UAV dynamics model parameters estimation techniques: A comparison study

Al-Shabi, Mohammad; Hatamleh, Khaled S.; Asad, Asad A.

doi:10.1109/aeect.2013.6716436

Cited by 14 publications

(16 citation statements)

References 8 publications

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“…   Figure 2. Schematic diagram of a quadrotor [27] Reference [26] presented a detailed nonlinear dynamics model of the RAVEN Quadrotor. The authors of this work have developed a discrete-time dynamics model for the same platform as described below.…”

Section: Computer Experiments Quadroter Dynamicsmentioning

confidence: 99%

“…That is done using a smoothing boundary layer with zero width. After that, the CKF is used in a secondary loop to refine the estimates by a time-varying smoothing boundary layer that was previously derived in [26] as: where , | and , | are the a priori output's estimation error for the first filter and its covariance matrix, respectively. The latter is obtained from equation 2.2.8.…”

Section: Cubature Smooth Variable Structure Filtermentioning

confidence: 99%

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The cubature smooth variable structure filter estimation strategy applied to a quadrotor controller

2015

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Unmanned aerial systems (UAS) are becoming increasingly popular in industry, military, and social environments. An UAS that provides good operating performance and robustness to disturbances is often quite expensive and prohibitive to the general public. To improve UAS performance without affecting the overall cost, an estimation strategy can be implemented on the internal controller. The use of an estimation strategy or filter reduces the number of required sensors and power requirement, and improves the controller performance. UAS devices are highly nonlinear, and implementation of filters can be quite challenging. This paper presents the implementation of the relatively new cubature smooth variable structure filter (CSVSF) on a quadrotor controller. The results are compared with other state and parameter estimation strategies.

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Section: Computer Experiments Quadroter Dynamicsmentioning

confidence: 99%

Section: Cubature Smooth Variable Structure Filtermentioning

confidence: 99%

The cubature smooth variable structure filter estimation strategy applied to a quadrotor controller

2015

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“…In order to show that, the percentage errors of all "OS4" estimated parameters using all methods are summarized in Table 5. Results of parameter estimation from previous work done by the authors [21] using recursive least squares (RLS) were included in Table 5.…”

Section: Paramentioning

confidence: 99%

“…While, Falkenberg used a Gray-Box-based, iterative parameter identification approach, that offered good accuracy [20]. Al-Shabi et al [21] introduced a comparison study between two parameter estimation methods; recursive least squares (RLS) and Smooth Variable Structure Filter (SVSF) applied over an "OS4" Quadrotor model. Results indicated that both RLS and SVSF have good performance, rapid convergence, and low percentage error with the absence of introduced state noise.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicles parameter estimation using Artificial Neural Networks and Iterative Bi-Section Shooting method

Hatamleh

Al-Shabi

Al-Ghasem

et al. 2015

Applied Soft Computing

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“…The problem with such applications is the necessary to apply nonlinear control signals to achieve the desired trajectories. The latter is not easy to be implemented and has several limitations [1]- [3]. For example, Sliding Mode Control (SMC) [1] is one of the robust control approaches.…”

Section: Introductionmentioning

confidence: 99%

Sigma-Point Filters in Robotic Applications

Al-Shabi¹

2015

ICA

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Sigma-Point Kalman Filters (SPKFs) are popular estimation techniques for high nonlinear system applications. The benefits of using SPKFs include (but not limited to) the following: the easiness of linearizing the nonlinear matrices statistically without the need to use the Jacobian matrices, the ability to handle more uncertainties than the Extended Kalman Filter (EKF), the ability to handle different types of noise, having less computational time than the Particle Filter (PF) and most of the adaptive techniques which makes it suitable for online applications, and having acceptable performance compared to other nonlinear estimation techniques. Therefore, SPKFs are a strong candidate for nonlinear industrial applications, i.e. robotic arm. Controlling a robotic arm is hard and challenging due to the system nature, which includes sinusoidal functions, and the dependency on the sensors' number, quality, accuracy and functionality. SPKFs provide with a mechanism that reduces the latter issue in terms of numbers of required sensors and their sensitivity. Moreover, they could handle the nonlinearity for a certain degree. This could be used to improve the controller quality while reducing the cost. In this paper, some SPKF algorithms are applied to 4-DOF robotic arm that consists of one prismatic joint and three revolute joints (PRRR). Those include the Unscented Kalman Filter (UKF), the Cubature Kalman Filter (CKF), and the Central Differences Kalman Filter (CDKF). This study gives a study of those filters and their responses, stability, robustness, computational time, complexity and convergences in order to obtain the suitable filter for an experimental setup.

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UAV dynamics model parameters estimation techniques: A comparison study

Cited by 14 publications

References 8 publications

The cubature smooth variable structure filter estimation strategy applied to a quadrotor controller

The cubature smooth variable structure filter estimation strategy applied to a quadrotor controller

Unmanned Aerial Vehicles parameter estimation using Artificial Neural Networks and Iterative Bi-Section Shooting method

Sigma-Point Filters in Robotic Applications

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