Microwave heating control system using genetic algorithm-based PID controller

Apriaskar, Esa; Prastiyanto, Dhidik; Malik, Muchamad; Ramadhan, A E; Destanto, R; Abdullah@Idris, Hasnain; Osman, Muhammad Khusairi

doi:10.1088/1755-1315/969/1/012060

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…More complete numerical results associated with Figure 4 can be shown in table 2. Since there is not any significant difference between MC-GA and MCIAE-GA, standard deviation that GA obtain after several running tests may become the next criteria to assess and highlight the repeatability of the algorithm [24]. As illustrated in table 3, the standard deviation of MC-GA is lower than that of MCIAE-GA after performing twenty running tests for each method.…”

Section: Figure 5 Objective Function Of Best Individual At Each Gener...mentioning

confidence: 99%

Multi-criteria Genetic Algorithm Optimization Approach for Balancing Bicopter Control

Apriaskar,

Prastiyanto,

Manaf

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Efforts to find the best controller for stabilizing a flying vehicle have been widely developed in recent years. This work aims to join the development track by exploring the use of genetic algorithm (GA) to find the most optimum controller parameter considering settling time and overshoot criteria. Roll attitude control of a balancing bicopter was chosen as the applied plant to confirm the designed method. The controller structure applied in this research is an ideal proportional-integral (PI) controller. Two equations considering the highlighted criteria for the objective function of the GA were developed with one of them involving integral absolute error (IAE) (MCIAE-GA), and the other one does not (MC-GA). Two GAs with both equations were compared to ziegler-nichols and the one with a standalone IAE (IAE-GA). The test results confirm that the proposed MC-GA and MCIAE-GA outperform the comparative controllers at their best performance by keeping the settling time as small as possible without overshooting. Despite the latter showing a slightly faster settling time, the former has better repeatability given a smaller standard deviation for Kp and Ki parameters with a mere 0.0820 and 0.0980, respectively.

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Section: Figure 5 Objective Function Of Best Individual At Each Gener...mentioning

confidence: 99%

Multi-criteria Genetic Algorithm Optimization Approach for Balancing Bicopter Control

Apriaskar,

Prastiyanto,

Manaf

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…Most previous techniques concentrate on the quality of the initial population seeding, such as random initialization, nearest neighbor, and K-means clustering [49][50][51]. Some researchers used GA to optimize PID, for instance, Guan and Jau [52], Swati K. et al [53], Tanvir ert et al [54], Gun B. S. [55], and Apriaskar et al [56]. On the other side, PSO is one of the EA's that was developed by James Kennedy and Russell Eberhart in 1995, for solving practical issues related to optimization, inspired by the behavior of living things.…”

Section: Introductionmentioning

confidence: 99%

Perspective Chapter: Fabulous Design Speed Industrial Robotic Arm

Alkhafaji¹

2023

Human-Robot Interaction - Perspectives and Applications

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This chapter focuses on the design of a handling 5 Degree of freedom (DOF) robot arm model for industrial application. Optimal trajectory planning of industrial robots in the assembly line is a key topic to boost productivity in a variety of manufacturing activities. The aim is to improve the speed performance using multi techniques starting from estimating the transfer function of each manipulated joint, then designing the controller for each DOF reached to modeling arm motion. The designed model has been developed the structural design and testing motion characteristics by using SolidWorks and Simscape toolbox. To enhance the speed performance, it is proposed a High-Speed Proportional Integral Derivative controller (HSPID) based on an improved GA. The comparison response time between uncontrolled and controlled systems proves that the proposed controller produced extreme reduction responses to be measured within the Microsecond unit. Based on trajectory motion, the efficiency of the proposed method is assured by case study motions. The innovative design offers the best solution to rise accurate performance and productivity.

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“…By employing proficient controllers, the risks associated with heat non-uniformities can be mitigated [1]. It has been suggested that modeling plant dynamics is instrumental in designing controllers that yield satisfactory performance, as it elucidates the temperature profile of the heated product in relation to allocated microwave power, a valuable insight for control design simulations [7,8].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Microwave Heating Systems with Octagonal Tube Cavities: A Comparative Study of Fuzzy-Based and ARX Approaches

Prastiyanto,

Apriaskar,

Handayani

et al. 2023

PEET

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In the quest to design a robust model for microwave heating systems with symmetrical octagonal tube cavities (MWHSO), a fuzzy-based approach, specifically the Takagi Sugeno Fuzzy Model, was explored to capture the dynamics of the heating process. To achieve this, the mathematical model was adaptively adjusted according to varying input conditions through the utilization of fuzzy logic. Input data were sourced from two magnetrons, with the system outputs derived from measurements acquired from five temperature sensors placed on the heated object. For performance evaluation, the Root Mean Square Error (RMSE) was employed. A comparison was drawn with the autoregressive model with exogenous variable (ARX), a traditional approach wherein the system's mathematical model remains static. Simulation studies were conducted, treating every probe measurement across all dataset validations as distinct cases. It was found that the T-S Fuzzy model surpassed the ARX40 in performance in 33 of the total cases, accounting for 92.49%. The most notable performance of the fuzzy-based approach was observed at a 180-Watt power input, recording an average RMSE of 0.00574 across the five sensors. In contrast, the ARX-based model registered an RMSE of 0.01256. These findings suggest that the fuzzy-based modeling approach presents a compelling alternative for representing the dynamic heating processes within MWHSO.

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Microwave heating control system using genetic algorithm-based PID controller

Cited by 4 publications

References 26 publications

Multi-criteria Genetic Algorithm Optimization Approach for Balancing Bicopter Control

Multi-criteria Genetic Algorithm Optimization Approach for Balancing Bicopter Control

Perspective Chapter: Fabulous Design Speed Industrial Robotic Arm

Modeling of Microwave Heating Systems with Octagonal Tube Cavities: A Comparative Study of Fuzzy-Based and ARX Approaches

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