More Detailed Disturbance Measurement and Active Disturbance Rejection Altitude Control for a Flapping Wing Robot Under Internal and External Disturbances

Mou, Jiawang; Zhang, Weiping; Zheng, Kexin; Wang, Yao; Wu, Chaofeng

doi:10.1007/s42235-022-00236-7

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…Autorzy pracy [21] zaimplementowali regulator ADRC z wykorzystaniem logiki rozmytej dla robota latającego typu quadrotor. W artykule [17] przedstawiono algorytm ADRC dla robota latającego z trzepoczącymi skrzydłami FWMAV (ang. Flapping Wing Micro Air Vehicle).…”

Section: Wprowadzenieunclassified

Kalman Filter as an Alternative to Extended State Observer in ADRC Control Algorithm

Michalski,

Mrotek,

Kozierski

2024

PAR

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The article presents a modified Active Disturbance Rejection Control (ADRC) algorithm that uses the Kalman Filter (KF) for the estimation of extended state vector. The Kalman filter replaced the Extended State Observer (ESO) used in its basic form. The purpose of this modification was to improve the system robustness under conditions of stochastic measurement disturbances. The method of the control system synthesis and the Kalman filter gains selection, ensuring control efficiency, as well as their impact on the system operation, were presented. The experiments were carried out on a laboratory setup – the Ball Balancing Table (BBT). Control quality was assessed based on time plots of signals and integral performance indices for various algorithm gains configurations and different noise levels. As a result of the conducted research, the advantage of using the Kalman filter over the ESO in terms of sensitivity to measurement noises was demonstrated. Implementation of the Kalman filter as the ESO determined a positive impact on control quality and the ability to reject internal disturbance also in a deterministic system.

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Section: Wprowadzenieunclassified

Kalman Filter as an Alternative to Extended State Observer in ADRC Control Algorithm

Michalski,

Mrotek,

Kozierski

2024

PAR

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“…This substantiates the superiority of the GRNN controller over the classic PID controller. Additionally, Mou et al conducted a study on the challenges associated with height control in the presence of disturbances [ 53 ]. They proposed an active perturbation suppression controller to estimate and suppress both internal and external disturbances, which effectively mitigates overshoot and enhances height control accuracy.…”

Section: Flight Controlmentioning

confidence: 99%

Review of the Flight Control Method of a Bird-like Flapping-Wing Air Vehicle

Fang,

Wen,

Gao

et al. 2023

Micromachines

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The Bird-like Flapping-wing Air Vehicle (BFAV) is a robotic innovation that emulates the flight patterns of birds. In comparison to fixed-wing and rotary-wing air vehicles, the BFAV offers superior attributes such as stealth, enhanced maneuverability, strong adaptability, and low noise, which render the BFAV a promising prospect for numerous applications. Consequently, it represents a crucial direction of research in the field of air vehicles for the foreseeable future. However, the flapping-wing vehicle is a nonlinear and unsteady system, posing significant challenges for BFAV to achieve autonomous flying since it is difficult to analyze and characterize using traditional methods and aerodynamics. Hence, flight control as a major key for flapping-wing air vehicles to achieve autonomous flight garners considerable attention from scholars. This paper presents an exposition of the flight principles of BFAV, followed by a comprehensive analysis of various significant factors that impact bird flight. Subsequently, a review of the existing literature on flight control in BFAV is conducted, and the flight control of BFAV is categorized into three distinct components: position control, trajectory tracking control, and formation control. Additionally, the latest advancements in control algorithms for each component are deliberated and analyzed. Ultimately, a projection on forthcoming directions of research is presented.

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“…Then it is mounted on a brushless motor according to the direction of rotation of the motor [13]. Next check the motor numbering with the Mission Planner Motor Test Menu [12] which is shown in Fig. 6.…”

Section: ) Brushless Motor Testmentioning

confidence: 99%

PID Control Tuning Based on Wind Speed Sensor in Flying Robot

Hasan,

Akbar

2023

Control Syst. Optim. Lett.

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The problem that is often faced by flying robots when carrying out the Vertical Take Off Landing (VTOL) process is the lack of stability of the vehicle due to differences in wind speed at any time. This is because the PID that has been pre-tuned is the PID at a certain wind speed and it is possible that during the race the wind speed suddenly changes, causing the vehicle to be less stable in carrying out the mission. Therefore, this study proposes a PID Control Tuning Control system based on the Wind Speed Sensor. In experiments that have been carried out with anemometer readings of 1-5 m/s, the ideal tuning results are obtained with each parameter P_roll = 0.1453, I_roll = 0.0892, D_roll = 0.004, P_pitch = 0.144, I_pitch = 0.09, D_pitch = 0.004, P_yaw = 0.184, I_yaw = 0.0184, D_yaw = 0.00309. In experiments with anemometer readings of 6-10 m/s, the ideal tuning results were obtained with each parameter P_roll = 0.148, I_roll = 0.0905, D_roll = 0.004, P_pitch = 0.1444, I_pitch = 0.09, D_pitch = 0.004, P_yaw = 0.1867, I_yaw = 0.0181, D_yaw = 0.0037. In an experiment with an anemometer reading of 11-15 m/s, the ideal tuning results were obtained with each parameter P_roll = 0.1494, I_roll = 0.09, D_roll = 0.004, P_pitch = 0.1457, I_pitch = 0.0902, D_pitch = 0.004, P_yaw = 0.1894, I_yaw = 0.018, D_yaw = 0.0037. PID adjustment based on this anemometer sensor utilizes the latest real-time wind speed data to support the robot in order to overcome instability in certain wind conditions by tuning PID so that the vehicle can maintain stability when carrying out certain missions.

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More Detailed Disturbance Measurement and Active Disturbance Rejection Altitude Control for a Flapping Wing Robot Under Internal and External Disturbances

Cited by 12 publications

References 26 publications

Kalman Filter as an Alternative to Extended State Observer in ADRC Control Algorithm

Kalman Filter as an Alternative to Extended State Observer in ADRC Control Algorithm

Review of the Flight Control Method of a Bird-like Flapping-Wing Air Vehicle

PID Control Tuning Based on Wind Speed Sensor in Flying Robot

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