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Robust tracking control of quadrotor based on flatness and active disturbance rejection control

Amine Abadi, Adnen El Amraoui, Hassen Mekki, Nacim Ramdani

2020IET Control Theory and Applications34 citationsDOI

Abstract

This study proposes a robust tracking controller for an underactuated quadrotor model based on the flatness theory and active disturbance rejection control (ADRC). Exploiting the differential flatness characteristic, it is demonstrated that a non‐linear quadrotor system can be changed into a linear canonical form, where it is easier to create a state feedback controller that ensures accurate trajectory tracking. In order to improve the tracking performance of the quadrotor, other factors are considered in the conception of the state feedback controller consisting in the estimation of the un‐measurable state variables in a canonical (Brunovsky) form and in the elimination of external perturbations and modelling uncertainties affecting the quadrotor system. To deal with this problem, first, an extended state observer (ESO) is designed to estimate a system state and an extended state known as lumped uncertainties. The latter represents the total effects of uncertain parameters, the neglected part of non‐linearity and the external disturbances. Second, based on the ESO result, an additional term is integrated into the feedback controller to eliminate the lumped disturbance effects and to ensure the stability of the closed loop. Simulation results are introduced to prove the benefits of associating the ADRC approach with flatness control.

Topics & Concepts

Control theory (sociology)Active disturbance rejection controlFlatness (cosmology)Disturbance (geology)Control engineeringRobust controlTracking (education)Robustness (evolution)Computer scienceControl (management)EngineeringControl systemArtificial intelligencePhysicsNonlinear systemBiologyPsychologyGeneQuantum mechanicsPaleontologyState observerElectrical engineeringBiochemistryCosmologyPedagogyAdaptive Control of Nonlinear SystemsTeleoperation and Haptic SystemsIterative Learning Control Systems
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