Parameter Identification for a Quadrotor Helicopter Using Multivariable Extremum Seeking Algorithm

Liu, Weizhen; Huo, Xin; Liu, Jinkun; Wang, Yunlong

doi:10.1007/s12555-017-0487-2

Cited by 14 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More specifically, if a f is either p f (hT ), v f (hT ) or ω f (hT ), then either the signalsṗ f (hT ),v f (hT ) oṙ ω f (hT ), respectively, are obtained with (33). 3) Signal transformation.…”

Section: )mentioning

confidence: 99%

“…The result is the linear velocity v f (hT ) in the body reference frame. Subsequently, the derivativesv f (hT ) andω f (hT ) needed in the regression matrix of the parameterized model in (11) are obtained by using the central difference algorithm in (33).…”

Section: )mentioning

confidence: 99%

“…In [32], the least-squares algorithm was used to estimate the parameters of the transfer functions used to represent part of the dynamic model of a quadrotor. In [33], a nonlinear closed-loop multi-variable extremum seeking parameter identification algorithm was proposed to estimate the parameters of a quadrotor. The results of the parameter identification algorithm support the identification method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Experimental Parameter Identifications of a Quadrotor by Using an Optimized Trajectory

et al. 2020

View full text Add to dashboard Cite

In this document, the parameter identification of a quadrotor is discussed. More precisely, the aim of this paper is to present results on the application of known methods for estimating the dynamic parameters that capture better the behavior of a quadrotor in comparison with the nominal parameters given by the manufacturer. To take into account the limitations of position, velocity, and acceleration of the quadrotor, an optimized trajectory to excite the quadrotor dynamics adequately is obtained. A proportionalintegral-derivative (PID) control scheme is used to implement experimentally the tracking of the optimized trajectory. The obtained data is processed off-line to construct the standard and filtered regression models from which the parameter identification is achieved. Specifically, the least-squares and gradient descent algorithms are applied to the regression models giving four sets of estimated parameters. The four sets of parameters obtained in this work are compared with the parameters provided by the manufacturer by computing the error between simulations and experiments. In addition, the output prediction errors of the regression models are computed, thus providing another validation form. All the comparisons show that the estimated parameters are more precise than the nominal ones. The given results support the functionality of the described methodology.INDEX TERMS Optimized trajectory, parameter identification, quadrotor, real-time experiments, regression model.

show abstract

Section: )mentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Experimental Parameter Identifications of a Quadrotor by Using an Optimized Trajectory

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Dynamic parameters are very important for control algorithms based on dynamic models and verification of simulation results [22]. Accurate dynamic parameters are the premise of accurate dynamic models, and accurate control can be achieved on the basis of accurate dynamic models [23,24]; precise control determines the application prospects of robots in the field of automation [25,26]. Therefore, as an important application of the dynamic model, dynamic parameter identification is of great significance to the application of industrial robots in manufacturing.…”

Section: Introductionmentioning

confidence: 99%

A Graph Theory-Based Method for Dynamic Modeling and Parameter Identification of 6-DOF Industrial Robots

Cheng

Chang

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

At present, the absolute positioning accuracy and control accuracy of industrial serial robots need to be improved to meet the accuracy requirements of precision manufacturing and precise control. An accurate dynamic model is an important theoretical basis for solving this problem, and precise dynamic parameters are the prerequisite for precise control. The research of dynamics and parameter identification can greatly promote the application of robots in the field of precision manufacturing and automation. In this paper, we study the dynamical modeling and dynamic parameter identification of an industrial robot system with six rotational DOF (6R robot system) and propose a new method for identifying dynamic parameters. Our aim is to provide an accurate mathematical description of the dynamics of the 6R robot and to accurately identify its dynamic parameters. First, we establish an unconstrained dynamic model for the 6R robot system and rewrite it to obtain the dynamic parameter identification model. Second, we establish the constraint equations of the 6R robot system. Finally, we establish the dynamic model of the constrained 6R robot system. Through the ADAMS simulation experiment, we verify the correctness and accuracy of the dynamic model. The experiments prove that the result of parameter identification has extremely high accuracy and the dynamic model can accurately describe the 6R robot system mathematically. The dynamic modeling method proposed in this paper can be used as the theoretical basis for the study of 6R robot system dynamics and the study of dynamics-based control theory.

show abstract

“…An approach to overcome the model uncertainty, such as unmodelled dynamics and parameter uncertainty, is using system identification techniques. In [35], a closed-loop multivariable extremum seeking algorithm (MESA) is suggested for parameter identification of a quadrotor. The black-box model identification using a continuous-time approach is adopted in [36] to obtain a linear model for the dynamics of a quadrotor.…”

Section: Introductionmentioning

confidence: 99%

System identification and H∞-based control of quadrotor attitude

Noormohammadi-Asl

Esrafilian

Arzati

2020

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

The attitude control of a quadrotor is a fundamental problem, which has a pivotal role in a quadrotor stabilization and control. What makes this problem more challenging is the presence of uncertainty such as unmodelled dynamics and unknown parameters. In this paper, to cope with uncertainty, an H ∞ control approach is adopted for a real quadrotor. To achieve H ∞ controller, first a continuous-time system identification is performed on the experimental data to encapsulate a nominal model of the system as well as a multiplicative uncertainty. By this means, H ∞ controllers for both roll and pitch angles are synthesized. To verify the effectiveness of the proposed controllers, some real experiments and simulations are carried out. Results verify that the designed controller does retain robust stability, and provide a better tracking performance in comparison with a well-tuned PID and a µ synthesis controller.

show abstract

Parameter Identification for a Quadrotor Helicopter Using Multivariable Extremum Seeking Algorithm

Cited by 14 publications

References 21 publications

Experimental Parameter Identifications of a Quadrotor by Using an Optimized Trajectory

Experimental Parameter Identifications of a Quadrotor by Using an Optimized Trajectory

A Graph Theory-Based Method for Dynamic Modeling and Parameter Identification of 6-DOF Industrial Robots

System identification and H∞-based control of quadrotor attitude

Contact Info

Product

Resources

About