Robust Model-Free Control Applied to a Quadrotor UAV

Younes, Younes Al; Drak, Ahmad; Noura, Hassan; Rabhi, Abdelhamid; Hajjaji, Ahmed El

doi:10.1007/s10846-016-0351-2

Cited by 84 publications

(54 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It may also depend on its relative degree. Indeed, studies have shown that with the i-PID and intelligent Proportional Derivative (i-PD) controllers the value ν = 2 is suitable while for the intelligent Proportional Integral (i-PI) and the intelligent Proportional (i-P) a value ν = 1 works better [26].…”

Section: A Ultra Local Modelmentioning

confidence: 99%

Intelligent Proportional–Integral–Derivative Control-Based Modulating Functions for Laser Beam Pointing and Stabilization

Ndoye

Asiri

Aloufi

et al. 2020

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

Section: A Ultra Local Modelmentioning

confidence: 99%

Intelligent Proportional–Integral–Derivative Control-Based Modulating Functions for Laser Beam Pointing and Stabilization

Ndoye

Asiri

Aloufi

et al. 2020

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

“…Replacing (18) in (17) with K equal to Proportional-Derivative gains, we have : (19) It follows that theoretically, if the error between the pitch angle estimator and the real pitch angle, is approximately zero during [t − T, t] :…”

Section: B Illustrative Examplementioning

confidence: 99%

Model-Free Control Approach for Fixed-Wing UAVs with Uncertain Parameters Analysis

Barth¹,

Condomines²,

Moschetta

et al. 2018

2018 23rd International Conference on Methods &Amp; Models in Automation &Amp; Robotics (MMAR)

View full text Add to dashboard Cite

This paper presents first results of an innovative Model-Free Control (MFC) architecture applied to fixed-wing UAVs. MFC is an algorithm dedicated to systems with poor modeling knowledge. Indeed, the costs to derive a reliable and representative aerodynamic model for UAVs motivated the use of such a controller. By exploiting a purely numerical model, this algorithm provides an intuitive method to tune the control loop without any information about the controlled system. We propose to extend the MFC architecture to the case of fixedwing UAVs and study the MFC properties in terms of uncertain parameters. As a first result, our designed MFC architecture provides a continuous controller able to stabilize the entire flight envelope of two different fixed-wing UAVs. These results show promising adaptive perspectives and demonstrate that MFC presents robust properties for both uncertain parameters and disturbance rejection.

show abstract

“…5 The MFC technique is first proposed by Fliess for single-input-single-output nonlinear dynamic systems. 6 Several engineering applications for the MFC are presented [7][8][9][10] with various modifications and variants, and some amendments on the algorithm are performed. A fuzzy version of the MFC is designed 11 and a sliding-mode MFC is proposed.…”

Section: Introductionmentioning

confidence: 99%

Optimal model‐free control for a generic MIMO nonlinear system with application to autonomous mobile robots

Safaei

Mahyuddin

2018

Adaptive Control & Signal

View full text Add to dashboard Cite

In this paper, the design procedure for optimal model-free control algorithm is presented for the tracking problem of completely unknown nonlinear dynamic systems operating under unknown disturbances. The procedure includes a new structure in the context of model-free control and data-driven control algorithms. In the new structure, the unknown nonlinear functions are segmented into 1 unknown linear-in-states part and another unknown nonlinear part. The adaptive laws proposed for estimating the unknown system dynamics are regressor-free estimation methods in which there is no need for regressor parameters and, consequently, the persistent excitation condition is not required anymore. Moreover, the main controller gains are updated online, incorporating the adapted values of linear terms in the system dynamics. A comparative study is presented to show that the proposed optimal model-free control outperforms the state-of-the-art model-free control algorithms. In addition, the simulation results for the application of the algorithm on autonomous mobile robots are provided.

show abstract

Robust Model-Free Control Applied to a Quadrotor UAV

Cited by 84 publications

References 12 publications

Intelligent Proportional–Integral–Derivative Control-Based Modulating Functions for Laser Beam Pointing and Stabilization

Intelligent Proportional–Integral–Derivative Control-Based Modulating Functions for Laser Beam Pointing and Stabilization

Model-Free Control Approach for Fixed-Wing UAVs with Uncertain Parameters Analysis

Optimal model‐free control for a generic MIMO nonlinear system with application to autonomous mobile robots

Contact Info

Product

Resources

About