Robust Adaptive Multilevel Control of a Quadrotor

Mahmoud, Magdi S.; Maaruf, Muhammad

doi:10.1109/access.2020.3022724

Cited by 10 publications

(7 citation statements)

References 55 publications

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“…In the implementation, the parameters are chosen as follows: Figures 6,11,and 16 show that the quadrotor is able to reach the desired position using the different methods and strategies. Figures 7,8,12,13,17,and 18 show the errors between the real and reference state variables. The position and attitude errors converge within a small proximity to zero.…”

Section: Simulation Results: Blf Controlmentioning

confidence: 99%

“…Therefore, its control system warrants further study, if it is to be stable and robust. Recently, many control strategies have been developed and applied for UAV stabilization and trajectory tracking, including feedback linearization (FBL) [1], [2], model predictive control (MPC) [3], [4], and [5], sliding mode control (SMC) [6], [7], adaptive control [8], [9], and backstepping [10], [11], to name a few.…”

Section: Introductionmentioning

confidence: 99%

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Barrier Lyapunov function and adaptive backstepping-based control of a quadrotor UAV

Khadhraoui,

Zouaoui,

Saad

2023

Robotica

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This paper presents backstepping control and backstepping constraint control approaches for a quadrotor unmanned aerial vehicle (UAV) control system. The proposed methods are applied to a Parrot Mambo drone model to control rotational motion along the $x$ , $y$ , and $z$ axes during hovering and trajectory tracking. In the backstepping control approach, each state of the system controls the previous state and is called “virtual control.” The last state is controlled by the real control input. The idea is to compute, in several steps, a control law that ensures the asymptotic stability of the system. The backstepping constraint control method, based on barrier Lyapunov functions (BLFs), is designed not only to track the desired trajectory but also to guarantee no violation of the position and angle constraints. Symmetric BLFs are introduced in the design of the controller. A nonlinear mathematical model is considered in this study. Based on Lyapunov stability theory, it can be concluded that the proposed controllers can guarantee the stability of the UAV system and the state converges asymptotically to the desired trajectory. To make the control robust, an adaptation law is applied to the backstepping control that estimates the unknown parameters and ensures their convergence to their respective values. Validation of the proposed controllers was performed by simulation on a flying UAV system.

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Section: Simulation Results: Blf Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Barrier Lyapunov function and adaptive backstepping-based control of a quadrotor UAV

Khadhraoui,

Zouaoui,

Saad

2023

Robotica

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“…In literature [51], the authors developed an adaptive backstepping control for trajectory tracking operation of an underactuated quadrotor UAV. In [52], a robust adaptive multilevel control of a quadrotor has been presented. The dynamic model of the quadrotor was divided into three subsystems and the different control methods were designed for the systems.…”

Section: Adaptive Controlmentioning

confidence: 99%

A Survey of Control Methods for Quadrotor UAV

Maaruf

Mahmoud

Ma’arif

2022

IJRCS

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Flight control design of unmanned aerial vehicles UAVs is becoming increasingly important due to advances in computational power of computers with lower cost. The control algorithms are mainly employed for the attitude and position control of the UAVs. In the past decades, quadrotors have become the most popular UAVs, their adaptability and small size. They are employed to carry out tasks such as delivery, exploration, fumigation, mapping, surveillance, rescue mission, traffic monitoring, and so on. While carrying out these tasks, quadrotor UAVs face various challenges, such as environmental disturbances, obstacles, and parametric and non-parametric perturbations. Therefore, they require robust and effective control to stabilize them and enhance their performance. This paper provides a survey of recent developments in control algorithms applied to attitude and position loops of quadrotor UAVs. In addition, the limitations of the previous control approaches are presented. In order to overcome the relative drawbacks of the previous control techniques and enhance the performance of the quadrotor, researchers are combining various control approaches to obtain the hybrid control architecture. In this study, a review of the recent hybrid control schemes is presented.

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“…In this section, the effectiveness of the proposed control approach is demonstrated by implementing it on a quadrotor dynamic model. the fully actuated nonlinear state-space model of the quadrotor is given by [50]:…”

Section: Simulation Studymentioning

confidence: 99%

“…. The physical meaning and values of the parameters are available in [50]. This paper has presented a finite-time RBFNN backstepping control of uncertain nonlinear systems with unknown dynamics and dead-zones subjected to external disturbances.…”

Section: Simulation Studymentioning

confidence: 99%

Neural Network-based Finite-time Control of Nonlinear Systems with Unknown Dead-zones: Application to Quadrotors

Maaruf

Babangida²,

Almusawi³

et al. 2022

Journal of Robotics and Control

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Over the years, researchers have addressed several control problems of various classes of nonlinear systems. This article considers a class of uncertain strict feedback nonlinear system with unknown external disturbances and asymmetric input dead-zone. Designing a tracking controller for such system is very complex and challenging. This article aims to design a finite-time adaptive neural network backstepping tracking control for the nonlinear system under consideration. In addition, all unknown disturbances and nonlinear functions are lumped together and approximated by radial basis function neural network (RBFNN). Moreover, no prior information about the boundedness of the dead-zone parameters is required in the controller design. With the aid of a Lyapunov candidate function, it has been shown that the tracking errors converge near the origin in finite-time. Simulation results testify that the proposed control approach can force the output to follow the reference trajectory in a short time despite the presence of asymmetric input dead-zone and external disturbances. At last, in order to highlight the effectiveness of the proposed control method, it is applied to a quadrotor unmanned aerial vehicle (UAV).

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Robust Adaptive Multilevel Control of a Quadrotor

Cited by 10 publications

References 55 publications

Barrier Lyapunov function and adaptive backstepping-based control of a quadrotor UAV

Barrier Lyapunov function and adaptive backstepping-based control of a quadrotor UAV

A Survey of Control Methods for Quadrotor UAV

Neural Network-based Finite-time Control of Nonlinear Systems with Unknown Dead-zones: Application to Quadrotors

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