System Identification for Quad-rotor Parameters Using Neural Network

Dief, Tarek N.; Yoshida, Shigeo

doi:10.5109/1657380

“…The proposed MLP structure consists of three units of hidden neurons with the sigmoid function. This study [54] indicates that estimated results from MLP are close to the sensor outputs and produced more accurate data for any initial conditions compared with placket model as in Error! Reference source not found..…”

Section: Fig 8-basic Mlp Nn Structurementioning

confidence: 51%

“…Recent research carried out by Dief and Yoshida [54] involves the use of MLP NN trained with the backpropagation algorithm to identify the system parameter of the quadrotor. The proposed MLP structure consists of three units of hidden neurons with the sigmoid function.…”

Section: Fig 8-basic Mlp Nn Structurementioning

confidence: 99%

Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review

Pairan¹,

Shamsudin²,

Zulkafli³

2020

JAMEA

3

0

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A quadcopter is a rotorcraft with a simple mechanical construction. It has the same hovering capability similar to the traditional helicopter, but it is easier to maintain. The quadcopter is hard to control due to its unstable system with highly coupled and non-linear dynamics. In order to design a robust control algorithm, it is crucial to obtain a precise quadrotor flight dynamics through system identification approach. System identification is a method of finding the mathematical model of the dynamics system using the input-output data measurement. Neural network (NN) based system identification is excellent alternative modeling because it reduces development costs and time by avoiding governing equations and large aerodynamic database. NN based system identification has successfully identified the quadcopter dynamics with good accuracy. This paper gives an overview of the characteristic of the quadcopter and presents a comprehensive survey of the modeling techniques used to determine the flight dynamics of a quadrotor with a particular focus on NN based system identification method. The presented research works have been classified into different categories such as the first principle modeling, system identification and implementation of NN based system identification in quadcopter platform. Finally, the paper highlights challenges that need to be addressed in developing efficient NN based system identification model for unmanned quadcopter system.

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“…As a part of future work, we can use various optimization 29,30) and machine learning-based algorithms 31,32,33,34) to further improve the model performance.…”

Section: Discussionmentioning

confidence: 99%

Estimating the Coverage Performance of a Wireless Sensor Network Considering Boundary Effects in the Presence of Sensor Failure

Mini¹,

Pal²

2021

Evergreen

2

0

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Coverage is one of the most critical performance metric of wireless sensor networks (WSNs) because it shows how well a region of interest (RoI) is being monitored by the deployed network. In general, the RoI is either circular or rectangular in shape which have boundary regions. Sensor nodes (SNs) deployed in these regions suffer boundary effects (BEs), i.e., the useful coverage area of an SN deployed near the boundary regions is less as compared to the SNs deployed in the middle of the RoI. It is imperative to consider these BEs while evaluating the performance of WSNs because analytical results derived for large networks are not valid for finite networks. In addition, SNs of a deployed WSN are prone to failure due to a large number of factors such as battery drainage, high temperature, and other environmental conditions. Earlier researchers have ignored the impact of BEs in the presence of sensor failure while evaluating the coverage performance of WSNs. In this work, we derive an analytical model by considering BEs and sensor failure to achieve a closed form expression for the k-coverage performance of a WSN deployed in a rectangular RoI. Further, we analyze the influence of various network parameters such as number of SNs, sensing range, and sensor failure rate on the k-coverage performance of the network. The results obtained using the proposed model show a good match with simulations outcomes with Root Mean Square Error (RMSE) no more than 0.03, thus, validating our model. For 𝑟𝑟 𝑠𝑠 = 80 m and N = 100, 1-coverage probabilities are found to be 0.9874, 0.9313 and 0.8069 for k = 1, 2 and 3 respectively showing that the k-coverage probability deriorates with the increase in the value of k.

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“…Fig. 9 -The estimated roll angle from the neural network model [54] The MLP structure with 100 units of hidden neuron had been used by Bansal et al [52] to find Crazyflie 2.0 quadrotor dynamics model and to prove the ability of NN to learn a dynamic model as shown in Error! Reference source not found.. MLP has been trained with a resilient backpropagation learning algorithm.…”

Section: Fig 8-basic Mlp Nn Structurementioning

confidence: 99%

Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review

Pairan¹,

Shamsudin²,

Zulkafli³

2020

JAMEA

View full text Add to dashboard Cite

A quadcopter is a rotorcraft with a simple mechanical construction. It has the same hovering capability similar to the traditional helicopter, but it is easier to maintain. The quadcopter is hard to control due to its unstable system with highly coupled and non-linear dynamics. In order to design a robust control algorithm, it is crucial to obtain a precise quadrotor flight dynamics through system identification approach. System identification is a method of finding the mathematical model of the dynamics system using the input-output data measurement. Neural network (NN) based system identification is excellent alternative modeling because it reduces development costs and time by avoiding governing equations and large aerodynamic database. NN based system identification has successfully identified the quadcopter dynamics with good accuracy. This paper gives an overview of the characteristic of the quadcopter and presents a comprehensive survey of the modeling techniques used to determine the flight dynamics of a quadrotor with a particular focus on NN based system identification method. The presented research works have been classified into different categories such as the first principle modeling, system identification and implementation of NN based system identification in quadcopter platform. Finally, the paper highlights challenges that need to be addressed in developing efficient NN based system identification model for unmanned quadcopter system.

show abstract

System Identification for Quad-rotor Parameters Using Neural Network

Cited by 32 publications

References 7 publications

Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review

Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review

Estimating the Coverage Performance of a Wireless Sensor Network Considering Boundary Effects in the Presence of Sensor Failure

Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review

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