Dynamic modeling and stabilization of quadrotor using PID controller

Khatoon, Shahida; Shahid, Mohammad; Ibraheem,; Chaudhary, Himanshu

doi:10.1109/icacci.2014.6968383

Cited by 29 publications

(12 citation statements)

References 7 publications

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“…For trajectory tracking, the linear control can be applied only when the trajectory and the flying conditions for the quadrotor are not complex. • Nonlinear control: It is developed to overcome the [107], [108] LQR/LQG LQR describes linear system with the space state form, and its key design idea is to make quadratic objective function take minimum value by researching state feedback controller. LQG combines linear quadratic estimator with Kalman Filter for systems with Gaussian noise and incomplete state information.…”

Section: (B) Flight Control Algorithmsmentioning

confidence: 99%

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Wang

Zhao

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

168

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Unmanned aerial vehicle (UAV) networks are playing an important role in various areas due to their agility and versatility, which have attracted significant attention from both the academia and industry in recent years. As an integration of the embedded systems with communication devices, computation capabilities and control modules, the UAV network could build a closed loop from data perceiving, information exchanging, decision making to the final execution, which tightly integrates the cyber processes into the physical devices. Therefore, the UAV network could be considered as a cyber physical system (CPS). Revealing the coupling effects among the three interacted components in this CPS system, i.e., communication, computation and control, is envisioned as the key to properly utilize all the available resources and hence improve the performance of the UAV networks. In this paper, we present a comprehensive survey on the UAV networks from a CPS perspective. Firstly, we respectively research the basics and advances with respect to the three CPS components in the UAV networks. Then we look inside to investigate how these components contribute to the system performance by classifying the UAV networks into three hierarchies, i.e., the cell level, the system level, and the system of system level. Further, the coupling effects among these CPS components are explicitly illustrated, which could be enlightening to deal with the challenges in each individual aspect. New research directions and open issues are discussed at the end of this survey. With this intensive literature review, we try to provide a novel insight into the state-of-the-art in the UAV networks.

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Section: (B) Flight Control Algorithmsmentioning

confidence: 99%

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Wang

Zhao

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

168

View full text Add to dashboard Cite

show abstract

“…In conclusion, the angular motion model of quad-rotor is: (9) In the equation, U is the control allocation matrix, L is the length of the quad-rotor arm,…”

Section: Wherementioning

confidence: 99%

“…In addition, a good control system is also the basis and premise for UAV to complete flight missions. At present, the control algorithms applied to a quad-rotor mainly include PID control, backstepping, nested saturation control, fuzzy control and sliding mode control, etc [9][10][11][12][13][14] . Among these control theories, except a few classic controllers (such as PID controller) can be applied in practice, most other advanced control theories can only be used for simulation due to the complexity, and there is still a large distance for the actual engineering application [15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Parameters Identification and Adaptive LADRC Attitude Control of Quad-Rotor Model

Yang¹,

Xi²,

Hao³

et al. 2018

Preprint

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In accordance with problems such as difficulty in obtaining aerodynamic parameters of a quad-rotor model, the change of model parameters with external interference affects the control performances, an aerodynamic parameter estimation method and an adaptive attitude control method based on LADRC are designed. Firstly, the motion model, dynamics model and control distribution model of quad-rotor are established by using the aerodynamic and Newtonian Euler equations. Secondly, the identification tool CIFER is used to identify the aerodynamic parameters with large uncertainties in frequency domain and a more accurate attitude model of the quad-rotor is obtained. Then an adaptive attitude decoupling controller based on LADRC is designed to solve the problem of poor anti-interference ability of the quad-rotor, so that the control parameter b0 can be automatically adjusted to identify the change of the moment of inertia in real time. Finally, a semi-physical simulation platform is used for simulation verification. The results show that the adaptive LADRC attitude controller designed can effectively estimate and compensate the system's internal and external disturbances, and the tracking speed of the controller is faster and the precision is higher which can effectively improve system's anti-interference and robustness.

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“…In order to autonomously navigate, an imperative need of UAVs is the ability to accurately position the UAV in the environment. Therefore, one of the main tasks in this research area is the design of position controllers, from conventional PD controllers [33], [34] to more sophisticated ones [35]- [37]. In the present work, an advanced PD controller such as the proposed gain-scheduled dual-rate one will be used to wirelessly control the orientation on z axis of a UAV.…”

mentioning

confidence: 99%

Periodic Event-Triggered Sampling and Dual-Rate Control for a Wireless Networked Control System With Applications to UAVs

Cuenca

Antunes

Castillo

et al. 2019

IEEE Trans. Ind. Electron.

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In this work, Periodic Event-Triggered Sampling (PETS) and dual-rate control techniques are integrated in a Wireless Networked Control System (WNCS), where time-varying network-induced delays and packet disorder are present. Compared to the conventional Time-Triggered Sampling (TTS) paradigm, the control solution is able to considerably reduce network utilization (number of transmissions), while retaining a satisfactory control performance. Stability for the proposed WNCS is assured using Linear Matrix Inequalities (LMIs). Simulation results show the main benefits of the control approach, which are experimentally validated by means of an Unmanned Aerial Vehicle (UAV) based test-bed platform.

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Dynamic modeling and stabilization of quadrotor using PID controller

Cited by 29 publications

References 7 publications

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Aerodynamic Parameters Identification and Adaptive LADRC Attitude Control of Quad-Rotor Model

Periodic Event-Triggered Sampling and Dual-Rate Control for a Wireless Networked Control System With Applications to UAVs

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