Optimal Control of Quadrotor with a Novel Madgwick/Extended Kalman Observer to Track a Spline Trajectory for Obstacle Avoidance

Bouaiss, Oussama; Mechgoug, Raihane; Taleb‐Ahmed, Abdelmalik; Brikel, Ala Eddine

doi:10.1007/s40998-022-00552-y

Cited by 2 publications

(6 citation statements)

References 30 publications

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“…Desired attitude angles 𝜙 d and 𝜓 d are obtained utilizing Equation (64). The simulated initial conditions of the quadrotor are such as X(∶, 0) = 0 with X ∈ R 12 .…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The discrete KF is based on a discrete linear time variant (LTV) model dynamics obtained via the Jacobian calculated at every estimated operating point to enhance the linearization process of the system in a large operating envelope. The linear equivalent system is first transformed to a discrete model, then used for the iterative algorithm of the EKF combined with adaptive RBFNN 12 …”

Section: Modeling Of Dynamicsmentioning

confidence: 99%

“…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, 8‐11 nonlinear observer‐based control 12‐14 …”

Section: Introductionmentioning

confidence: 99%

“…Reference 20 developed an adaptive multi models‐based Kalman filter (KF) and in Reference 21, an extended Kalman filter (EKF) combined with Rauch Tung Striebel (RTS) smoother to generate accurate flight data. Reference 12 developed and demonstrated an enhanced combination of Madgwick data fuse filter that was proposed initially in References 22 and 23, with an EKF ensuring higher performance with faster convergence.…”

Section: Introductionmentioning

confidence: 99%

“…[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. 16 Quadrotor dynamics are highly nonlinear, furthermore, it is considered an underactuated system, due to the six degrees of freedom (DOF) that are manipulated by only four rotors.…”

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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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“…Desired attitude angles 𝜙 d and 𝜓 d are obtained utilizing Equation (64). The simulated initial conditions of the quadrotor are such as X(∶, 0) = 0 with X ∈ R 12 .…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Modeling Of Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

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

Bouaiss

Mechgoug

Taleb‐Ahmed

et al. 2023

Adaptive Control & Signal

Self Cite

View full text Add to dashboard Cite

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Control design of quadrotor aircraft based on improved integral backstepping sliding mode

Jinlong,

Jianhong,

Ruchun

et al. 2024

Asian Journal of Control

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An improved integral backstepping sliding mode control (IIBSMC) strategy is proposed to address the problems of long regulation time and poor disturbance resistance of integral backstepping control (IBC) for quadrotor aircraft. The IIBSMC method first introduces the integral term into the virtual variable on the basis of IBC, which makes the system respond faster, overshoot smaller and anti‐interference performance stronger. After that, combined with sliding mode control, the system is further processed to improve the control performance of the system. Finally, the quadrotor is controlled to achieve fixed‐point hovering and trajectory tracking, and the rotation and translation performance of the aircraft and the stability under external interference are improved. When the quadrotor aircraft is subjected to instantaneous interference, constant interference, sinusoidal interference, white noise interference, and complex interference, the simulation experiments of IBC, improved integral backstepping control (IIBC), integral backstepping sliding mode control (IBSMC), and IIBSMC are compared. It can be obviously found that the IIBSMC method has smaller system overshoot, faster recovery of the original equilibrium position, shorter adjustment time, and smaller error. When using the IIBSMC method to design the controller, the stability of the controller is theoretically proved by backstepping recursion. Finally, the simulation results show that the designed controller can better achieve fixed‐point hovering and trajectory tracking control well.

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Optimal Control of Quadrotor with a Novel Madgwick/Extended Kalman Observer to Track a Spline Trajectory for Obstacle Avoidance

Cited by 2 publications

References 30 publications

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

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

Control design of quadrotor aircraft based on improved integral backstepping sliding mode

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