System identification and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si14.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>-based control of quadrotor attitude

Noormohammadi-Asl, Ali; Esrafilian, Omid; Arzati, Mojtaba Ahangar

doi:10.1016/j.ymssp.2019.106358

Cited by 47 publications

(19 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Other researchers implemented the H ∞ controller for the quadcopter system because of its robustness in rejecting the model uncertainties and external disturbances. Noormohammadi-Asl et al [88] suggested a H ∞ controller for a quadcopter model to cope up with the unmodeled dynamics and unknown parameters. The experimental results indicated the robustness of the designed controller in providing better tracking performance compared to a well-tuned PID and μ synthesis controllers.…”

Section: Quad-rotor Systemsmentioning

confidence: 99%

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

2020

View full text Add to dashboard Cite

This paper presents a review of the various control strategies that have been conducted to address and resolve several challenges for a particular category of unmanned aerial vehicles (UAVs), the emphasis of which is on the rotorcraft or rotary-wing systems. Initially, a brief overview of the important relevant definitions, configurations, components, advantages/disadvantages, and applications of the UAVs is first introduced in general, encompassing a wide spectrum of the flying machines. Subsequently, the focus is more on the two most common and versatile rotorcraft UAVs, namely, the twin-rotor and quadrotor systems. Starting with a brief background on the dual-rotor helicopter and a quadcopter, the full detailed mathematical dynamic model of each system is derived based on the Euler-Lagrange and Newton-Euler methods, considering a number of assumptions and considerations. Then, a state-of-the-art review of the diverse control strategies for controlling the rotorcraft systems with conceivable solutions when the systems are subjected to the different impediments is demonstrated. To counter some of these limitations and adverse operating/loading conditions in the UAVs, several innovative control techniques are particularly highlighted, and their performance are duly analyzed, discussed, and compared. The applied control techniques are deemed to produce a useful contribution to their successful implementation in the wake of varied constraints and demanding environments that result in a degree of robustness and efficacy. Some of the off-the-shelf developments in the rotorcraft systems for research and commercial applications are also presented.

show abstract

Section: Quad-rotor Systemsmentioning

confidence: 99%

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

2020

View full text Add to dashboard Cite

show abstract

“…are the regression matrices of known functions, and Θ ∈ R 7 is the parameter vector. The implementation of the discrete filtering schemes is performed in MATLAB by using the function filter(b,a,x), where b and a represents row vectors containing the coefficients for the numerator and denominator either of the transfer functions (35) or (36), respectively, and x represents the signal to be processed. In order to reject the high-frequency noise components from the measured signals and avoiding to lose important information from the system dynamics, the cut-off frequency of the filter must be selected considering the highest frequency from the system excitation signals, see [25] and [58].…”

Section: ) Filtered Regression Model Constructionmentioning

confidence: 99%

“…Comparisons between a PID scheme and an internal model control were given. Parameter identification for a continuous-time black-box model of a quadrotor was achieved in [35].…”

Section: Introductionmentioning

confidence: 99%

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

“…Indeed, synthesis of several high-efficiency nonlinear control schemes may require accurate mathematical models or have a complex structure, which complicate their implementation in realistic systems due to some variables and parameters are unavailable or hard to obtain [9]. us, the Active Disturbance Rejection Control (ADRC) methodology constitutes an excellent alternative to achieve robustness against a wide class of disturbances [10].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Motion Tracking Control Approach for a Quadrotor Aerial Mechanical System

Yañez-Badillo

Beltrán-Carbajal

Tapia‐Olvera

et al. 2020

Shock and Vibration

View full text Add to dashboard Cite

This paper deals with the reference trajectory tracking problem and simultaneous active disturbance suppression on a class of controlled aerial mechanical systems by processing measurable output signals. A novel dynamic control method for desired motion reference trajectory tracking for quadrotor helicopters is introduced. Measurements of position output signals for efficient and robust tracking of motion profiles specified for the unmanned aerial vehicle are only required. Thus, differentiation of signals and real-time estimation of disturbances affecting the multi-input multioutput, underactuated nonlinear dynamic system are unnecessary. The presented active control approach can be directly extended for a class of vibrating mechanical systems. Analytical, experimental, and numerical results are presented to prove the satisfactory performance of the proposed trajectory tracking control approach for considerably perturbed operating scenarios.

show abstract

System identification and H∞-based control of quadrotor attitude

Cited by 47 publications

References 46 publications

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

Experimental Parameter Identifications of a Quadrotor by Using an Optimized Trajectory

A Dynamic Motion Tracking Control Approach for a Quadrotor Aerial Mechanical System

Contact Info

Product

Resources

About