal on Smart Sensing and Intelligent Systems

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Section: Introductionmentioning

confidence: 99%

“…The STFPID controllers are more attractive for industrial use since can overcome the limitation of PID controllers such as parameters change and disturbed by unknown facts [29,32]. The STFPID controller has the advantageous over PID controller by significantly reduce rise time and percentage overshoot [14,29].…”

Section: Introductionmentioning

confidence: 99%

Real Time Steam Temperature Regulation using Self-Tuning Fuzzy PID Controller on Hydrodiffusion Essential Oil Extraction System

Yusoff

Muhammad

Hezri

et al. 2013

International Journal on Smart Sensing and Intelligent Systems

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“…The model of the system can perform well for processes that are not precisely defined unlike PID controller. Fuzzy controllers are suitable in achieving a decreased rise time and slight overshoot [12].The structure of the two inputs (error e and error change ∆e) and three output (proportional gain K p , integral gain K i and derivative gain K d ) are designed for the fuzzy rules used in the hybrid fuzzy controller. The structure for the fuzzy logic controller designed for this research paper is two inputs (error e and error change ∆e) and single output of the error and error change.…”

Section: Controller For Underwater Wet Welding Processmentioning

confidence: 99%

Intelligent Control Mechanism for Underwater Wet Welding

Omajene¹

2015

IJMEA

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Abstract:It is important to achieve high quality weld in underwater welding as it is vital to the integrity of the structures used in the offshore environment. Due to the difficulty in ensuring sound welds as it relates to the weld bead geometry, it is important to have a robust control mechanism that can meet this need. This work is aimed at designing a control mechanism for underwater wet welding which can control the welding process to ensure the desired weld bead geometry is achieved. Obtaining optimal bead width, penetration and reinforcement are essential parameters for the desired bead geometry. The method used in this study is the use of a control system that utilizes a combination of fuzzy and PID controller in controlling flux cored arc welding process. The outcome will ensure that optimal weld bead geometry is achieved as welding is being carried out at different water depth in the offshore environment. The result for the hybrid fuzzy-PID gives a satisfactory outcome of overshoot, rise time and steady error. This will lead to a robust welding system for oil and gas companies and other companies that carry out repair welding or construction welding in the offshore.Keyword: Control System, Bead Geometry, Fuzzy Logic, Process Parameter, Underwater Welding BackgroundOwing to the environmental conditions in which structures operate in the offshore, it is important that a high structural integrity is guaranteed. It is evident that structural failures can arise as a result of poor weld quality and other mechanical properties of the loaded structures operating in the offshore. The quality of welds achieved underwater experience a major setback because of the unique feature of the weld metal fast cooling rate and other factors such as the stability of the welding arc, loss of alloying elements and difficulty of a good visibility to weld underwater. This research paper addresses the issue of controlling the welding parameters at different water depth in achieving a desired weld bead geometry that is reasonably satisfactory of the weld quality that can operate in the offshore. A control mechanism which incorporates the design advantages of fuzzy logic control and PID control is implemented in this study. Experimental data to be analyzed in this paper is adopted from the work of Chon L. Tsai et. al. It is a wellknown fact that high cooling rate and hydrogen embrittlement are characteristics of underwater wet welding (UWW). Rapid cooling mechanism and their effects have been studied by Chon L. Tsai and Koichi Masubuchi [1]. The final microstructure of the heat affected zone (HAZ) for a given material is determined by the composition, peak temperature and cooling rate. It is possible to control the weld metal composition, the peak temperature and the cooling rate to yield favorable microstructure. However, it is not possible to control the composition of the HAZ of the parent material. Fast cooling effect of the water environment in UWW results in a martensitic heat affected zone having high hardness and poor notch...

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“…Adaptive PID controller based on parameter optimization with fuzzy inference means that the three gains , , and of PID controller are adjusted online by using fuzzy logic control [27]- [29]. Online tuning gains of PID controller lead to enhance the adaptive performance of PID controller.…”

Section: Adaptive Pid Controller Based On Parameter Optimization Wmentioning

confidence: 99%

Adaptive PID Control of UAV Altitude Dynamics Based on Parameter Optimization with Fuzzy Inference

Sarhan¹,

Beijing²,

Qin³

2016

IJMO

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A hybrid of fuzzy and fuzzy self-tuning PID controller for servo electro-hydraulic system

Cited by 42 publications

References 5 publications

Real Time Steam Temperature Regulation using Self-Tuning Fuzzy PID Controller on Hydrodiffusion Essential Oil Extraction System

Real Time Steam Temperature Regulation using Self-Tuning Fuzzy PID Controller on Hydrodiffusion Essential Oil Extraction System

Intelligent Control Mechanism for Underwater Wet Welding

Adaptive PID Control of UAV Altitude Dynamics Based on Parameter Optimization with Fuzzy Inference

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