Model-based real-time robust controller for a small helicopter

He, Miaolei; He, Jiping; Scherer, Sebastian

doi:10.1016/j.ymssp.2020.107022

Cited by 20 publications

(10 citation statements)

References 30 publications

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“…As mentioned above, the monitoring process basically consists in a flight around the person’s position to obtain images of the face. Indeed, several very recent papers are dedicated to the still challenging task of designing robust flight controllers to control small flying vehicles (e.g., [ 27 , 28 , 29 ]), especially indoors [ 30 ]. In our case, during the flight around the person, a visual interaction between the robot and the assisted person is produced.…”

Section: General Description Of the Systemmentioning

confidence: 99%

Feeling of Safety and Comfort towards a Socially Assistive Unmanned Aerial Vehicle That Monitors People in a Virtual Home

Belmonte

García

Morales

et al. 2021

Sensors

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Unmanned aerial vehicles (UAVs) represent a new model of social robots for home care of dependent persons. In this regard, this article introduces a study on people’s feeling of safety and comfort while watching the monitoring trajectory of a quadrotor dedicated to determining their condition. Three main parameters are evaluated: the relative monitoring altitude, the monitoring velocity and the shape of the monitoring path around the person (ellipsoidal or circular). For this purpose, a new trajectory generator based on a state machine, which is successfully implemented and simulated in MATLAB/Simulink®, is described. The study is carried out with 37 participants using a virtual reality (VR) platform based on two modules, UAV simulator and VR Visualiser, both communicating through the MQTT protocol. The participants’ preferences have been a high relative monitoring altitude, a high monitoring velocity and a circular path. These choices are a starting point for the design of trustworthy socially assistive UAVs flying in real homes.

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Section: General Description Of the Systemmentioning

confidence: 99%

Feeling of Safety and Comfort towards a Socially Assistive Unmanned Aerial Vehicle That Monitors People in a Virtual Home

Belmonte

García

Morales

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, a new robust sliding mode controller is proposed. Model-based state feedback is presented with a linear ESO to ensure attitude state estimation, and a new robust SMC controller is designed by combing backstepping and SCM methods to ensure the stability of the controller (He et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

Human-robot skills transfer interface for UAV-based precision pesticide in dynamic environments

Zhou

Chen

et al. 2021

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Purpose Nowadays, the global agricultural system is highly dependent on the widespread use of synthetic pesticides to control diseases, weeds and insects. The unmanned aerial vehicle (UAV) is deployed as a major part of integrated pest management in a precision agriculture system for accurately and cost-effectively distributing pesticides to resist crop diseases and insect pests. Design/methodology/approach With multimodal sensor fusion applying adaptive cubature Kalman filter, the position and velocity are enhanced for the correction and accuracy. A dynamic movement primitive is combined with the Gaussian mixture model to obtain numerous trajectories through the teaching of a demonstration. Further, to enhance the trajectory tracking accuracy under an uncertain environment of the spraying, a novel model reference adaptive sliding mode control approach is proposed for motion control. Findings Experimental studies have been carried out to test the ability of the proposed interface for the pesticides in the crop fields. The effectiveness of the proposed interface has been demonstrated by the experimental results. Originality/value To solve the path planning problem of a complex unstructured environment, a human-robot skills transfer interface is introduced for the UAV that is instructed to follow a trajectory demonstrated by a human teacher.

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“…Nowadays, there are different study areas focused on he-licopters, being of special interest the improvement of its stability, autonomous navigation, takeoff and landing control, trajectory tracking, disturbance rejection, among others [7]- [10]. To achieve the above, it is necessary to know the behavior of the disturbances that frequently occur, which greatly impairs the Tail Rotor (TR), causing stability problems during flight and limiting maneuvering space [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Integration of CNN in a Dynamic Model-Based Controller for Control of a 2DOF Helicopter With Tail Rotor Perturbations

et al. 2022

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This paper shows a proposal for a control scheme for the trajectory tracking problem in a Two Degree of Freedom Helicopter (2DOFH). For this purpose, a control scheme based on a feedback linearization combined with a Generalized Proportional Integral (GPI) controller is used. In order to implement linearization by feedback, it is required to know and have access to all the physical 2DOFH parameters, however, angular velocity and viscous friction are often not available. Commonly, state observers are used to know the angular velocity, however, estimating friction results out to be more complex. Therefore, we propose the use of a Convolutional Neural Network (CNN) to estimate viscous friction and angular velocity. The variables estimated by the CNN are entered into both the GPI and feedforward controllers. Thus, the system is brought to a linear representation that directly relates the GPI control to the dynamics of perturbations and non-model parameters. Finally, results of numerical simulations are shown that validate the robustness of our scheme in the presence of disturbances in the tail rotor, as well as the advantages of using a feedforward control based on a CNN. INDEX TERMSFriction estimation, Neural networks, Non-linear system, Tail rotor disturbance, Two degrees of freedom helicopter. Hence, different experimental prototypes have been developed to study helicopter dynamics [13]-[15]. One of the most popular consists of a Two Degree of Freedom Helicopter (2DOFH), which recreates the dynamic behavior of the helicopter in its pitch (θ) and yaw (ψ) rotations [15], [16].

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Model-based real-time robust controller for a small helicopter

Cited by 20 publications

References 30 publications

Feeling of Safety and Comfort towards a Socially Assistive Unmanned Aerial Vehicle That Monitors People in a Virtual Home

Feeling of Safety and Comfort towards a Socially Assistive Unmanned Aerial Vehicle That Monitors People in a Virtual Home

Human-robot skills transfer interface for UAV-based precision pesticide in dynamic environments

Integration of CNN in a Dynamic Model-Based Controller for Control of a 2DOF Helicopter With Tail Rotor Perturbations

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