Robust Adaptive Synchronization of Interconnected Heterogeneous Quadrotors Transporting a Cable-Suspended Load

Cardona, Gustavo A.; Arévalo-Castiblanco, Miguel F.; Tellez‐Castro, Duvan; Calderón, Juan M.; Mojica‐Nava, Eduardo

doi:10.1109/icra48506.2021.9561513

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…The degradation in controller performance caused by noise is presented in Table 17. This degradation is calculated as the difference between the ideal operation without noise and the performance under noisy conditions, as shown in (19). It is important to note that the baseline controller does not utilize acceleration information for the control action calculation, and for the R-axis, speed information is also excluded.…”

Section: Noise Degradationmentioning

confidence: 99%

“…More complex control systems have been proposed to improve the performance of UAVs under specific operation points and to enhance the stability of these vehicles in the presence of external disturbances, such as those considering the dependency between movement and attitude in our plant to offer a robust control in the presence of loads [16], un-modeled high-frequency dynamics [17], an adaptive control for multi-quadrotor cooperation under load uncertainties [18], Robust adaptive control for heterogeneous multiquadrotor cooperation [19], and Adaptive Backstepping Control to deal with uncertainties and non-linear dynamics from the UAV model [20]. However, these models often require an accurate high-order mathematical model with sophisticated coefficient computations.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

et al. 2023

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In recent years, multi-rotor UAVs have become valuable tools in several productive fields, from entertainment to agriculture and security. However, during their flight trajectory, they sometimes do not accurately perform a specific set of tasks, and the implementation of flight controllers in these vehicles is required to achieve a successful performance. Therefore, this research describes the design of a flight position controller based on Deep Neural Networks and subsequent implementation for a multi-rotor UAV. Five promising Neural Network architectures are developed based on a thorough literature review, incorporating LSTM, 1-D convolutional, pooling, and fully-connected layers. A dataset is then constructed using the performance data of a PID flight controller, encompassing diverse trajectories with transient and steady-state information such as position, speed, acceleration, and motor output signals. The tuning of hyperparameters for each type of architecture is performed by applying the Hyperband algorithm. The best model obtained (LSTMCNN) consists of a combination of LSTM and CNN layers in one dimension. This architecture is compared with the PID flight controller in different scenarios employing evaluation metrics such as rise time, overshoot, steady-state error, and control effort. The findings reveal that our best models demonstrate the successful generalization of flight control tasks. While our best model is able to work with a wider operational range than the PID controller and offers step responses in the Y and X axis with 97% and 98% similarity, respectively, within the PID’s operational range. This outcome opens up possibilities for efficient online training of flight controllers based on Neural Networks, enabling the development of adaptable controllers tailored to specific application domains.

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Section: Noise Degradationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

et al. 2023

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“…According to [13], [18] and [19], in collaborative load transportation with multiple multi-rotors, the geometry of the multi-rotor formation can be modified to facilitate the precise positioning and orientation of the payload. However, studies [15]- [17] suggest that when three or more multirotors are employed for payload transportation, the payload is confined to the geometric center of the multi-rotor formation and the obstacle avoidance approaches proposed in [14], [18] require that multiple multi-rotors traverse the obstacles alongside the payload. However, in some situations, multiple multi-rotors might unable to navigate through obstacles to achieve successful payload transportation regardless of how the formation geometry is adjusted.…”

Section: Introductionmentioning

confidence: 99%

Large-Workspace Dual Multi-Rotor Aerial Payload Deployment System Using a Cable-Suspended Device With Ducted Fans

Han,

Miyazaki,

Gao

et al. 2024

IEEE Access

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The demand for payload transportation has shown exceptional growth owing to its inherent convenience and ability to reduce manpower requirements. To address the limitations of previous studies that primarily employed a single multi-rotor to deploy payloads within a limited reach, we designed a cablesuspended aerial payload deployment system with a large workspace that allowed payload deployment at a distance from the multi-rotor platforms. Our system comprised two multi-rotors, a payload deployment device (PDD, hereafter) with ducted fans, servomotors and a microcomputer. The ducted fans were used not only for swing suppression during payload transportation, but also to ensure that the PDD maintained a certain angle to the vertical plane where the dual multi-rotors were located during the deployment phase. Compared to existing research results, our design has a larger working distance in the vertical and horizontal directions. The controller designed for the PDD was adaptive and robust to variation of the payload mass and distance between the dual multi-rotors. We verified the effectiveness of our design and algorithms through several experiments.INDEX TERMS Cable-suspension, disturbance estimation and rejection, actuated payload deployment device, multi-rotor, unmanned aerial vehicles.

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“…Cooperative control of multi-robot systems has gained significant attention from the robotics and control community over the past two decades due to its potential applications in solving a variety of complex and repetitive tasks. For instance, localisation and mapping [1], search, rescue and retrieval operations [2], multi-target enclosing and surveillance [3], cooperative payload transportation [4], [5], etc. The term cooperative control encompasses leader-following consensus control, formation control, rendezvous control, group formation tracking, etc.…”

Section: Introductionmentioning

confidence: 99%

A Negative Imaginary Theory-Based Time-Varying Group Formation Tracking Scheme for Multi-Robot Systems: Applications to Quadcopters

Bhowmick

Lanzon

2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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Robust Adaptive Synchronization of Interconnected Heterogeneous Quadrotors Transporting a Cable-Suspended Load

Cited by 7 publications

References 18 publications

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

Large-Workspace Dual Multi-Rotor Aerial Payload Deployment System Using a Cable-Suspended Device With Ducted Fans

A Negative Imaginary Theory-Based Time-Varying Group Formation Tracking Scheme for Multi-Robot Systems: Applications to Quadcopters

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