<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In adaptive model-based control systems, determining the appropriate controller gain is a complex and time-consuming task due to noise and external disturbances. Changes in the controller parameters were assumed to be dependent on the quadcopter mass, which was the process variable. A nonlinear model of the plant was used to identify the mass, employing the weighted recursive least squares (WRLS) method for online identification. The identification and control processes involved filtration using differential filters, which provided appropriate derivatives of signals. Proportional integral derivative (PID) controller tuning was performed using the Gauss–Newton optimisation procedure on the plant. Differential filters played a crucial role in all the developed control systems by significantly reducing measurement noise. The results showed that the performance of classical PID controllers can be improved by using differential filters and gain scheduling. The control and identification algorithms were implemented in an National Instruments (NI) myRIO-1900 controller. The nonlinear model of the plant was built based on Newton’s equations.</p>
</abstract>

In adaptive model-based control systems, determining the appropriate controller gain is a complex and time-consuming task due to noise and external disturbances. Changes in the controller parameters were assumed to be dependent on the quadcopter mass, which was the process variable. A nonlinear model of the plant was used to identify the mass, employing the weighted recursive least squares (WRLS) method for online identification. The identification and control processes involved filtration using differential filters, which provided appropriate derivatives of signals. Proportional integral derivative (PID) controller tuning was performed using the Gauss–Newton optimisation procedure on the plant. Differential filters played a crucial role in all the developed control systems by significantly reducing measurement noise. The results showed that the performance of classical PID controllers can be improved by using differential filters and gain scheduling. The control and identification algorithms were implemented in an National Instruments (NI) myRIO-1900 controller. The nonlinear model of the plant was built based on Newton’s equations.

An Adaptive PID Control System for the Attitude and Altitude Control of a Quadcopter

Acta Mechanica et Automatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In adaptive model-based control systems, determining the appropriate controller gain is a complex and time-consuming task due to noise and external...

An Adaptive PID Control System for the Attitude and Altitude Control of a Quadcopter

Data publikacji: 30 gru 2023

Zakres stron: 29 - 39

Otrzymano: 25 maj 2023

Przyjęty: 12 wrz 2023

DOI: https://doi.org/10.2478/ama-2024-0004

Słowa kluczowe
UAV drone, mechatronics, adaptive control systems

© 2024 Leszek Cedro et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

An Adaptive PID Control System for the Attitude and Altitude Control of a Quadcopter

Data publikacji: 30 gru 2023

Zakres stron: 29 - 39

Otrzymano: 25 maj 2023

Przyjęty: 12 wrz 2023

DOI: https://doi.org/10.2478/ama-2024-0004

Słowa kluczoweUAV drone, mechatronics, adaptive control systems

© 2024 Leszek Cedro et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
UAV drone, mechatronics, adaptive control systems