Parameter identification for pilot behaviour model using the MATLAB system identification toolbox

Jirgl, Miroslav; Obšilová, Lucie; Bořil, Jan; Jalovecky, Rudolf

doi:10.1109/miltechs.2017.7988824

Cited by 6 publications

(3 citation statements)

References 7 publications

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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. This technique develops the identification model in (15).…”

Section: System Identification Methodsmentioning

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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Section: System Identification Methodsmentioning

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

View full text Add to dashboard Cite

show abstract

“…Finally, the validation step that consists of applying several tests to prove the usability of the model. The resulting models' main objectives can be to understand the behavior of the studied system [6], or building the system control loop [7].…”

Section: Experimental Modelsmentioning

confidence: 99%

Microcontroller Based Data Acquisition and System Identification of a DC Servo Motor Using ARX, ARMAX, OE, and BJ Models

Salah-eddine¹,

Sadki²,

Bensassi³

2020

Adv. sci. technol. eng. syst. j.

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This paper is carrying out a practical identification of a DC servomotor from input and output data. The system's step response was obtained from an acquisition card that we have developed using a microcontroller. The data is collected and downloaded to the PC Thought RS232 Protocol to process the identification process using polynomial models (ARX, ARMAX, OE, and BJ). Afterward, a comparison between the resulting models has been made using the validation criterions R² and FPE. The results show that the ARMAX, OE, and BJ models have reproduced a better fit compared to the ARX model.

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“…This makes full flight simulator development very costly, because the data cannot be gathered during scheduled flights (specific experiments are required) and the dedicated flight tests last multiple flight hours [3]. Despite the high cost in aeronautics, the system identification is widely used for airplanes [4,5], rotorcrafts [6,7], projectiles [8,9] or pilot modelling [10,11] as it delivers very accurate mathematical models of these objects.…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Lichota¹

2020

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In this paper, quanizted multisine inputs for a maneuver with simultaneous elevator, aileron and rudder deflections are presented. The inputs were designed for 9 quantization levels. A nonlinear aircraft model was exited with the designed inputs and its stability and control derivatives were identified. Time domain output error method with maximum likelihood principle and a linear aircraft model were used to perform parameter estimation. Visual match and relative standard deviations of the estimates were used to validate the results for each quantization level for clean signals and signals with measurement noise present in the data. The noise was included into both output and input signals. It was shown that it is possible to obtain accurate results when simultaneous flight controls deflections are quantized and noise is present in the data.

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Parameter identification for pilot behaviour model using the MATLAB system identification toolbox

Cited by 6 publications

References 7 publications

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

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

Microcontroller Based Data Acquisition and System Identification of a DC Servo Motor Using ARX, ARMAX, OE, and BJ Models

Bulletin of the Polish Academy of Sciences: Technical Sciences

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