Robust Observer-based Trajectory Tracking Control for Unmanned Aerial Manipulator

Liang, Jiacheng; Chen, Yanjie; Lai, Ningbin; He, Bingwei

doi:10.1007/s12555-021-0829-y

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…In [3], a disturbance observer is employed to estimate time-varying external disturbances and uncertainties for ducted fan aerial vehicles. Te unknown disturbance is estimated as one of the unmeasured states based on the expansion state observer in [4][5][6][7].…”

Section: Literature Review Of Related Workmentioning

confidence: 99%

Nonsingular Fast Terminal Sliding Mode Neural Network Decentralized Control of a Quadrotor Unmanned Aerial Vehicle

Mao,

Chen

2023

Complexity

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A nonsingular terminal sliding mode decentralized controller that can ensure the tracking errors of the trajectories and attitude rapid convergence in finite time is proposed for an insufficient driven and strongly coupled nonlinear four-rotor unmanned aerial vehicle (UAV). The total lift of the UAV system is decomposed into three virtual drive separation forces corresponding to the three positions. The insufficient drive UAV system is transformed into a virtual full-drive model for research. The three position states and the three attitude states of UAV are placed correspondingly to the six subsystems by variable substitution. The model uncertainty and unknown disturbance term for each subsystem serves as total coupling terms among the subsystems. The upper bounds of the total coupling terms are considered as unknown ordinary higher order polynomials varying with the six states of the system under the action of time change. With the help of Cauchy inequality, the estimates of the upper bounds are obtained from the approximation performance of the RBF neural network. Finally, the decentralized controller is designed for each attitude subsystem and the virtual decentralized controller for each position subsystem. It is also mapped to the tracking total lift controller by using the virtual decentralized position controller. The controller design process uses the nonsingular terminal sliding mode control technology to ensure that the quadrotor attitude and position variables can quickly converge to the desired value in a short time. Simulation experiments verify that the proposed control method is effective and feasible.

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Section: Literature Review Of Related Workmentioning

confidence: 99%

Nonsingular Fast Terminal Sliding Mode Neural Network Decentralized Control of a Quadrotor Unmanned Aerial Vehicle

Mao,

Chen

2023

Complexity

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“…Initially, UAVs were primarily used for imaging purposes by incorporating cameras (Altan & Hacıoğlu, 2020;Karaduman et al, 2019;Khan et al, 2022;Yao et al, 2019). Over time, they were employed for sample collection (Bonyan Khamseh et al, 2018;Hamaza et al, 2019) and object transport (Altuğ et al, 2020;de Angelis & Giulietti, 2023;Vidyadhara et al, 2022) by attaching various grippers (Ballesteros-Escamilla et al, 2019;Liang et al, 2023;Stewart et al, 2023). Similarly, their usage in agricultural practices, such as seed scattering and spraying, has become more widespread (Guillen-Climent et al, 2012;Wang et al, 2022;Yıldırım & Ulu, 2023;Zheng et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Proposal for a UAV-Integrated Manipulator-Based System for Capturing Animals with Anesthesia: Simulation and Experimental-Based Validation

Cabuk

2024

Preprint

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In this study, a model was proposed for performing remote anesthesia on animals using a quadrotor unmanned aerial vehicle (UAV). The proposed model integrates a manipulator into the UAV, and a mechanism system for throwing an anesthesia needle was mounted on this manipulator. A solid model of the entire proposed system was created, and a mathematical model of the system was developed. The validity of the proposed model was verified by examining the system parameters through simulation studies based on this mathematical model. Two different scenarios were constructed in the simulations. In the first scenario, the system locked on the target for a fixed position, while the second scenario was on a target that is in motion in all three dimensions. In both simulations, the UAV flew autonomously along a predetermined trajectory. The results obtained were presented graphically. In addition, the system for which a solid model was created was produced and preliminary tests were carried out in the outdoor environment. It has been shown that this proposed system can be used in a real environment.

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“…In [10], an adaptive sliding mode tracking controller of an industrial manipulator was proposed to suppress the complex uncertain factors including parameter changing, environmental disturbance and joint friction. In [11], a robust observer-based trajectory tracking control method was presented to solve the stable motion problem for an unmanned aerial manipulator considering internal interactions and external disturbances. In [12], an adaptive controller was proposed for a hydraulic manipulator considering uncertain environment with unknown stiffness and location.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered Stochastic Finite-time Tracking Control of Robot Manipulator with Uncertain Disturbance Neural Network Estimation

Dang,

2024

Preprint

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This study presents a novel event-triggered finite-time stochastic control method for a robot manipulator. The random moment of inertia of the manipulator system is expressed by building a stochastic dynamic model, and the parameter variation disturbance is estimated by using a stochastic configuration neural network. An event-triggered controller with uncertain disturbance rejection is proposed, which not only realizes the stochastic finite-time stability of the tracking error system, but also guarantees the safety of motion velocity, and robustly improves the tracking accuracy of the robot manipulator. Compared with the existing works, the obvious feature of the proposed method is that it can simultaneously solve the random disturbance and uncertain parameter disturbance of the manipulator system, save communication resources, and ensure that the manipulator system can reach a steady state in finite time. We also discuss the effectiveness of the proposed stochastic tracking control method. Simulation and comparative analysis results further show that the controller can be updated less frequently while guaranteeing robust tracking performance of the robot manipulator.

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Robust Observer-based Trajectory Tracking Control for Unmanned Aerial Manipulator

Cited by 6 publications

References 32 publications

Nonsingular Fast Terminal Sliding Mode Neural Network Decentralized Control of a Quadrotor Unmanned Aerial Vehicle

Nonsingular Fast Terminal Sliding Mode Neural Network Decentralized Control of a Quadrotor Unmanned Aerial Vehicle

Proposal for a UAV-Integrated Manipulator-Based System for Capturing Animals with Anesthesia: Simulation and Experimental-Based Validation

Event-triggered Stochastic Finite-time Tracking Control of Robot Manipulator with Uncertain Disturbance Neural Network Estimation

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