Quadrotor tuning for attitude control based on double-loop PID controller using fictitious reference iterative tuning (FRIT)

Julkananusart, Arthit; Nilkhamhang, Itthisek

doi:10.1109/iecon.2015.7392862

Cited by 16 publications

(9 citation statements)

References 6 publications

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“…aerodynamics effects. Approximating these effects numerically is possible for short maneuvers [12], [13], [14], but the more complex the maneuver or the system is, the more difficult the identification becomes, making these methods impractical for tuning controllers to fly time-optimal maneuvers. When a precise model of the platform is not available, methods like L1 adaptive control can estimate model errors online and account for them in a reactive fashion [15].…”

Section: Related Workmentioning

confidence: 99%

AutoTune: Controller Tuning for High-Speed Flight

Loquercio

Saviolo

Scaramuzza

2022

IEEE Robot. Autom. Lett.

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Due to noisy actuation and external disturbances, tuning controllers for high-speed flight is very challenging. In this paper, we ask the following questions: How sensitive are controllers to tuning when tracking high-speed maneuvers What algorithms can we use to automatically tune them To answer the first question, we study the relationship between parameters and performance and find out that the faster the maneuver, the more sensitive a controller becomes to its parameters. To answer the second question, we review existing methods for controller tuning and discover that prior works often perform poorly on the task of high-speed flight. Therefore, we propose AutoTune, a sampling-based tuning algorithm specifically tailored to high-speed flight. In contrast to previous work, our algorithm does not assume any prior knowledge of the drone or its optimization function and can deal with the multi-modal characteristics of the parameters' optimization space. We thoroughly evaluate AutoTune both in simulation and in the physical world. In our experiments, we outperform existing tuning algorithms by up to 90

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Section: Related Workmentioning

confidence: 99%

AutoTune: Controller Tuning for High-Speed Flight

Loquercio

Saviolo

Scaramuzza

2022

IEEE Robot. Autom. Lett.

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“…The approach in this paper is inspired by a classic Iterative Feedback Tuning method [23,24,25] in which optimal controller parameters are sought in reference to some cost function calculated using a current output signal of a closed-loop system. The references mention the latter method among such approaches as Fictitious Reference Iterative Tuning [26,27,28], Iterative Learning Approach [29,30] or Virtual Reference Feedback Tuning [31]. The latter methods enable direct tuning based on experimental input-output data.…”

Section: State Of the Artmentioning

confidence: 99%

Real-Time Model-Free Minimum-Seeking Autotuning Method for Unmanned Aerial Vehicle Controllers Based on Fibonacci-Search Algorithm

Giernacki

Horla

Báča

et al. 2019

Sensors

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The paper presents a novel autotuning approach for finding locally-best parameters of controllers on board of unmanned aerial vehicles (UAVs). The controller tuning is performed fully autonomously during flight on the basis of predefined ranges of controller parameters. Required controller properties may be simply interpreted by a cost function, which is involved in the optimization process. For example, the sum of absolute values of the tracking error samples or performance indices, including weighed functions of control signal samples, can be penalized to achieve very precise position control, if required. The proposed method relies on an optimization procedure using Fibonacci-search technique fitted into bootstrap sequences, enabling one to obtain a global minimizer for a unimodal cost function. The approach is characterized by low computational complexity and does not require any UAV dynamics model (just periodical measurements from basic onboard sensors) to obtain proper tuning of a controller. In addition to the theoretical background of the method, an experimental verification in real-world outdoor conditions is provided. The experiments have demonstrated a high robustness of the method to in-environment disturbances, such as wind, and its easy deployability.

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“…Recently, most of the various flight controller boards like Ardupilot, Pixhawk, and OpenPilot applied the PID control method with double loop structure for roll, pitch, and yaw channel [25]. Thus, PID becomes the conventional method in UAV's autonomous feature.…”

Section: Constructing Pid Controller For Quadrotor Attitudementioning

confidence: 99%

“…Including the quadrotor, PID also become a common method to build the flight controller. Although many methods and control algorithms have been explored for a quadrotor, like backstepping, sliding mode, feedback linearization, and adaptive control, the PID method was popularly applied in current control board developer [25]. The main reason of PID implementation is the simple structure, the simple hardware implementation, being easily tunable, and having a good range of performance [26].…”

Section: Pid Control Of Quadrotor Dynamicsmentioning

confidence: 99%

“…The main reason of PID implementation is the simple structure, the simple hardware implementation, being easily tunable, and having a good range of performance [26]. Yet the PID controller was simple to be implemented, but the manual tuning process for its gains is a complex and time-consuming effort with required experience and caution [25]. Moreover, from the practitioner point of view, the mathematical based PID tuning is considered to be difficult and impractical for the quadrotor purpose, so they tuned their PID by trial and error method instead [27][28][29][30].…”

Section: Pid Control Of Quadrotor Dynamicsmentioning

confidence: 99%

See 1 more Smart Citation

Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

Muliadi

Kusumoputro

2018

Journal of Advanced Transportation

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This article proposes a comparative method to assess the performance of artificial neural network's direct inverse control (DIC-ANN) with the PID control system. The comparison served as an analysis tool to assess the advantages of DIC-ANN over conventional control method for a UAV attitude controller. The development of ANN method for UAV control purposes arises due to the limitations of the conventional control method, which is the mathematical based model, involving complex expression, and most of them are difficult to be solved directly into analytic solution. Although the linearization simplified the solving process for such mathematical based model, omitting the nonlinear and the coupling terms is unsuitable for the dynamics of the multirotor vehicle. Thus, the DIC-ANN perform learning mechanism to overcome the limitation of PID tuning. Therefore, the proposed comparative method is developed to obtain conclusive results of DIC-ANN advantages over the linear method in UAV attitude control. Better achievement in the altitude dynamics was attained by the DIC-ANN compared to PID control method.

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Quadrotor tuning for attitude control based on double-loop PID controller using fictitious reference iterative tuning (FRIT)

Cited by 16 publications

References 6 publications

AutoTune: Controller Tuning for High-Speed Flight

AutoTune: Controller Tuning for High-Speed Flight

Real-Time Model-Free Minimum-Seeking Autotuning Method for Unmanned Aerial Vehicle Controllers Based on Fibonacci-Search Algorithm

Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

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