Experimental Based Identification Model of Low Fluid Flow Rate Control Systems

Prakosa, Jalu A.; Kurniawan, Edi; Adinanta, Hendra; Suryadi, Suryadi; Purwowibowo, Purwowibowo

doi:10.1109/icramet51080.2020.9298594

Cited by 4 publications

(2 citation statements)

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“…The system identification method provides the optimal design of experiments for an efficient model, which is developed for flow rate control system in [19]. The statistical approach of the system identification, which is applied in [26], estimates the model by minimizing the error between the model output, Ymod, and the measured experimental results, Ymeas.…”

Section: System Identification Methodsmentioning

confidence: 99%

“…For this reason, it is fascinating to study the observation of the design of the bicopter system model employing the hover law. On the other hand, the previous flow rate control study in [19] showed that the identification system offers a model design approach that is no less impressive. But this method firstly requires measured data from both input and output values from a plant.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: System Identification Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%