Development, implementation, and experimental outdoor evaluation of quadcopter controllers for computationally limited embedded systems

Paredes, Juan Augusto; Sharma, Prashin; Ha, Brian; Lanchares, Manuel; Atkins, Ella M.; Gaskell, Peter; Kolmanovsky, Ilya

doi:10.1016/j.arcontrol.2021.06.001

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…The controller BB-Blue uses pregenerated trajectories as guidance and uses a linear quadratic regulator (LQR) controller to maneuver the vehicle along the desired trajectory. The controller used in this paper was a modified version of the LQR controller from [40]. For navigation, the MPU9250 IMU has a built-in processor that provides roll and pitch estimates, while motion capture position and yaw estimates are streamed from the ground station to the vehicle via wireless XBee radios during each flight.…”

Section: Equipment Facilities and Setupmentioning

confidence: 99%

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Improving Attitude Estimation Using Gaussian-Process-Regression-Based Magnetic Field Maps

Kuevor

Cutler

Atkins

2021

Sensors

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Magnetometers measure the local magnetic field and are present in most modern inertial measurement units (IMUs). Readings from magnetometers are used to identify Earth’s Magnetic North outdoors, but are often ignored during indoor experiments since the magnetic field does not behave how most expect. This paper presents methods to create, validate, and visualize three-dimensional magnetic field maps to expand the use of magnetic fields as a sensing modality for navigation. The utility of these maps is measured in their ability to accurately represent the magnetic field and to enable dynamic attitude estimation. In experiments with motion capture truth data, a small multicopter with three-axis inertial measurements, including magnetometer, traversed five flight profiles distinctly exciting roll, pitch, and yaw motion to provide interesting trajectories for attitude estimation. Indoor experimental results were compared to those outdoors to emphasize how spatial variation in the magnetic field drives the need for our mapping techniques. Our work presents a new way of visualizing 3D magnetic fields, which allows users to better reason about the magnetic field in their workspace. Next, we show that magnetic field maps generated from coverage patterns are generally more accurate, but training such maps using observations from desired flight paths is sufficient in the vicinity of these paths. All training sets were interpolated using Gaussian process regression (GPR), which yielded maps with <1 μT of error when interpolating between and extrapolating outside of observed locations. Finally, we validated the utility of our GPR-based maps in enabling attitude estimates in regions of high magnetic field spatial variation with experimental data.

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Section: Equipment Facilities and Setupmentioning

confidence: 99%

“…The LQR controller from [40] did not use integrator terms on position, altitude, or yaw angle. The controller was not tuned rigorously for this work, so there tended to be a slight, but slow decrease in altitude during each flight.…”

Section: Equipment Facilities and Setupmentioning

confidence: 99%

Improving Attitude Estimation Using Gaussian-Process-Regression-Based Magnetic Field Maps

Kuevor

Cutler

Atkins

2021

Sensors

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“…Paredes et al [25] presented (in the form of a tutorial) the design, simulation, implementation and testing of quadcopter controllers, which were designed based on a linearized quadcopter model. The aim was to assess the performance of these controllers when implemented on computationally limited embedded systems.…”

Section: Linear Controllers and Algorithmsmentioning

confidence: 99%

Design, Simulation, Analysis and Optimization of PID and Fuzzy Based Control Systems for a Quadcopter

2021

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Unmanned aerial vehicles or drones are becoming one of the key machines/tools of the modern world, particularly in military applications. Numerous research works are underway to explore the possibility of using these machines in other applications such as parcel delivery, construction work, hurricane hunting, 3D mapping, protecting wildlife, agricultural activities, search and rescue, etc. Since these machines are unmanned vehicles, their functionality is completely dependent upon the performance of their control system. This paper presents a comprehensive approach for dynamic modeling, control system design, simulation and optimization of a quadcopter. The main objective is to study the behavior of different controllers when the model is working under linear and/or non-linear conditions, and therefore, to define the possible limitations of the controllers. Five different control systems are proposed to improve the control performance, mainly the stability of the system. Additionally, a path simulator was also developed with the intention of describing the vehicle’s movements and hence to detect faults intuitively. The proposed PID and Fuzzy-PD control systems showed promising responses to the tests carried out. The results indicated the limits of the PID controller over non-linear conditions and the effectiveness of the controllers was enhanced by the implementation of a genetic algorithm to autotune the controllers in order to adapt to changing conditions.

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“…In general, dynamical systems must perform in uncontrolled environments (for instance, see previous works [1][2][3][4][5] ), implying the possible appearance of various destabilizing and unknown disturbances when controlling these systems. In addition, such systems can become unstable due to the effect produced by the model uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Delayed-based controller for fully actuated systems

Coronado-Salazar

Méndez-Barrios

González-Galván

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this work, a new control structure for fully actuated systems that solve the regulation and tracking of trajectories problem is proposed. This controller stands out for its ease of implementation and its ability to attenuate disturbances caused by parametric uncertainties and external forces. The control structure is based on the estimation of the delayed disturbances. Then, the current disturbance is canceled with the delayed disturbance, via the control input. The way in which the dynamics of the system is affected by the residual of these disturbances is analyzed. Also, the necessary conditions for establishing a reduced overshoot, settling time, and an attenuation factor of the disturbance are obtained. The performance of the proposed control structure is illustrated numerically by analyzing the behavior of different second-order single-input–single-output systems and also in the case of a RR-type robot. The control structure was also experimentally implemented in the case of a birotor type drone.

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Development, implementation, and experimental outdoor evaluation of quadcopter controllers for computationally limited embedded systems

Cited by 10 publications

References 8 publications

Improving Attitude Estimation Using Gaussian-Process-Regression-Based Magnetic Field Maps

Improving Attitude Estimation Using Gaussian-Process-Regression-Based Magnetic Field Maps

Design, Simulation, Analysis and Optimization of PID and Fuzzy Based Control Systems for a Quadcopter

Delayed-based controller for fully actuated systems

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