Optimization of Water Level Control Systems Using ANFIS and Fuzzy-PID Model

Muhlasin,; Budiman, .; Ali, Machrus; Parwanti, Asnun; Firdaus, Aji Akbar; Iswinarti,

doi:10.1109/icvee50212.2020.9243229

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…In order to simulate the delay problem that may be encountered in the real situation, a transmission delay module with a delay of 30 seconds is added to the system model. Figure 5 models a complete fuzzy PID system [12][13].…”

Section: Resultsmentioning

confidence: 99%

Enhancing water level system based on Fuzzy PID control

Jiang

2024

J. Phys.: Conf. Ser.

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In specific fields such as industrial manufacturing, the water level control of containers such as water tanks is an important step. To enhance the liquid level control with its efficiency and stability, a fuzzy PID liquid level control system integrates the principles of PID control theory and fuzzy control theory, and Simulink simulation program is used to compare it with traditional PID control in this paper. The liquid level control system will adjust the water flow speed according to the current situation in the tank by using PID control, such as the liquid level height, the rate of change of the height, so that the liquid level height can reach the desired value in the shortest time. The fuzzy control will adjust the output result of the former more precisely, making the whole system more stable and reliable. The results demonstrate that, within the context of a standard water tank system, the response time of the fuzzy PID controller is more rapid compared to the traditional PID controller, with a reduction in response time from 117 seconds to 98 seconds, an improvement of more than 10 percent, but it is consistent with the latter in terms of regulation time, overshoot and steady-state error.

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Section: Resultsmentioning

confidence: 99%

Enhancing water level system based on Fuzzy PID control

Jiang

2024

J. Phys.: Conf. Ser.

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“…In this paper, optimization is carried out to find the value of the PID parameter so that the PID can produce the smallest overshot and undershot. The PSO parametersican be seen in Table 1[12] [13]. Best (overall best position), velocity (speed) determines the direction of movement the position carried out in each iteration, inertial weights are used to control the impact of speed changes, acceleration coefficients (controlling the movement of one iteration can be determined independently [10].…”

Section: Conventional Pid Modelmentioning

confidence: 99%

Hybrid System PSO-ANFIS for Optimization of The Water Level in The Tank

Yunus,

Siswanto,

Wahyudi

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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The speed and pressure of the water flow are determined by the height and volume of the water. The speed of the water flow in the actuator is determined by the use of this flow sensor system. A good tank-based water flow control model should be developed. At a certain point, the actuator stabilizes the rate of water production per minute. Therefore, it is necessary to develop an automatic and precise control technique. Many Artificial Intelligence (AI) methods are used in system optimization. Among them are Particle Swarm Optimization (PSO), Neural Network (NN), Fuzzy Inference System (FIS), and ANFIS. Adaptive Neuro Fuzzy Inference System (ANFIS) is a combination of NN and FIS. In this study, the PSO method was combined with ANFIS. This hybrid method produces better optimization compared to the previous method. The best water level control simulation results are PSO-ANFIS with an overshot of 0.572 pu, undershot of 0.563 pu, and flow output overshot of 0.008 pu, undershot of 0.009 pu.

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“…Intelligent control based on Artificial Intelligence has been developed a lot to improve the control system. Many control systems have been used; including conventional control systems, PID controls, Fuzzy controls, Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and other types of controllers [7] [8]. PID controllers with artificial intelligence tuning have been used in Imperialist Competitive Algorithm (ICA), Particle Swarm Optimization (PSO) Method, Firefly Algorithm (FA) Method, Imperialist Competitive Algorithm (ICA) Method, Ant Colony Optimization (ACO) Method, and Bat Algorithm (BA).…”

Section: Introductionmentioning

confidence: 99%

PID Controller Design for Heating Furnace Temperature Based on Bat Algorithm (BA)

Budiman

Ali²

2023

JEEMECS

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A furnace is a tool for heating materials, oil, and others. Furnaces use gas, coal, and oil as fuel. Temperature is the main parameter that needs to be controlled to remain stable, and precise and improve fuel efficiency. As technology develops, several methods can be used to control temperatures that are more reliable than conventional controls. The technology is a Proportional Integral Derivative (PID) controller. PID controllers have been proven and widely used in the industry, but determining the gain of the PID value is still not accurate. This can affect temperature stability, and slow response to reach the desired set point. Therefore, it is necessary to optimize the control system. Optimizing by looking for a better PID gain value with the artificial intelligence tuning method. The Artificial Intelligence method is Bat Algorithm(BA). The simulation results and discussion show that the best design is PID-BA with an overshoot of 0.0429, non undershoot, and the fastest settling time of 16.9 seconds

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Optimization of Water Level Control Systems Using ANFIS and Fuzzy-PID Model

Cited by 13 publications

References 15 publications

Enhancing water level system based on Fuzzy PID control

Enhancing water level system based on Fuzzy PID control

Hybrid System PSO-ANFIS for Optimization of The Water Level in The Tank

PID Controller Design for Heating Furnace Temperature Based on Bat Algorithm (BA)

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