PSO-based online estimation of aerodynamic ground effect in the backstepping controller of quadrotor

Keshavarzian, Hasan; Daneshjou, K.

doi:10.1007/s40430-020-02614-w

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…2) Offset Issues by Ground Effect of Near-ground Flight: Amphibious vehicles with rotary wings suffer from severe ground effects. The stability of take-off and landing is always a barrier for flight control systems [121]. When the ground effect occurs, the airflow obstructed by the ground generates aerodynamic interference and influences the normal airflow [122].…”

Section: Intelligent Control Technologiesmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Amphibious Ground-Aerial Vehicles: State of the Art Technology for Future Transportation

Zhang¹,

Huang²,

Huang³

et al. 2022

Preprint

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Amphibious ground-aerial vehicles fuse flying and driving modes to enable more flexible air-land mobility and have received growing attention recently. By analyzing the existing amphibious vehicles, we highlight the autonomous fly-driving functionality for the effective uses of amphibious vehicles in complex three-dimensional urban transportation systems. We review and summarize the key enabling technologies for intelligent flying-driving in existing amphibious vehicle designs, identify major technological barriers and propose potential solutions for future research and innovation. This paper aims to serve as a guide for research and development of intelligent amphibious vehicles for urban transportation toward the future.

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Section: Intelligent Control Technologiesmentioning

confidence: 99%

“…To address the above problems, compensation controllers are needed. For instance, [129] invented a backstepping controller that could optimize the parameters from the controller using a PSO algorithm. The key dominance of the controller is its real-time estimation and compensation of the ground effect dynamically.…”

Section: Intelligent Control Technologiesmentioning

confidence: 99%

Intelligent Amphibious Ground-Aerial Vehicles: State of the Art Technology for Future Transportation

Zhang¹,

Huang²,

Huang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To obtain accurate and efficient control performance, several optimization techniques such as artificial bee optimization, ant colony optimization, particle swamp optimization, and e.t.c. have been applied to quadrotors [29], [30], [31]. Due to the universal approximation properties of ANN and FLS, they have been employed to estimate the dynamic characteristics of quadrotors [32], [33].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping and Sliding Mode Control of a Quadrotor

maaruf,

Gulzar,

Abubakar

2024

Preprint

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Quadrotors are increasingly employed for both civilian and military applications. Recently, researchers have combined different control and estimation schemes to come up with a hybrid control structure to improve the robustness and tracking performance of the quadrotors. To further enhance the tracking precision of quadrotors subjected to parametric variations and environmental disturbances, this article proposes a new robust adaptive hybrid control architecture. In this study, the quadrotor model is divided into altitude, attitude, and position subsystems for which appropriate control methods are designed. A fractional-order sliding mode control with adaptive gain (AFSMC) is designed to enhance the tracking of the altitude subsystem. A robust backstepping control with adaptive gain (RAB) is developed for the horizontal position to generate the required roll and pitch orientations. A nonsingular fast terminal sliding mode control (NFTSMC) is incorporated with a finite-time disturbance observer (FDO) to accurately suppress the disturbances, follow the target rotation angles, and attain finite-time stability. The compounded control structure ensures accurate, fast, and robust tracking. The efficacy of the developed hybrid control scheme is assessed via simulations and comparisons with existing control methods.

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“…In addition, parameter tuning methods based on other intelligent algorithms are referred to this paper. For example, genetic algorithm (GA) [11][12][13], fuzzy control algorithm [14], particle swarm optimization algorithm (PSO) [15], differential evolution algorithm (DE) [16], and fuzzy neural network [17].…”

Section: Introductionmentioning

confidence: 99%

Parameter Tuning of Brushless DC Motor for Improving Control Effect with Worm Algorithm

Qin¹,

Wang²,

Xiao³

2021

EJEE

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Aiming at the problem of low control precision and small applicable scope caused by adjusting control parameters in Ziegler-Nichols (ZN) method, a parameter tuning method based on Worm algorithm (WOA) is proposed for Brushless DC motor. Firstly, the model of speed control is established by proportional integral method for Brushless DC motor with two - phase conduction and three - phase full bridge drive. Then the fitness function of the controller is constructed by the Integral Absolute Error (IAE). Finally, the early optimization process, the later movement rule and the peak extraction rule are determined for WOA, and the controller parameter tuning process is designed. Simulation results under constant and sinusoidal conditions show the effectiveness of the proposed method. WOA was compared with ZN, genetic algorithm (GA), differential evolution algorithm (DE) and particle swarm optimization algorithm (PSO) in the experiment. The experimental results show that the control effect (CE) of WOA under uniform speed has been improved by 2.56% on average, and has been improved by 16.93% on average under sinusoidal speed. Compared with previous methods, this method can be used for parameter adjustment of complex control with higher control precision.

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PSO-based online estimation of aerodynamic ground effect in the backstepping controller of quadrotor

Cited by 10 publications

References 28 publications

Intelligent Amphibious Ground-Aerial Vehicles: State of the Art Technology for Future Transportation

Intelligent Amphibious Ground-Aerial Vehicles: State of the Art Technology for Future Transportation

Adaptive Backstepping and Sliding Mode Control of a Quadrotor

Parameter Tuning of Brushless DC Motor for Improving Control Effect with Worm Algorithm

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