Adaptive Robust Control based on Backstepping Sliding Mode techniques for Quadrotor UAV under external disturbances

Nettari, Yakoub; Kurt, Serkan; Labbadi, Moussa

doi:10.1016/j.ifacol.2022.07.320

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…The robustification against disturbances has also been studied in the literature [9]. In addition, the authors of [10] have proposed a backstepping method interfaced with sliding mode control for position and attitude control in the presence of external disturbances, while the estimation of controller parameters was assured by using adaptive laws. In addition, the upper limits of the attitude perturbations were estimated with the same approach.…”

Section: Introductionmentioning

confidence: 99%

Combined Observer-Based State Feedback and Optimized P/PI Control for a Robust Operation of Quadrotors

Benzinane,

Rauh

2024

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This paper deals with a discrete-time observer-based state feedback control design by taking into consideration bounded parameter uncertainty, actuator faults, and stochastic noise in an inner control loop which is extended in a cascaded manner by outer PI- and P-control loops for velocity and position regulation. The aim of the corresponding subdivision of the quadrotor model is the treatment of the control design in a systematic manner. In the inner loop, linear matrix inequality techniques are employed for the placement of poles into a desired area within the complex z-plane. A robustification of the design towards noise is achieved by optimizing both control and observer gains simultaneously guaranteeing stability in a predefined bounded state domain. This procedure helps to reduce the sensitivity of the inner control loop towards changes induced by the outer one. Finally, a model-based optimization process is employed to tune the parameters of the outer P/PI controllers. To allow for the validation of accurate trajectory tracking, a comparison of the novel approach with the use of a standard extended Kalman filter-based linear-quadratic regulator synthesis is presented to demonstrate the superiority of the new design.

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Section: Introductionmentioning

confidence: 99%

Combined Observer-Based State Feedback and Optimized P/PI Control for a Robust Operation of Quadrotors

Benzinane,

Rauh

2024

Axioms

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“…Unmanned aerial vehicles (UAV) control is considered one of the most complex control systems topics. Quadrotor is a special hovering class of UAV that has been an increasingly popular research and development platform, especially with its hovering, maneuverability, and effectiveness recently in the military sectors 1‐3 …”

Section: Introductionmentioning

confidence: 99%

“…Reference 11 investigated the adaptive NN to estimate and control the quadrotor by sliding mode tracking control for agriculture use. In Reference 2, a similar adaptive robust tracking based on backstepping techniques for a disturbed quadrotor was considered, where in Reference 10, adaptive NN fault‐tolerant control was investigated via fast terminal sliding mode.…”

Section: Introductionmentioning

confidence: 99%

“…Quadrotor is a special hovering class of UAV that has been an increasingly popular research and development platform, especially with its hovering, maneuverability, and effectiveness recently in the military sectors. [1][2][3] Recently, plenty of performant control methods have been proposed to solve these challenges considering different architectures, such as robust control, 4,5 adaptive control, 6,7 neural network (NN)-based control, [8][9][10][11] nonlinear observer-based control. [12][13][14] Robust autonomous trajectory tracking and stability analysis in presence of the model uncertainties, noise, and strong random bounded disturbances have been highly addressed and considered in either scenario of obstacles free environment 15 or dynamic obstacles avoidance.…”

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confidence: 99%

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Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

Bouaiss

Mechgoug

Taleb‐Ahmed

et al. 2023

Adaptive Control & Signal

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SummaryNeural network (NN) methods have become increasingly efficient and applicable in control. This article presents a novel nested control strategy based on adaptive radial basis function neural networks (RBFNN) and NN supervised control embedded with integrator back stepping (IBS) for a robust position and attitude trajectory tracking of quadrotor aerial robot in presence of modeling uncertainties, sensing noise, and external bounded disturbance. The control philosophy is derived from the decentralized inverse dynamics, where the adaptive RBFNN is adopted for the outer loop to approximate the unknown external disturbance, and adaptively compensate for their effect. In conjunction with a supervised control that stabilizes the attitude in the inner loop by adaptively compensating the unknown and unmodeled uncertainties, avoiding initial instability of the attitude during NN convergence. Furthermore, noise is attenuated by an adaptive extended Kalman filter. Stability analysis was elaborated using Lyapunov stability theory. Simulation of adaptive RBFNN control philosophy proved robustness and effectiveness compared to proportional integral derivative, IBS, and offline decentralized convolution neural network algorithms as it generates the control law by guarantying a fast convergence of parameters, better external disturbance compensation, and noise attenuation. The proposed scheme of robust tracking was validated.

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“…In view of the characteristics of UAV such as underactuation and strong coupling, PID control [2], sliding mode control [3], backstepping control [4] and neural network control [5] have been widely used in UAV control systems. In [6], fuzzy cascade PID and conventional cascade PID autonomously switching control strategy is used to improve the dynamic performance of UAV control systems, and the effectiveness of the algorithm is verified by actual flight.…”

Section: Introductionmentioning

confidence: 99%

Finite-time control based on RBF neural network for quadrotor UAVs with varied mass load

Duan,

Zhou,

Zhao

et al. 2023

J. Phys.: Conf. Ser.

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Aiming at the problem of the gradual reduction of the weight and the external wind disturbance affect flight performance of the quadrotor Unmanned Aerial Vehicle (UAV), a dual-loop finite time control strategy based on Radial Basis Function (RBF) neural network is proposed. The UAV model under disturbance is decoupled into position outer loop subsystem and attitude inner loop subsystem. In the outer loop, the changing weight and the external wind disturbance are approximated by using RBF neural network, command filter is used to avoid the “computing explosion” problem in the traditional backstepping method, and the finite-time control method is able to improve the convergence speed of the position. In the inner loop, the cascade RBF neural network PID control which relies on the self-learning of neural network to realize the dynamic tuning of PID parameters is adopted to achieve rapid convergence of the attitude angle. The simulation results show that compared with the traditional backstepping method and cascade PID control, the convergence time is reduced by 31% on average, which verifies the superiority and effectiveness of the proposed control strategy.

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Adaptive Robust Control based on Backstepping Sliding Mode techniques for Quadrotor UAV under external disturbances

Cited by 6 publications

References 8 publications

Combined Observer-Based State Feedback and Optimized P/PI Control for a Robust Operation of Quadrotors

Combined Observer-Based State Feedback and Optimized P/PI Control for a Robust Operation of Quadrotors

Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

Finite-time control based on RBF neural network for quadrotor UAVs with varied mass load

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