Liquid level control with different control methods based on Matlab/Simulink and Arduino for the control systems lesson

Yumurtacı, Mehmet; Verim, Ozgur

doi:10.35860/iarej.750664

Cited by 8 publications

(4 citation statements)

References 7 publications

(7 reference statements)

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“…In a recent study, Yumurtaci et al has developed a liquid-level control based on Matlab Simulink with PID system, the result is possible to control the liquid level [1]. Besides, Dey et al and Azmi et al were studied the design and implementation liquid-level control system with fuzzy control.…”

Section: Introductionmentioning

confidence: 99%

IMPLEMENTATION OF AN ECONOMICAL IoT-BASED LIQUID TANK CONTROL SYSTEM TO SUPPORT ONLINE PROCESS CONTROL PRACTICUM

Anugraha,

Juwari,

Renanto

et al. 2024

JCTM

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Due to the pandemic era, many courses in higher education were conducted online to minimize the risk of disease transmission. For several engineering courses that contain practical material, an appropriate method is required to deliver practicum learning materials online to maintain the quality of these courses. Therefore, in this paper, the implementation of an economical IoT-based (Internet of Things) liquid tank control system was introduced to support the online practicum class. The tank system design has a height of 120 cm and a diameter of 50 cm. The tank features a valve, flowmeter, and an 18 LPM water pump. The control systems, consisting of Arduino Uno, an ultrasonic sensor, and a stepper motor, was run on the MATLAB Simulink application. IoT systems can be deployed using the TeamViewer app. After running the simulation, it was found that the lower setpoint change did not make significant changes in the process and produced the same curve shape as the steady state. The curves at steady state and during processing are not much different for the disturbance change. The error can be calculated using IAE and ITAE. The performances of the control system with IAE and ITAE values are 1.460 and 915.122 for setpoint change, respectively. For disturbance change, the IAE and ITAE values are 0.877 and 490.446, respectively. This system can help students better understand the industry 4.0 concept and allow them do practical work effectively from home at a low cost.

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Section: Introductionmentioning

confidence: 99%

IMPLEMENTATION OF AN ECONOMICAL IoT-BASED LIQUID TANK CONTROL SYSTEM TO SUPPORT ONLINE PROCESS CONTROL PRACTICUM

Anugraha,

Juwari,

Renanto

et al. 2024

JCTM

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show abstract

“…Proportional integral derivative (PID) tuning parameters are found by fuzzy logic to have a higher motion precision. 11,18 Fuzzy logic 6,19,20 and neural networks 9 are usually used to solve the task weights in real time. Fuzzy logic, as a higher-level supervisor, can possess applicability instead of constant.…”

Section: Introductionmentioning

confidence: 99%

Combined dynamics and kinematics networked fuzzy task priority motion planning for underwater vehicle-manipulator systems

Wei

Hou

Luo

et al. 2021

International Journal of Advanced Robotic Systems

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The underwater vehicle-manipulator systems (UVMS) face significant challenges in trajectory tracking and motion planning because of external disturbance (current and payload) and kinematic redundancy. Former algorithms can finish the tracking of end-effector (EE) and free of singularity redundancy solution alone. However, only a few analytical studies have been conducted on coordinated motion planning of UVMS considering the dynamics controller. This article introduces a combined dynamics and kinematics networked fuzzy task priority motion planning method to solve the above problems. It avoids the assumption of perfect dynamic control. Firstly, to eliminate the kinematics error, a dynamic transformation method from joint space to task space is proposed. Without chattering, an outer loop sliding mode controller is designed for tracking EE’s trajectory. Further, to ensure the underwater vehicle’ posture stability and joint constraint, a task priority frame with kinematics error is used to planning the coordinated motion of UVMS, in which the posture and joint limits map into the null space of prioritized tasks, and weight gains are adopted to guarantee orthogonality of secondary tasks. On top of that, the gain weighted are updated by the networked fuzzy logic. The proposed algorithm achieves better coordinated motion planning and tracking performance. Effectiveness is validated by numerical simulation.

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“…Since the parameters of the industrial plant change frequently, maintaining the water level at a specific point is difficult (Shahid et al, 2016). For this reason, the liquid level control system is considered a complex system that traditional control methods are insufficient to deal with (Yumurtacı and Verim, 2020). Thus, it is necessary to employ an embedded control system capable of adjusting the liquid level at its set point and which is not affected by the variable disturbances in the plant (Thakur et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Pid Controller and Fuzzy Logic Controller for Water Level Control With Applying Time Delay

Omar

2021

Konya Journal of Engineering Sciences

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Liquid level control is a system often found in most industrial facilities. Usually, embedded control systems are used, as these systems play an increasingly important role in control engineering. Time delay is one of the most important parameters which affects the control systems performance. This study aims to design a water level control system in MATLAB/ Simulink environment using the proportional integral derivative (PID) controller and Fuzzy Logic (FL) controller and then to investigate the effect of the time delay on their performance. The two control systems were tested and compared in terms of performance, with and without applying a time delay. The study highlights the application of two types of delays (distributed time delay and discrete-time delay) to the output of the built-in control system and discusses the results. The results showed that the FL controller is better than the PID controller in terms of stability and performance. Although the rise time of both controllers was almost the same, the FL controller showed higher performance in terms of overshoot and settling time. While the overshoot for the PID controller was 9.33 %, it did not appear when using the FL controller. Moreover, the settling time of the PID controller was 10 seconds, while the FL controller took only 8.6 seconds to achieve stabilization. As a result, the FL controller has demonstrated the ability to eliminate the overshoot problem that appears when using PID controllers and stabilize faster.

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Liquid level control with different control methods based on Matlab/Simulink and Arduino for the control systems lesson

Cited by 8 publications

References 7 publications

IMPLEMENTATION OF AN ECONOMICAL IoT-BASED LIQUID TANK CONTROL SYSTEM TO SUPPORT ONLINE PROCESS CONTROL PRACTICUM

IMPLEMENTATION OF AN ECONOMICAL IoT-BASED LIQUID TANK CONTROL SYSTEM TO SUPPORT ONLINE PROCESS CONTROL PRACTICUM

Combined dynamics and kinematics networked fuzzy task priority motion planning for underwater vehicle-manipulator systems

Performance Comparison of Pid Controller and Fuzzy Logic Controller for Water Level Control With Applying Time Delay

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