Adaptive Sliding Mode Attitude Control of Quadrotor UAVs Based on the Delta Operator Framework

Zheng, Bo‐Chao; Wu, Yuewen; Li, Hui; Chen, Zhipeng

doi:10.3390/sym14030498

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…To validate the accuracy of the motion model of the amphibious multi-rotor UAV and the effectiveness of the cascade ADRC position and attitude controller in the cross-media phase, a position and attitude controller was developed in this study using both the PID algorithm and SMC (sliding mode control) algorithm [45,46].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Research on Amphibious Multi-Rotor UAV Out-of-Water Control Based on ADRC

Tan

Liang

et al. 2023

Applied Sciences

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This paper presents a study on controlling the out-of-water motion of amphibious multi-rotor UAVs using a cascade control method based on the Active Disturbance Rejection Control (ADRC) algorithm. The aim is to overcome the challenges of time-varying model parameters and complex external disturbances. The research involves developing an underwater dynamic model and analyzing hydrodynamic forces to calculate theoretical inertial hydrodynamic forces and simulate viscous hydrodynamic forces. This establishes the relationship between viscous hydrodynamic forces and exit velocity. A complete air dynamic model is then established, selecting model parameters based on the center of mass position of the amphibious vehicle to enable switching from water to air. To address control algorithm instability caused by changes in model parameters, position and attitude controllers are built using the ADRC algorithm. The control effects are compared with traditional PID and sliding mode controllers (SMC) to verify the effectiveness and superiority of the proposed cascade ADRC control strategy. Experimental results show that our controller has stronger anti-interference than traditional PID and SMC controllers and can overcome control instability caused by changes in model parameters. Our research highlights the importance of using ADRC-based controllers for amphibious multi-rotor UAVs to achieve robust and stable control.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Research on Amphibious Multi-Rotor UAV Out-of-Water Control Based on ADRC

Tan

Liang

et al. 2023

Applied Sciences

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“…According to the previous research content, the output parts of the command filter v d , the linear controller v pd and the fuzzy neural network controller v f nn have been obtained. Substituting it into (17) gives v = ..…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Compared with the nonlinear calculation, the linearization method is used to calculate the effect of thrust vector increment [16]. Furthermore, the sliding mode control [17], the active disturbance rejection control [18] and the fuzzy control [19] are often used as control algorithms for quadrotor UAV. A dynamic surface-active disturbance rejection control strategy is proposed by Zhang, in which a first-order filter is introduced to obtain the derivative of the virtual control and simplifies the control law of the system.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Neural Network Dynamic Inverse Control Strategy for Quadrotor UAV Based on Atmospheric Turbulence

Yang

Cheng

et al. 2022

Applied Sciences

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Quadrotor UAV is vulnerable to external interference, which affects search and rescue. In this paper, a fuzzy neural network dynamic inverse controller (FNN-DIC) is designed to eliminate the instability of the attitude angle caused by atmospheric turbulence. Considering the complexity of atmospheric turbulence, the component model of atmospheric turbulence is obtained firstly based on the Dryden model, using Gaussian white noise as a random input signal and a designed shaping filter. Combined with the Newton-Euler equation, a nonlinear dynamic model for the quadrotor UAV with atmospheric disturbance is established. While the traditional nonlinear dynamic inverse cancels the nonlinearity of the controlled object, it relies on precise mathematical models. The fuzzy neural network can adaptively compensate for the inaccurate part of the model and the inverse error of the model caused by the external disturbance, and the stability of the control system is strictly proved by using the Lyapunov function. The experiments are carried out on the simulation platform, and the results show that the FNN method can ensure that the quadrotor UAV can still fly smoothly against strong disturbances, and that robustness of the system is significantly improved.

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“…Adaptive sliding mode control (ASMC) is another effective solution to suppress system chattering caused by large discontinuous control gains [27][28][29][30][31][32][33][34]. Uncertainties induced by modeling errors, external disturbances and actuator faults are frequently dealt with by estimating and compensating for the load mass, moment of inertia or control effect matrix in ASMC.…”

Section: Introductionmentioning

confidence: 99%

Disturbance Observer-Enhanced Adaptive Fault-Tolerant Control of a Quadrotor UAV against Actuator Faults and Disturbances

Hu,

Wang,

Shen

et al. 2023

Drones

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For a quadrotor unmanned aerial vehicle (UAV), this paper proposes an adaptive sliding mode control (SMC) strategy enhanced with a disturbance observer to attain precise trajectory and attitude tracking performance while compensating for the detrimental impacts of actuator faults and disturbances. First, an adaptive SMC strategy that utilizes an integral sliding surface is presented to enhance the fault-tolerance capabilities of the studied quadrotor UAV against actuator faults. In addition, a disturbance observer is further created to compensate for the disturbances. By integrating the proposed adaptive SMC strategy with the designed disturbance observer, both actuator faults and disturbances can be effectively accommodated. It was theoretically demonstrated that the system is stable while using the proposed adaptive fault-tolerant control strategy. The effectiveness and benefits of the proposed strategy is verified with comparative simulation results under different faulty scenarios.

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Adaptive Sliding Mode Attitude Control of Quadrotor UAVs Based on the Delta Operator Framework

Cited by 12 publications

References 32 publications

Research on Amphibious Multi-Rotor UAV Out-of-Water Control Based on ADRC

Research on Amphibious Multi-Rotor UAV Out-of-Water Control Based on ADRC

Fuzzy Neural Network Dynamic Inverse Control Strategy for Quadrotor UAV Based on Atmospheric Turbulence

Disturbance Observer-Enhanced Adaptive Fault-Tolerant Control of a Quadrotor UAV against Actuator Faults and Disturbances

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