Quadrotors trajectory tracking using a differential flatness-quaternion based approach

Choutri, Kheireddine; Lagha, Mohand; Dala, Laurent; Lipatov, M.

doi:10.1109/icmsao.2017.7934901

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…The reference model parameters were calculated on the basis of the analysis of the results from [4]. The values of k x and k r were calculated using (17). The value of φ d could be chosen arbitrarily.…”

Section: Numerical Experiments and Discussionmentioning

confidence: 99%

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Quadrotor Trajectory Tracking Using Model Reference Adaptive Control, Neural Network-Based Parameter Uncertainty Compensator, and Different Plant Parameterizations

Glushchenko,

Lastochkin

2023

Computation

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A quadrotor trajectory tracking problem is addressed via the design of a model reference adaptive control (MRAC) system. As for real-world applications, the entire quadrotor dynamics is typically unknown. To take that into account, we consider a plant model, which contains uncertain nonlinear terms resulting from aerodynamic friction, blade flapping, and the fact that the mass and inertia moments of the quadrotor may change from their nominal values. Unlike many known studies, the explicit equations of the parameter uncertainty for the position control loop are derived in two different ways using the differential flatness approach: the control signals are (i) used and (ii) not used in the parametric uncertainty parameterization. After analysis, the neural network (NN) is chosen for both cases as a compensator of such uncertainty, and the set of NN input signals is justified for each of them. Unlike many known MRAC systems with NN for quadrotors, in this study, we use the kxx+krr baseline controller, which follows from the control system derivation, with both time-invariant (parameterization (i)) and adjustable (parameterization (ii)) parameters instead of an arbitrarily chosen non-tunable PI/PD/PID-like one. Adaptive laws are derived to adjust the parameters of NN uncertainty compensator for both parameterizations. As a result, the position controller ensures the asymptotic stability of the tracking error for both cases under the assumption of perfect attitude loop tracking, which is ensured in the system previously developed by the authors. The results of the numerical experiments support the theoretical conclusions and provide a comparison of the effectiveness of the derived parameterizations. They also allow us to make conclusions on the necessity of the baseline controller adjustment.

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Section: Numerical Experiments and Discussionmentioning

confidence: 99%

“…The aim is to ensure trajectory tracking independent of the value of the yaw angle, i.e., X d , Y d , Z d are to be followed with the required quality at any value of φ. To achieve this, the differential flatness approach [17] is applied:…”

Section: Uncertainty Parameterizationmentioning

confidence: 99%

Quadrotor Trajectory Tracking Using Model Reference Adaptive Control, Neural Network-Based Parameter Uncertainty Compensator, and Different Plant Parameterizations

Glushchenko,

Lastochkin

2023

Computation

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“…Quadrotors are a differential system with 4 at outputs [8]. These at outputs are the inertial position of the vehicle, x, y, and z, and the yaw angle .…”

Section: Quadrotors Dynamicsmentioning

confidence: 99%

“…However, their relative motions are strongly constrained to keep the formation. In order to achieve precise formation control of multiple UAVs such as quadrotors, an accurate position control of each one is required [8][9][10]. For formation control, consensus algorithms have been extensively studied in the literature [4,7].…”

Section: Introductionmentioning

confidence: 99%

Distributed Obstacles Avoidance For UAVs Formation Using Consensus-based Switching Topology

Choutri

Lagha

Dala³

2019

IJCDS

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Nowadays, most of the recent researches are focusing on the use of multi-UAVs in both civil and military applications. Multiple robots can offer many advantages compared to a single one such as reliability, time decreasing and various simultaneous interventions. However, solving the formation control and obstacles avoidance problems is still a big challenge. This paper proposes a distributed strategy for UAVs formation control and obstacles avoidance using a consensus-based switching topology. This novel approach allows UAVs to keep the desired topology and switch it in the event of avoiding obstacles. A double loop control structure is designed using a backstepping controller for tracking of the reference path, while a Sliding Mode Controller (SMC) is adopted for formation control. Furthermore, collaborative obstacles avoidance is assured by switching the swarm topology. Numerical simulations show the efficiency of the proposed strategy.

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“…However, Euler angles reach singularity at specific attitudes. To avoid these issues, quaternion is employed to represent the attitude of quadrotor UAVs, as can be seen in references [15][16][17][18], resulting in improved attitude control performance. In geometric theory, the changes in attitude are correlated to the evolution of the SO(3) manifold.…”

Section: Introductionmentioning

confidence: 99%

A Robust Disturbance-Rejection Controller Using Model Predictive Control for Quadrotor UAV in Tracking Aggressive Trajectory

Xu,

Fan,

Qiu

et al. 2023

Drones

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A robust controller for the waypoint tracking of a quadrotor unmanned aerial vehicle (UAV) is proposed in this paper, in which position control and attitude control are effectively decoupled. Model predictive control (MPC) is employed in the position controller. The constraints of motors are imposed on the state and input variables of the optimization equation. This design effectively mitigates the nonlinearity of the attitude loop and enhances the planning efficiency of the position controller. The attitude controller is designed using a nonlinear and robust control law based on SO(3) space, which enables continuous control on the SO(3) manifold. By extending the differential flatness of the quadrotor-UAV to the angular acceleration level, the mapping of the control reference from the position controller to the attitude controller is achieved. Simulations are carried out to demonstrate the capability of the proposed controller. In the simulations, multiple aggressive flight trajectories and severe external disturbances are designed. The results show that the controller is robust, with superior accuracy in tracking aggressive trajectories.

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Quadrotors trajectory tracking using a differential flatness-quaternion based approach

Cited by 8 publications

References 5 publications

Quadrotor Trajectory Tracking Using Model Reference Adaptive Control, Neural Network-Based Parameter Uncertainty Compensator, and Different Plant Parameterizations

Quadrotor Trajectory Tracking Using Model Reference Adaptive Control, Neural Network-Based Parameter Uncertainty Compensator, and Different Plant Parameterizations

Distributed Obstacles Avoidance For UAVs Formation Using Consensus-based Switching Topology

A Robust Disturbance-Rejection Controller Using Model Predictive Control for Quadrotor UAV in Tracking Aggressive Trajectory

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