Integration of model reference adaptive control (MRAC) with PX4 firmware for quadcopters

Niit, Edgar A.; Smit, Willie J.

doi:10.1109/m2vip.2017.8211479

Cited by 11 publications

(5 citation statements)

References 1 publication

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“…These autopilots require completely different architectures, which cannot operate along with the original off-the-shelf architecture. Adaptation methods based on these architectures, such as adaptive predictive control [30], disturbance observer control based on Euler-Lagrange dynamics [31], or deep learning based on neural networks [32], require to substantially modify or completely replace the original open-source autopilot.…”

Section: Related Workmentioning

confidence: 99%

Embedding Adaptive Features in the ArduPilot Control Architecture for Unmanned Aerial Vehicles

Liu

Xia

Baldi

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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The operation of Unmanned Aerial Vehicles (UAVs) is often subject to state-dependent alterations and unstructured uncertainty factors, such as unmodelled dynamics, environmental weather disturbances, aerodynamics gradients, or changes in inertia and mass due to payloads. While a large number of autopilot solutions have been proposed to operate UAVs, none of these solutions is able to counteract the effects of state-dependent and unstructured uncertainties online by parameter estimation and adaptive control techniques. This work presents a systematic integration of adaptive control into ArduPilot, a popular open-source autopilot suite maintained by a large community of UAV developers. Adaptation features are embedded in the ArduPilot control structure without altering the original architecture, to allow users to use the autopilot suite as usual. Tests show that the proposed adaptive ArduPilot provides consistent improved performance in several uncertain flight conditions. The source code of the proposed adaptive ArduPilot is released at https://github.com/ Friend-Peng/Adaptive-ArduPilot-Autopilot.

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Section: Related Workmentioning

confidence: 99%

Embedding Adaptive Features in the ArduPilot Control Architecture for Unmanned Aerial Vehicles

Liu

Xia

Baldi

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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“…In terms of attitude control, the roll, pitch, and yaw responses of the quadcopter system developed in this work are typically more jittery compared with the responses of quadcopters using popular firmware systems like ArduPilot [30] and Pixhawk [31,32] but the developed system can nonetheless achieve a lower average tracking error in the roll and pitch angles.…”

Section: Pid Tuningmentioning

confidence: 99%

Modeling and Implementation of Quadcopter Autonomous Flight Based on Alternative Methods to Determine Propeller Parameters

Rible¹,

Arriola²,

Ramos³

2020

Adv. sci. technol. eng. syst. j.

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To properly simulate and implement a quadcopter flight control for intended load and flight conditions, the quadcopter model must have parameters on various relationships including propeller thrust-torque, thrust-PWM, and thrust-angular speed to a certain level of accuracy. Thrust-torque modeling requires an expensive reaction torque measurement sensor. In the absence of sophisticated equipment, the study comes up with alternative methods to complete the quadcopter model. The study also presents a method of modeling the rotational aerodynamic drag on the quadcopter. Although the resulting model of the reaction torque generated by the quadcopter's propellers and the model of the drag torque acting on the quadcopter body that are derived using the methods in this study may not yield the true values of these quantities, the experimental modeling techniques presented in this work ensure that the derived dynamic model for the quadcopter will nevertheless behave identically with the true model for the quadcopter. The derived dynamic model is validated by basic flight controller simulation and actual flight implementation. The model is used as basis for a quadcopter design, which eventually is used for test purposes of basic flight control. This study serves as a baseline for fail-safe control of a quadcopter experiencing an unexpected motor failure.

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“…Local pose updates from the navigation algorithm) and low-level control (flight controller) is done over MAVROS. The PX4 controller [16] takes care of the path following part in both simulation and real-world experiments in real-time. The above codes are all written with python language, and the physical drone we use is exhibited in Figure 7.…”

Section: Drones Execution Set-upmentioning

confidence: 99%

Cell A* for Navigation of Unmanned Aerial Vehicles in Partially-known Environments

Hao¹,

Wang²,

Krolicki³

et al. 2020

Preprint

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Proper path planning is the first step of robust and efficient autonomous navigation for mobile robots. Meanwhile, it is still challenging for robots to work in a complex environment without complete prior information. This paper presents an extension to the A* search algorithm and its variants to make the path planning stable with less computational burden while handling long-distance tasks. The implemented algorithm is capable of online searching for a collision-free and smooth path when heading to the defined goal position. This paper deploys the algorithm on the autonomous drone platform and implements it on a remote control car for algorithm efficiency validation.

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Integration of model reference adaptive control (MRAC) with PX4 firmware for quadcopters

Cited by 11 publications

References 1 publication

Embedding Adaptive Features in the ArduPilot Control Architecture for Unmanned Aerial Vehicles

Embedding Adaptive Features in the ArduPilot Control Architecture for Unmanned Aerial Vehicles

Modeling and Implementation of Quadcopter Autonomous Flight Based on Alternative Methods to Determine Propeller Parameters

Cell A* for Navigation of Unmanned Aerial Vehicles in Partially-known Environments

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