Performance Evaluation of Balancing Bicopter using P, PI, and PID Controller

Apriaskar, Esa; Fahmizal, Fahmizal; Salim, Nur Azis; Prastiyanto, Dhidik

doi:10.15294/jte.v11i2.23032

Cited by 12 publications

(12 citation statements)

References 18 publications

(19 reference statements)

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“…It also happens to proportional and integral parameters. Therefore, a tuning method, such as Ziegler Nichols, as the most popular one, is usually utilized to obtain the appropriate value for controller parameter [24]. However, in this research, genetic algorithm is used to determine the parameter.…”

Section: Figure 2 Discrete Pid Controller Block Diagram In Matlab Simulinkmentioning

confidence: 99%

Microwave heating control system using genetic algorithm-based PID controller

Apriaskar

Prastiyanto

Malik

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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The superiorities of microwave heating technology have attracted many researchers for further development. Designing a satisfying temperature control system for the microwave system is one of them. To confirm that the controller can work with good system performance, the adjustment of controller parameters is important. Most previous research about microwave control systems did not focus on the tuning problem which is presented in this work. This paper provides the use of a genetic algorithm to adjust the best parameters for a PID controller. A microwave heating system with a single microwave power input and a single output temperature probe which is identified using the ARX model is examined. Evaluation of the proposed controller was conducted in the simulation environment using MATLAB. The result shows that the genetic algorithm can obtain the most optimized parameter for PID controller which can follow a certain heating pattern. The parameters for Kp, Ki, and Kd are 998.94, 79.42, and 79.78, respectively. It can minimize the value of integral absolute error for the given setpoint to 446.11.

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Section: Figure 2 Discrete Pid Controller Block Diagram In Matlab Simulinkmentioning

confidence: 99%

Microwave heating control system using genetic algorithm-based PID controller

Apriaskar

Prastiyanto

Malik

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…To build a balancing bicopter with good and robust performance, it requires a controller that can speed up the response without enlarging the overshoot. Despite being a favourite controller, PID could not outperform simpler PI controller for step response evaluation of bicopter as described in [21]. It is true that both controllers could eliminate the steady state error, but the former had the higher overshoot and longer settling time.…”

Section: Introductionmentioning

confidence: 98%

“…Some parameters such as settling time, maximum overshoot, and steady state error, are required to identify whether the system is behaving smoothly or not [22]. Since settling time and overshoot become two most important things based on step response analysis in [21], this work also proposes the use of those two criterions inside the objective function of the GA and compared it with the use of integral absolute error (IAE) of the controlled system performance. The evaluation of those methods was carried out using a balancing bicopter model with respect to roll movement.…”

Section: Introductionmentioning

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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“…The number of rotors to generate thrust is generally more than one. For example, a bicopter in [8], [9] has two rotors of motor that are controlled to fly properly [10]. Therefore, designing a dual motor system model is exciting research with a high difficulty related to the multiple inputs and outputs [11].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Two System Models for a MIMO System to Hover the Bicopter Unmanned Aerial Vehicle

Prakosa

Agmal

Kurniawan

et al. 2022

Jurnal Teknik Elektro

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Building unmanned aerial vehicle (UAV) control system models are highly challenging due to multiple inputs and multiple outputs (MIMO). Not only does it have various angular position outputs such as roll, yaw, and pitch, but also flight control has more than one input; for instance, a bicopter has dual rotors. More rotors have more complex model. The hover condition has a zero resultant force which can be utilized to design a system model. On the other hand, an attractive identification system method is applied to develop the design. This research aims to evaluate the performance of two MIMO design on bicopter between methods based on the hover principle and identification technique. Experimental validation by employing bicopter simulator is an excellent strategy to fulfil this purpose. The results of the investigation of the experiment showed that the identification model was more accurate than the hover design, particularly regarding the overshot phenomenon and error. In addition, the hover principle tended to build ideal model because it did not include the dynamic, uncertainty and nonlinear conditions in aeroplane control design. Although the identification system was complicated because it previously needed to measure the input and output values, it performed closer to the actual experiment. It performed more satisfactory overshoot values compared with the experimental validation than the hover model by 1°, 3°, and 8° in roll, pitch, and yaw angles, respectively.

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Performance Evaluation of Balancing Bicopter using P, PI, and PID Controller

Cited by 12 publications

References 18 publications

Microwave heating control system using genetic algorithm-based PID controller

Microwave heating control system using genetic algorithm-based PID controller

Multi-criteria Genetic Algorithm Optimization Approach for Balancing Bicopter Control

Performance Evaluation of Two System Models for a MIMO System to Hover the Bicopter Unmanned Aerial Vehicle

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